UNIVERSIDAD POLITÉCNICA DE MADRID
DEPARTAMENTO DE AUTOMÁTICA, INGENIERÍA ELECTRÓNICA E
INFORMATICA INDUSTRIAL
ESCUELA TÉCNICA SUPERIOR DE INGENIEROS INDUSTRIALES
CENTRO DE ELECTRÓNICA INDUSTRIAL
A novel methodology for planning
reliable wireless sensor networks
TESIS DOCTORAL
Autor: Danping He
Master of Electronics Engineering from Politecnico di Torino
Directores:
Teresa Riesgo Alcaide
Doctora Ingeniera Industrial por la
Universidad Politécnica de Madrid
Jorge Portilla Berrueco Doctor por la Universidad
Politécnica de Madrid en Ingeniería Electrónica
2014
Tribunal
Tribunal nombrado por el Mgfco. y Excmo. Sr. Rector de la Universidad Politécnica de
Madrid, el día
de
de 2013.
Presidente:
Javier Uceda, Universidad Politécnica de Madrid
Vocales:
Roberto Sarmiento Rodríguez, Universidad de las Palmas de Gran Canaria
Celia López Ongil, Universidad Carlos III de Madrid
Alan Mc Gibney, Cork Institute of Technology
Secretario:
José Ramón Casar, Universidad Politécnica de Madrid
Suplentes:
Ángel De Castro Martín, Universidad Autónoma de Madrid
Marta Portela García, Universidad Carlos III de Madrid
Realizado el acto de lectura y defensa de la Tesis el día
de
de 2013 en la Escuela
Técnica Superior de Ingenieros Industriales de la Universidad Politécnica de Madrid.
Calificación:
EL PRESIDENTE
EL SECRETARIO
LOS VOCALES
To my parents
Acknowledgements
I came to this beautiful country three years ago to pursue PhD study in CEI. I’ve
been getting acquainted with those lovely professors and colleagues, who inspire me
the best on scientific researches and bring a lot of happiness to my life. I do not feel
alone on the way fighting for dreams, because deep in my heart, I know that there are
someone always besides me and supporting me. At this important moment when I
about to finish this doctoral thesis, I would like to express my sincerely appreciations
to them.
First and foremost, I would like to thank my advisor Professor Teresa Riesgo. I’ve
been profoundly benefit from her advises and inspirations over the past three years,
which are always important guiding lights leading me to the bright way towards
scientific research. I deeply thank her for the unprecedented freedom she offered to
explore my intellectual curiosity in my work, and for fostering my capacity critically
as an independent researcher.
I would also sincerely thank my co-advisor Dr. Jorge Portilla. I absorbed
important knowledge from him through every discussion, not only on professional
area but also on other aspects that will absolutely benefit my future careers. I learnt
a lot from him and progressed fast. I will always appreciate him for all the patience
to correct my work, and for the consistent support to eliminate my nervousness
before presentations.
I take this opportunity to record my gratitude to Professor David Symplot-Ryl
and Dr. Nathalie Mitton, who offered me the exchange research stay at Inria
Lille - Nord Europe.
Their valuable advices and ideas not only make me
successful in academic publications, but also illuminate another research direction
and methodology to me.
I would like to thank the members of my thesis committee, for generously offering
their time, support, guidance and good will throughout the preparation and review
of this document.
Gabriel Mujica, my project partner in CEI. We work together for the great
aspiration of achieving success in DPCM project.
His knowledge on hardware
programming perfectly compensates my shortage and it’s my fortune to have his
collaboration. I would like to thank him for all the intelligent and patient works
on establishing sensor testbed to allow us making real measurements efficiently and
precisely.
I would also express my acknowledgement to Edu, who gives me so many
important advises and encouragements. From time to time, he concerns about my
work and my progress on preparing documents for the thesis committee. Without
his great efforts, the whole procedure would not be run smoothly.(!)
All the lovely professors in CEI, José Antonio, Yago, Rafa, Jesús, Pedro and
Roberto. I sincerely thank you all for the supports and encouragements on my
study. I gain quite significantly from your teaching and supports.
I enjoyed a wonderful time in CEI with all the colleagues (CEIers). They are
earnest, friendly and hard-working, and I would like to appreciate them for all the
help and happiness they gave me. My special gratitude should give to Marcelo who
taught me on how to efficiently use Latex, Monica who translated for me the abstract
of this thesis, and Nico who gave me so many helpful suggestions on multi-objective
optimization and writing papers, thank you so much!
I would like to express my deep appreciation to James Zhao for everything he
has done for me. His working attitude inspire me so much along these years. His
valuable advises, encouragements and generous supports bring warmth to me and
shine my life, like the sunlight in severe winter.
My acknowledgement will never be complete without the special mention of
my Chinese friends. I would like to thank Tianjun Zhou, Cheng Xie, Yang Wang,
Pengming Cheng, Wei He, Sisi Zhao, Zhi Wang and Ke Guan for being with me and
trusting me. Their moral supports and motivations drive me to do the best and I
find myself lucky to have friends like them.
Finally, I would like to give my greatest appreciation to my parents Dedong He
and Sujuan Qiu, for showing faith in me and giving me liberty to chase what I
desired, for standing behind me with all their love.
Sincerely thank you all. Danping
ii
Abstract
Wireless sensor networks (WSNs) have shown their potentials in various
applications, which bring a lot of benefits to users from both research and industrial
areas. For many setups, it is envisioned that WSNs will consist of tens to hundreds of
nodes that operate on small batteries. However due to the diversity of the deployed
environments and resource constraints on radio communication, sensing ability and
energy supply, it is a very challenging issue to plan optimized WSN topology and
predict its performance before real deployment.
During the network planning phase, the connectivity, coverage, cost, network
longevity and service quality should all be considered. Therefore it requires designers
coping with comprehensive and interdisciplinary knowledge, including networking,
radio engineering, embedded system and so on, in order to efficiently construct
a reliable WSN for any specific types of environment. Nowadays there is still a
lack of the analysis and experiences to guide WSN designers to efficiently construct
WSN topology successfully without many trials. Therefore, simulation is a feasible
approach to the quantitative analysis of the performance of wireless sensor networks.
However the existing planning algorithms and tools, to some extent, have serious
limitations to practically design reliable WSN topology:
• Only a few of them tackle the 3D deployment issue, and an overwhelming
number of works are proposed to place devices in 2D scheme.
Without
considering the full dimension, the impacts of environment to the performance
of WSN are not completely studied, thus the values of evaluated metrics such
as connectivity and sensing coverage are not sufficiently accurate to make
proper decision.
• Even fewer planning methods model the sensing coverage and radio
propagation by considering the realistic scenario where obstacles exist. Radio
signals propagate with multi-path phenomenon in the real world, in which
direct paths, reflected paths and diffracted paths contribute to the received
signal strength. Besides, obstacles between the path of sensor and objects
might block the sensing signals, thus create coverage hole in the application.
• None of the existing planning algorithms model the network longevity and
packet delivery capability properly and practically.
They often employ
unilateral and unrealistic formulations.
• The optimization targets are often one-sided in the current works. Without
comprehensive evaluation on the important metrics, the performance of
planned WSNs can not be reliable and entirely optimized.
• Modeling of environment is usually time consuming and the cost is very
high, while none of the current works figure out any method to model
the 3D deployment environment efficiently and accurately. Therefore many
researchers are trapped by this issue, and their algorithms can only be
evaluated in the same scenario, without the possibility to test the robustness
and feasibility for implementations in different environments.
In this thesis, we propose a novel planning methodology and an
intelligent WSN planning tool to assist WSN designers efficiently
planning reliable WSNs.
First of all, a new method is proposed to efficiently and automatically
model the 3D indoor and outdoor environments.
To the best of our
knowledge, this is the first time that the advantages of image understanding
algorithm are applied to automatically reconstruct 3D outdoor and indoor scenarios
for signal propagation and network planning purpose. The experimental results
indicate that the proposed methodology is able to accurately recognize different
objects from the satellite images of the outdoor target regions and from the
scanned floor plan of indoor area.
Its mechanism offers users a flexibility to
reconstruct different types of environment without any human interaction. Thereby
it significantly reduces human efforts, cost and time spent on reconstructing a 3D
geographic database and allows WSN designers concentrating on the planning issues.
Secondly, an efficient ray-tracing engine is developed to accurately
and practically model the radio propagation and sensing signal on the
constructed 3D map. The engine contributes on efficiency and accuracy to the
estimated results. By using image processing concepts, including the kd-tree space
division algorithm and modified polar sweep algorithm, the rays are traced efficiently
without detecting all the primitives in the scene. The radio propagation model
iv
is proposed, which emphasizes not only the materials of obstacles but also their
locations along the signal path. The sensing signal of sensor nodes, which is sensitive
to the obstacles, is benefit from the ray-tracing algorithm via obstacle detection.
The performance of this modelling method is robust and accurate compared with
conventional methods, and experimental results imply that this methodology is
suitable for both outdoor urban scenes and indoor environments. Moreover, it can
be applied to either GSM communication or ZigBee protocol by varying frequency
parameter of the radio propagation model.
Thirdly, WSN planning method is proposed to tackle the above
mentioned challenges and efficiently deploy reliable WSNs. More metrics
(connectivity, coverage, cost, lifetime, packet latency and packet drop rate) are
modeled more practically compared with other works. Especially 3D ray tracing
method is used to model the radio link and sensing signal which are sensitive to the
obstruction of obstacles; network routing is constructed by using AODV protocol;
the network longevity, packet delay and packet drop rate are obtained via simulating
practical events in WSNet simulator, which to the best of our knowledge, is the
first time that network simulator is involved in a planning algorithm. Moreover, a
multi-objective optimization algorithm is developed to cater for the characteristics
of WSNs. The capability of providing multiple optimized solutions simultaneously
allows users making their own decisions accordingly, and the results are more
comprehensively optimized compared with other state-of-the-art algorithms.
iMOST is developed by integrating the introduced algorithms, to
assist WSN designers efficiently planning reliable WSNs for different
configurations.
The abbreviated name iMOST stands for an Intelligent
Multi-objective Optimization Sensor network planning Tool. iMOST contributes
on: (1) Convenient operation with a user-friendly vision system; (2) Efficient and
automatic 3D database reconstruction and fast 3D objects design for both indoor
and outdoor environments; (3) It provides multiple multi-objective optimized 3D
deployment solutions and allows users to configure the network properties, hence it
can adapt to various WSN applications; (4) Deployment solutions in the 3D space
and the corresponding evaluated performance are visually presented to users; and (5)
The Node Placement Module of iMOST is available online as well as the source code
of the other two rebuilt heuristics. Therefore WSN designers will be benefit from
v
this tool on efficiently constructing environment database, practically and efficiently
planning reliable WSNs for both outdoor and indoor applications. With the open
source codes, they are also able to compare their developed algorithms with ours to
contribute to this academic field.
Finally, solid real results are obtained for both indoor and outdoor
WSN planning. Deployments have been realized for both indoor and outdoor
environments based on the provided planning solutions.
coincide well with the estimated results.
The measured results
The proposed planning algorithm is
adaptable according to the WSN designer’s desirability and configuration, and it
offers flexibility to plan small and large scale, indoor and outdoor 3D deployments.
The thesis is organized in 7 chapters. In Chapter 1, WSN applications and
motivations of this work are introduced, the state-of-the-art planning algorithms and
tools are reviewed, challenges are stated out and the proposed methodology is briefly
introduced. In Chapter 2, the proposed 3D environment reconstruction methodology
is introduced and its performance is evaluated for both outdoor and indoor
environment. The developed ray-tracing engine and proposed radio propagation
modelling method are described in details in Chapter 3, their performances are
evaluated in terms of computation efficiency and accuracy. Chapter 4 presents
the modelling of important metrics of WSNs and the proposed multi-objective
optimization planning algorithm, the performance is compared with the other
state-of-the-art planning algorithms. The intelligent WSN planning tool iMOST is
described in Chapter 5. Real WSN deployments are prosecuted based on the planned
solutions for both indoor and outdoor scenarios, important data are measured and
results are analysed in Chapter 6. Chapter 7 concludes the thesis and discusses
about future works.
vi
Resumen en Castellano
Las redes de sensores inalámbricas (en inglés Wireless Sensor Networks, WSNs) han
demostrado su potencial en diversas aplicaciones que aportan una gran cantidad
de beneficios para el campo de la investigación y de la industria. Para muchas
configuraciones se prevé que las WSNs consistirán en decenas o cientos de nodos
que funcionarán con baterı́as pequeñas. Sin embargo, debido a la diversidad de
los ambientes para desplegar las redes y a las limitaciones de recursos en materia
de comunicación de radio, capacidad de detección y suministro de energı́a, la
planificación de la topologı́a de la red y la predicción de su rendimiento es un tema
muy difı́cil de tratar antes de la implementación real.
Durante la fase de planificación del despliegue de la red se deben considerar
aspectos como la conectividad, la cobertura, el coste, la longevidad de la red y
la calidad del servicio. Por lo tanto, requiere de diseñadores con un amplio e
interdisciplinario nivel de conocimiento que incluye la creación de redes, la ingenierı́a
de radio y los sistemas embebidos entre otros, con el fin de construir de manera
eficiente una WSN confiable para cualquier tipo de entorno. Hoy en dı́a todavı́a
hay una falta de análisis y experiencias que orienten a los diseñadores de WSN
para construir las topologı́as WSN de manera eficiente sin realizar muchas pruebas.
Por lo tanto, la simulación es un enfoque viable para el análisis cuantitativo del
rendimiento de las redes de sensores inalámbricos.
Sin embargo, los algoritmos y herramientas de planificación existentes tienen, en
cierta medida, serias limitaciones para diseñar en la práctica una topologı́a fiable de
WSN:
• Sólo unos pocos abordan la cuestión del despliegue 3D mientras que existe una
gran cantidad de trabajos que colocan los dispositivos en 2D. Si no se analiza la
dimensión completa (3D), los efectos del entorno en el desempeño de WSN no
se estudian por completo, por lo que los valores de los parámetros evaluados,
como la conectividad y la cobertura de detección, no son lo suficientemente
precisos para tomar la decisión correcta.
• Aún en menor medida los métodos de planificación modelan la cobertura de
los sensores y la propagación de la señal de radio teniendo en cuenta un
escenario realista donde existan obstáculos. Las señales de radio en el mundo
real siguen una propagación multicamino, en la que los caminos directos, los
caminos reflejados y los caminos difractados contribuyen a la intensidad de
la señal recibida. Además, los obstáculos entre el recorrido del sensor y los
objetos pueden bloquear las señales de detección y por lo tanto crear áreas sin
cobertura en la aplicación.
• Ninguno de los algoritmos de planificación existentes modelan el tiempo
de vida de la red y la capacidad de entrega de paquetes correctamente
y prácticamente. A menudo se emplean formulaciones unilaterales y poco
realistas.
• Los objetivos de optimización son a menudo tratados unilateralmente en
los trabajos actuales.
Sin una evaluación exhaustiva de los parámetros
importantes, el rendimiento previsto de las redes inalámbricas de sensores no
puede ser fiable y totalmente optimizado.
• Por lo general, el modelado del entorno conlleva mucho tiempo y tiene un coste
muy alto, pero ninguno de los trabajos actuales propone algún método para
modelar el entorno de despliegue 3D con eficiencia y precisión. Por lo tanto,
muchos investigadores están limitados por este problema y sus algoritmos sólo
se pueden evaluar en el mismo escenario, sin la posibilidad de probar la solidez
y viabilidad para las implementaciones en diferentes entornos.
En esta tesis, se propone una nueva metodologı́a de planificación ası́
como una herramienta inteligente de planificación de redes de sensores
inalámbricas para ayudar a los diseñadores a planificar WSNs fiables de
una manera eficiente.
En primer lugar, se propone un nuevo método para modelar de manera
eficiente y automática los ambientes interiores y exteriores en 3D. Según
nuestros conocimientos hasta la fecha, esta es la primera vez que las ventajas del
algoritmo de image understandingse aplican para reconstruir automáticamente
los escenarios exteriores e interiores en 3D para analizar la propagación de la señal y
viii
la planificación de la red. Los resultados experimentales indican que la metodologı́a
propuesta es capaz de reconocer con precisión los diferentes objetos presentes en las
imágenes satelitales de las regiones objetivo en el exterior y de la planta escaneada
en el interior. Su mecanismo ofrece a los usuarios la flexibilidad para reconstruir
los diferentes tipos de entornos sin ninguna interacción humana. De este modo se
reduce considerablemente el esfuerzo humano, el coste y el tiempo invertido en la
reconstrucción de una base de datos geográfica con información 3D, permitiendo ası́
que los diseñadores se concentren en los temas de planificación.
En segundo lugar, se ha desarrollado un motor de trazado de rayos (en
inglés ray tracing ) eficiente para modelar con precisión la propagación de
la señal de radio y la señal de los sensores en el mapa 3D construido. El
motor contribuye a la eficiencia y la precisión de los resultados estimados. Mediante
el uso de los conceptos de procesamiento de imágenes, incluyendo el algoritmo del
árbol kd para la división del espacio y el algoritmo polar sweepmodificado, los
rayos se trazan de manera eficiente sin la detección de todas las primitivas en la
escena. El modelo de propagación de radio que se propone no sólo considera los
materiales de los obstáculos, sino también su ubicación a lo largo de la ruta de
señal. La señal de los sensores de los nodos, que es sensible a los obstáculos, se ve
beneficiada por la detección de objetos llevada a cabo por el algoritmo de trazado
de rayos. El rendimiento de este método de modelado es robusto y preciso en
comparación con los métodos convencionales, y los resultados experimentales indican
que esta metodologı́a es adecuada tanto para escenas urbanas al aire libre como para
ambientes interiores. Por otra parte, se puede aplicar a cualquier comunicación GSM
o protocolo ZigBee mediante la variación de la frecuencia del modelo de propagación
de radio.
En tercer lugar, se propone un método de planificación de WSNs
para hacer frente a los desafı́os mencionados anteriormente y desplegar
redes de sensores fiables de manera eficiente. Se modelan más parámetros
(conectividad, cobertura, coste, tiempo de vida, la latencia de paquetes y tasa de
caı́da de paquetes) en comparación con otros trabajos. Especialmente el método
de trazado de rayos 3D se utiliza para modelar el enlace de radio y señal de los
sensores que son sensibles a la obstrucción de obstáculos; el enrutamiento de la red se
construye utilizando el protocolo AODV; la longevidad de la red, retardo de paquetes
ix
y tasa de abandono de paquetes se obtienen a través de la simulación de eventos
prácticos en el simulador WSNet, y según nuestros conocimientos hasta la fecha, es
la primera vez que simulador de red está implicado en un algoritmo de planificación.
Por otra parte, se ha desarrollado un algoritmo de optimización multi-objetivo para
satisfacer las caracterı́sticas de las redes inalámbricas de sensores. La capacidad
de proporcionar múltiples soluciones optimizadas de forma simultánea permite a los
usuarios tomar sus propias decisiones en consecuencia, obteniendo mejores resultados
en comparación con otros algoritmos del estado del arte.
iMOST se desarrolla mediante la integración de los algoritmos
presentados, para ayudar de forma eficiente a los diseñadores en
la planificación de WSNs fiables para diferentes configuraciones.
El
nombre abreviado iMOST (Intelligent Multi-objective Optimization Sensor network
planning Tool) representa una herramienta inteligente de planificación de redes de
sensores con optimización multi-objetivo. iMOST contribuye en: (1) Operación
conveniente con una interfaz de fácil uso, (2) Reconstrucción eficiente y automática
de una base de datos con información 3D y diseño rápido de objetos 3D para
ambientes interiores y exteriores, (3) Proporciona varias soluciones de despliegue
optimizadas para los multi-objetivo en 3D y permite a los usuarios configurar
las propiedades de red, por lo que puede adaptarse a diversas aplicaciones de
WSN, (4) las soluciones de implementación en el espacio 3D y el correspondiente
rendimiento evaluado se presentan visualmente a los usuarios, y (5) El Node
Placement Modulede iMOST está disponible en lı́nea, ası́ como el código fuente
de las otras dos heurı́sticas de planificación. Por lo tanto los diseñadores WSN se
beneficiarán de esta herramienta para la construcción eficiente de la base de datos
con información del entorno, la planificación práctica y eficiente de WSNs fiables
tanto para aplicaciones interiores y exteriores. Con los códigos fuente abiertos, son
capaces de comparar sus algoritmos desarrollados con los nuestros para contribuir a
este campo académico.
Por último, se obtienen resultados reales sólidos tanto para la
planificación de WSN en interiores y exteriores. Los despliegues se han
realizado tanto para ambientes de interior y como para ambientes de exterior
utilizando las soluciones de planificación propuestas.
Los resultados medidos
coinciden en gran medida con los resultados estimados. El algoritmo de planificación
x
propuesto se adapta convenientemente al deiseño de redes de sensores inalámbricas,
y ofrece flexibilidad para planificar los despliegues 3D a pequeña y gran escala tanto
en interiores como en exteriores.
La tesis se estructura en 7 capı́tulos.
En el Capı́tulo 1, se presentan las
aplicaciones de WSN y motivaciones de este trabajo, se revisan los algoritmos
y herramientas de planificación del estado del arte, se presentan los retos y se
describe brevemente la metodologı́a propuesta.
En el Capı́tulo 2, se presenta
la metodologı́a de reconstrucción de entornos 3D propuesta y su rendimiento es
evaluado tanto para espacios exteriores como para espacios interiores. El motor de
trazado de rayos desarrollado y el método de modelado de propagación de radio
propuesto se describen en detalle en el Capı́tulo 3, evaluándose en términos de
eficiencia computacional y precisión. En el Capı́tulo 4 se presenta el modelado
de los parámetros importantes de las WSNs y el algoritmo de planificación de
optimización multi-objetivo propuesto, el rendimiento se compara con los otros
algoritmos de planificación descritos en el estado del arte. La herramienta inteligente
de planificación de redes de sensores inalámbricas, iMOST, se describe en el Capı́tulo
5. En el Capı́tulo 6 se llevan a cabo despliegues reales de acuerdo a las soluciones
previstas para los escenarios interiores y exteriores, se miden los datos importantes
y se analizan los resultados. En el Capı́tulo 7 se concluye la tesis y se discute acerca
de los trabajos futuros.
xi
Contents
1 Introduction
1.1
1
WSN applications and deployment experiences . . . . . . . . . . . .
2
1.1.1
Military applications . . . . . . . . . . . . . . . . . . . . . . .
3
1.1.2
Environmental applications . . . . . . . . . . . . . . . . . . .
4
1.1.3
Health applications . . . . . . . . . . . . . . . . . . . . . . . .
7
1.1.4
Other applications . . . . . . . . . . . . . . . . . . . . . . . .
9
Challenges when deploying WSNs . . . . . . . . . . . . . . . . . . . .
10
1.2.1
Necessity of simulation . . . . . . . . . . . . . . . . . . . . . .
12
1.3
Introduction to the planning algorithms and tools . . . . . . . . . . .
13
1.4
Proposed methodology and work flow: Main contributions
. . . . .
22
1.5
Conclusion
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
25
1.2
2 3D environment reconstruction method
2.1
2.2
2.3
27
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
27
2.1.1
3D environment reconstruction from Lidar systems . . . . . .
28
2.1.2
3D environment reconstruction from images . . . . . . . . . .
29
3D outdoor environment reconstruction . . . . . . . . . . . . . . . .
32
2.2.1
Proposed algorithm for outdoor environment reconstruction .
35
2.2.2
Image database . . . . . . . . . . . . . . . . . . . . . . . . . .
36
2.2.3
Image understanding and segmentation algorithm
. . . . . .
37
2.2.4
Performance enhancement . . . . . . . . . . . . . . . . . . . .
44
2.2.5
Shape matching and vectorization . . . . . . . . . . . . . . .
55
Indoor environment reconstruction . . . . . . . . . . . . . . . . . . .
64
2.3.1
Image calibration and classification . . . . . . . . . . . . . . .
65
2.3.2
Thinning and feature points extraction . . . . . . . . . . . . .
66
2.3.3
Smoothing and vectorizing . . . . . . . . . . . . . . . . . . .
66
Contents
2.3.4
2.4
Demonstration and analysis . . . . . . . . . . . . . . . . . . .
Conclusion
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3 Ray-tracing engine and radio propagation modelling
68
69
73
3.1
Space division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
75
3.2
Polar sweeping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
77
3.2.1
Direct path . . . . . . . . . . . . . . . . . . . . . . . . . . . .
79
3.2.2
Reflection path . . . . . . . . . . . . . . . . . . . . . . . . . .
81
3.2.3
Diffraction path
. . . . . . . . . . . . . . . . . . . . . . . . .
82
Measurements and experimental results . . . . . . . . . . . . . . . .
83
3.3.1
Outdoor RF propagation verification . . . . . . . . . . . . . .
83
3.3.2
Indoor RF propagation verification . . . . . . . . . . . . . . .
88
3.3
3.4
Conclusion
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4 Planning the WSN
4.1
99
101
Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.1.1
Star network . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.1.2
Tree network . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.1.3
Mesh network . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.1.4
Cluster network . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.2
Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.3
Introduction and modelling of important metrics . . . . . . . . . . . 106
4.4
4.5
4.3.1
Preliminaries and assumptions . . . . . . . . . . . . . . . . . 106
4.3.2
The cost of WSN . . . . . . . . . . . . . . . . . . . . . . . . . 109
4.3.3
Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.3.4
Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
4.3.5
Lifetime, Packet latency and Packet drop rate . . . . . . . . . 118
The proposed multi-objective optimization methodology
. . . . . . 121
4.4.1
Initialization of individuals . . . . . . . . . . . . . . . . . . . 124
4.4.2
Crossover and mutation . . . . . . . . . . . . . . . . . . . . . 125
4.4.3
Evaluation based on desirability models and constraints . . . 126
Experimental results and analysis
. . . . . . . . . . . . . . . . . . . 127
4.5.1
The impact of maximum number of generation . . . . . . . . 127
4.5.2
Performance comparison with other heuristics . . . . . . . . . 130
xiii
Contents
4.6
Conclusion
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
5 iMOST: an Intelligent Multi-objective Optimization Sensor
network planning Tool
5.1
5.2
5.3
135
Menu bar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
5.1.1
Image Processing Module . . . . . . . . . . . . . . . . . . . . 136
5.1.2
Environment property configuration . . . . . . . . . . . . . . 136
Toolbar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
5.2.1
Node deployment . . . . . . . . . . . . . . . . . . . . . . . . . 138
5.2.2
Network Planning Module . . . . . . . . . . . . . . . . . . . . 138
5.2.3
Ray-tracing Propagation Module . . . . . . . . . . . . . . . . 140
5.2.4
3D navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
Conclusion
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
6 Real measurements and results analysis
143
6.1
Aggregation mechanism of measured data . . . . . . . . . . . . . . . 143
6.2
Application interface . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
6.3
Indoor measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
6.4
Outdoor measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 155
6.5
Conclusion
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
7 Conclusions and future works
163
7.1
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
7.2
Future works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
7.3
Publications based on this work . . . . . . . . . . . . . . . . . . . . . 166
7.4
Implementation of this work . . . . . . . . . . . . . . . . . . . . . . . 168
Bibliography
169
xiv
List of Figures
Fig. 1.1
A typical WSN architecture . . . . . . . . . . . . . . . . . . . .
2
Fig. 1.2
Environmental applications . . . . . . . . . . . . . . . . . . . .
5
Fig. 1.3
Health care applications . . . . . . . . . . . . . . . . . . . . . .
8
Fig. 1.4
Smartsantander . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
Fig. 1.5
Libelium smart world . . . . . . . . . . . . . . . . . . . . . . . .
11
Fig. 1.6
An example of tool interface . . . . . . . . . . . . . . . . . . . .
17
Fig. 1.7
Demonstration of the single-hop solution . . . . . . . . . . . . .
18
Fig. 1.8
Demonstration of the multi-hop solution . . . . . . . . . . . . .
18
Fig. 1.9
The proposed methodology . . . . . . . . . . . . . . . . . . . . .
24
Fig. 2.1
Airborne laser scanning . . . . . . . . . . . . . . . . . . . . . . .
28
Fig. 2.2
DSM with the original image
. . . . . . . . . . . . . . . . . . .
28
Fig. 2.3
The principle of visual hull reconstruction . . . . . . . . . . . .
30
Fig. 2.4
Example of space carving reconstruction . . . . . . . . . . . . .
31
Fig. 2.5
Example of image-based rendering . . . . . . . . . . . . . . . .
31
Fig. 2.6
Workflow of the work by Saxena et al. . . . . . . . . . . . . . .
32
Fig. 2.7
Google street view tools . . . . . . . . . . . . . . . . . . . . . .
34
Fig. 2.8
3D outdoor reconstruction . . . . . . . . . . . . . . . . . . . . .
36
Fig. 2.9
MSRC database . . . . . . . . . . . . . . . . . . . . . . . . . . .
38
Fig. 2.10 CEIeurope database . . . . . . . . . . . . . . . . . . . . . . . .
39
Fig. 2.11 17D filter bank . . . . . . . . . . . . . . . . . . . . . . . . . . .
40
Fig. 2.12 The process of image textonization . . . . . . . . . . . . . . . .
40
Fig. 2.13 Calculating feature response . . . . . . . . . . . . . . . . . . . .
42
Fig. 2.14 Multi-object recognition procedure. . . . . . . . . . . . . . . . .
44
Fig. 2.15 Comparison between k-mean and graphcut . . . . . . . . . . . .
45
Fig. 2.16 Example of sub-clustering based on connectivity property. . . .
47
List of Figures
Fig. 2.17 The comparison between the proposed algorithm and that of
Shotton et al. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49
Fig. 2.18 The principle of shadow detection . . . . . . . . . . . . . . . . .
50
Fig. 2.19 The result of shadow detection
. . . . . . . . . . . . . . . . . .
51
Fig. 2.20 Road segmentation and orientation estimation . . . . . . . . . .
52
Fig. 2.21 Clustering of orientations . . . . . . . . . . . . . . . . . . . . . .
53
Fig. 2.22 Comparison of orientations . . . . . . . . . . . . . . . . . . . . .
54
Fig. 2.23 The result of road detection . . . . . . . . . . . . . . . . . . . .
55
Fig. 2.24 Frequently seen façade shapes of buildings . . . . . . . . . . . .
57
Fig. 2.25 Hierarchical shape matching . . . . . . . . . . . . . . . . . . . .
59
Fig. 2.26 Successful result of shape matching . . . . . . . . . . . . . . . .
61
Fig. 2.27 Shape registration . . . . . . . . . . . . . . . . . . . . . . . . . .
62
Fig. 2.28 KML shape description shown on Google Earth . . . . . . . . .
64
Fig. 2.29 Image calibration and segmentation . . . . . . . . . . . . . . . .
65
Fig. 2.30 Thinning step and feature point extraction . . . . . . . . . . . .
66
Fig. 2.31 Smoothing and regularization. . . . . . . . . . . . . . . . . . . .
67
Fig. 2.32 Vectorization result.
. . . . . . . . . . . . . . . . . . . . . . . .
68
Fig. 2.33 A wall is described by four vertexes. . . . . . . . . . . . . . . .
68
Fig. 2.34 Reconstructed 3D indoor map in different views.
. . . . . . . .
69
Fig. 2.35 A toy example by using the downloaded map downloaded . . .
70
Fig. 3.1
Space division by kd-tree . . . . . . . . . . . . . . . . . . . . . .
76
Fig. 3.2
Conventional Polar sweep. . . . . . . . . . . . . . . . . . . . . .
77
Fig. 3.3
Polar sweep. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
78
Fig. 3.4
Direct path. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
80
Fig. 3.5
Reflection path searching. . . . . . . . . . . . . . . . . . . . . .
81
Fig. 3.6
Diffraction path searching. . . . . . . . . . . . . . . . . . . . . .
82
Fig. 3.7
Three different routes measured by COST231 group. . . . . . .
84
Fig. 3.8
Classification and radio propagation over Munich scenario . . .
85
Fig. 3.9
Simulation result for the first route METRO200. . . . . . . .
86
Fig. 3.10 Simulation result for the second route METRO201. . . . . . .
86
Fig. 3.11 Simulation result for the third route METRO202. . . . . . . .
87
Fig. 3.12 Four-layer architecture and physical view of the Cookie node . .
88
xvi
List of Figures
Fig. 3.13 ETRX2 ZigBee communication module on communication layer
89
Fig. 3.14 Radio pattern of antenna of ETRX2 module . . . . . . . . . . .
89
Fig. 3.15 Three scenarios for radio measurements . . . . . . . . . . . . . .
90
Fig. 3.16 Ray tracing demonstrations from different TXs and RXs . . . .
91
Fig. 3.17 Simulation result: example 1 . . . . . . . . . . . . . . . . . . . .
92
Fig. 3.18 Simulation result: example 2 . . . . . . . . . . . . . . . . . . . .
92
Fig. 3.19 Results and comparisons of Scenario A . . . . . . . . . . . . . .
93
Fig. 3.20 Results and comparisons of Scenario B . . . . . . . . . . . . . .
94
Fig. 3.21 Results and comparisons of Scenario C . . . . . . . . . . . . . .
95
Fig. 3.22 Demonstration of the toy example . . . . . . . . . . . . . . . . .
96
Fig. 3.23 Average time consumption of polar sweeping . . . . . . . . . . .
97
Fig. 3.24 Average time consumption of ray tracing . . . . . . . . . . . . .
98
Fig. 3.25 Polar sweeping with and without kd-tree traversing . . . . . . .
99
Fig. 4.1
Different topologies of WSN . . . . . . . . . . . . . . . . . . . . 103
Fig. 4.2
The searching of covered point . . . . . . . . . . . . . . . . . . . 107
Fig. 4.3
Products by Crossbow . . . . . . . . . . . . . . . . . . . . . . . 110
Fig. 4.4
BTnode rev3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
Fig. 4.5
Waspmote,Wismote and SEED-EYE . . . . . . . . . . . . . . . 112
Fig. 4.6
Tyndall mote . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Fig. 4.7
Deployment cost configuration in vertical view . . . . . . . . . . 116
Fig. 4.8
Modelling strategy by using WSNet simulator . . . . . . . . . . 121
Fig. 4.9
Proposed planning algorithm strategy . . . . . . . . . . . . . . . 122
Fig. 4.10 Crossover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Fig. 4.11 Mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Fig. 4.12 Configuration of scenario CEI-UPM . . . . . . . . . . . . . . . . 128
Fig. 4.13 Desirability values . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Fig. 4.14 Time consumption . . . . . . . . . . . . . . . . . . . . . . . . . 129
Fig. 4.15 Scenario East Lansing . . . . . . . . . . . . . . . . . . . . . . . 131
Fig. 4.16 Scenario Madrid . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
Fig. 5.1
The mainframe of the planning tool . . . . . . . . . . . . . . . . 137
Fig. 5.2
User command on constructing new map . . . . . . . . . . . . . 137
Fig. 5.3
Environment property setting dialog . . . . . . . . . . . . . . . 138
xvii
List of Figures
Fig. 5.4
Node property configuration dialog . . . . . . . . . . . . . . . . 139
Fig. 5.5
Node configuration dialog . . . . . . . . . . . . . . . . . . . . . 140
Fig. 5.6
Generated topology demonstration . . . . . . . . . . . . . . . . 140
Fig. 5.7
3D navigation for outdoor scenario . . . . . . . . . . . . . . . . 141
Fig. 5.8
3D navigation for indoor scenario . . . . . . . . . . . . . . . . . 142
Fig. 6.1
BKR2400 antenna. . . . . . . . . . . . . . . . . . . . . . . . . . 144
Fig. 6.2
Application interface. . . . . . . . . . . . . . . . . . . . . . . . . 146
Fig. 6.3
Indoor modelling by using iMOST
Fig. 6.4
User requirement over the indoor test. . . . . . . . . . . . . . . 149
Fig. 6.5
Topology comparison 1 . . . . . . . . . . . . . . . . . . . . . . . 149
Fig. 6.6
Topology comparison 2 . . . . . . . . . . . . . . . . . . . . . . . 150
Fig. 6.7
RSS comparison between real measurement and simulation result 153
Fig. 6.8
Remaining energy of N1 , N8 and N14 . . . . . . . . . . . . . . . 153
Fig. 6.9
Comparing the packet delivery status: Indoor . . . . . . . . . . 154
. . . . . . . . . . . . . . . . 148
Fig. 6.10 The sensed data of N4 . . . . . . . . . . . . . . . . . . . . . . . . 155
Fig. 6.11 Outdoor modelling by using iMOST . . . . . . . . . . . . . . . 156
Fig. 6.12 Topology comparison: Outdoor . . . . . . . . . . . . . . . . . . 159
Fig. 6.13 RSS comparison between real measurement and simulation result 160
Fig. 6.14 Remaining energy of N4 , N5 and N2 . . . . . . . . . . . . . . . 160
Fig. 6.15 Comparing the packet delivery status: Outdoor . . . . . . . . . 161
xviii
List of Tables
Table 1.1
Comparison of planning algorithms . . . . . . . . . . . . . . .
20
Table 2.1
Confusion matrix of classification result . . . . . . . . . . . . .
47
Table 2.2
Shape matching result
. . . . . . . . . . . . . . . . . . . . . .
60
Table 3.1
Comparison of radio estimation result with other methods. . .
87
Table 3.2
Results comparison: Scenario A . . . . . . . . . . . . . . . . .
93
Table 3.3
Results comparison: Scenario B . . . . . . . . . . . . . . . . .
95
Table 3.4
Results comparison: Scenario C . . . . . . . . . . . . . . . . .
96
Table 3.5
Attenuation parameters of major objects indoors. . . . . . . .
98
Table 4.1
Important symbols . . . . . . . . . . . . . . . . . . . . . . . . 106
Table 4.2
Features of various platform. . . . . . . . . . . . . . . . . . . . 114
Table 4.3
Features of algorithms for comparison . . . . . . . . . . . . . . 130
Table 4.4
Results comparison for Scenario CEI-UPM. . . . . . . . . . . . 133
Table 4.5
Results comparison for Scenario East Lansing. . . . . . . . . . 133
Table 4.6
Results comparison for Scenario Madrid. . . . . . . . . . . . . 134
Table 6.1
Routing table format. . . . . . . . . . . . . . . . . . . . . . . . 145
Table 6.2
Packet format. . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Table 6.3
Evaluated performance of the two candidates. . . . . . . . . . 150
Table 6.4
Neighborhood table and RSS comparisons: Indoor . . . . . . . 152
Table 6.5
Evaluated performance of the selected candidate. . . . . . . . 157
Table 6.6
Neighborhood table and RSS comparisons: Outdoor . . . . . . 158
Chapter 1
Introduction
Recent years have witnessed an increased interest in the use of Wireless Sensor
Networks (WSNs) in various applications such as environmental monitoring, factory
automation, habitat tracking, security surveillance, intelligent transportation and
smart cities. This technology has brought a lot of benefits to the users from both
research and industrial areas. Fig. 1.1 depicts a typical sensor network architecture.
It is envisioned that tens to thousands of miniaturized sensor nodes, which operate
on small batteries, will be deployed to operate autonomously to construct WSNs
in different types of environments. Sensor networks may consist of various types
of sensors such as seismic, low sampling rate magnetic, thermal, visual, infrared,
acoustic and radar, which are able to monitor a wide range of ambient conditions
including [1]: temperature, humidity, light condition, the presence or absence of
objects, mechanical stress levels on attached objects, and the mobility characteristics
such as speed and direction. The sensed data are collected and sent to a base
station directly or via multiple hops depending on the network topology and routing
protocols. In addition to the ability to probe its surroundings, each sensor node
has one or more onboard radios to communicate with other nodes through wireless
communication protocols such as ZigBeeT M [2], BluetoothT M [3] and Ultra-wideband
(UWB)[4] among others. Therefore the combinations of micro-sensing and wireless
communication offers a huge number of possibilities of WSN applications.
The categorization of the applications of WSNs differs by different researches.
Some works categorize the applications into military, environment, health, home
and other commercial areas [5]; whereas some classify them into two categories
[6]: monitoring and tracking. It is possible to expand this classification with other
categories such as event detection and spatial process estimation [7].
In this chapter, we enumerate the WSN applications based on the monitoring
and tracking frame with the items of different implemented areas as mentioned in [5],
Chapter 1. Introduction
Base station
Applications
Figure 1.1: A typical WSN architecture.
the deployment experiences of some applications are reviewed as well. Afterwards,
the challenges of deploying a WSN are identified to state out our motivations and
targets. The main frame of the proposed planning methodology and planning tool
are briefly described at the end of this chapter.
1.1
WSN applications and deployment experiences
The origin of WSNs can be traced back to the 1950s during the cold war between
Soviet Union and the United States. The SOund SUrveillance System (SOSUS) was
developed by the United States Military to detect and track Soviet submarines. In
order to echo the investments made in the 1960s and 1970s to develop the hardware
for today’s Internet, the United States Defense Advanced Research Projects Agency
(DARPA) launched the Distributed Sensor Network (DSN) program in 1980 to
formally explore the challenges in implementing distributed wireless sensor networks.
With the birth of DSN and its progression into academia, the governments
and universities eventually show their interests in this topic and promote the
research atmosphere to improve the performance of WSN and explore new areas
of applications using WSNs, such as air quality monitoring, forest fire detection,
natural disaster prevention and structural monitoring. Then there arises a strong
2
1.1. WSN applications and deployment experiences
demand in the market as researchers made their steps into the corporation with
technology giants of the day, such as IBM, CISCO and Bell Labs, they began to
explore and expand the use of WSNs in our daily life, such as security surveillance,
health monitoring and smart life. Lessons and extensive experiences are learnt
during designing and constructing the wireless sensor networks, which continuously
provide challenging issues and attracts more interests in this area.
1.1.1
Military applications
The C4ISRT (Command, Control, Communications, Computers, Intelligence,
Surveillance and Reconnaissance) systems integrate WSNs to realize military
monitoring and tracking. The systems can be used to monitor friendly forces,
equipment and ammunition.
Every troop, vehicle, equipment and critical
ammunition can be attached with small sensors to report the statuses. Those reports
are gathered in sink nodes or be forwarded to the upper levels of the hierarchy and
aggregated with the data from other units at each level, and sent to the troop
leaders at the end. They are also implemented for battlefield surveillance where
critical terrains, approach routes, paths and straits can be rapidly covered with
sensor networks, to closely monitor the activities of the opposing forces. As a result,
the deployment efficiency and reliability are very demanding for such applications to
allow WSNs being constructed expeditiously and able to cover the area of interest.
There are other military applications developed for targeting and tracking:
Sensor networks can be incorporated into guidance systems of the intelligent
ammunition, they can also be attached to soldiers to track their mobility and
locations. In [8], the invasion of individual enemy soldiers are detected by using
unattended acoustic and seismic sensors in the protecting military sites or buildings.
The Early attack reaction sensor (EARS) [9] is a man-wearable gunshot system. It
uses passive acoustic sensing system with small microphone array to detect gunshots
(muzzle blast and/or shockwave) and provide relative azimuth and range information
of the shot origin to the user. It has been tested in both open field and military
operations in urban terrain (MOUT) environment and has provided useable bearing
and range information against the firing positions. Some systems also perform
localization such as [10, 11] to protect soldiers from potential menace by blasts
3
Chapter 1. Introduction
and snipers. Such applications should pay sufficient attentions to the connections
of nodes.
In [12], hazardous chemicals are detected and identified by their unique infrared
absorption signatures. The application is for deployment on expendable unmanned
aerial vehicles in a nadir-viewing configuration from an altitude of 300 m while
traveling at an air speed of 96 km/h.
The applications of monitoring missile
environment target at dramatically increasing missile active service life, saving
millions of dollars and reducing the number of missiles needed. The challenge of such
real-time monitoring systems is to collect and store data on environmental shock
with high speed and observe vibration (up to 100 g) in missile canisters without
electrical hazards. An optical sensor system capable to monitor shock and vibration
in missile canisters in three dimensions at high speed (5 kHz) is proposed in [13].
1.1.2
Environmental applications
Environmental monitoring has been studied for a long time. It includes the detection
and reaction towards natural disaster such as earthquake and avalanche, the
detections on climate change and pollution, the tracking and observations of animal
behaviors. The old mechanisms recorded data at specific intervals and required
human intervention to download them. The employment of WSN technology in
this field shortens the time and reduces efforts on data aggregation issue, and most
importantly it is capable to monitor/track a huge quantity of the environmental data
without disturbing the natural environment too much compared with the traditional
approach.
W-TREMORS [14] is a platform with high-frequency distributed data acquisition
ability, and it is designed for earthquake engineering and structural monitoring. By
adopting a novel communication protocol together with the developed software,
it is able to test the shaking with inexpensive hardware resources. Wong et al.
from UC Berkeley built a wireless sensor seismic response monitoring system based
on MICA2 motes [15].
The system was tested by using a reinforced concrete
bridge column (Fig. 1.2(a)). However, the accelerometer and the analog-to-digital
converter implemented in the MICA2 motes do not possess the fidelity required
for structural state evaluation. Both works mentioned about resolution problems,
due to quantization significantly affects the low level acceleration readings. Besides,
4
1.1. WSN applications and deployment experiences
(a)
(b)
(c)
Figure 1.2: Environmental applications. (a) The test of WSN on specimen [15], (b)
ZebraNet [16], (c) The volcanic monitoring system[17].
due to the interferences in radio transmission with multiple motes attempting to
communicate simultaneously, the communication packet loss is quite pronounced at
high sampling rates.
ZebraNet system [16] shown in Fig. 1.2(b) is a mobile wireless sensor network
used to track animal migrations. ZebraNet is composed of sensor nodes built into the
zebra’s collar. The node consists of a 16-bit TI microcontroller, 4 Mbits off-chip flash
memory, a 900 MHz radio, and a GPS unit. Positional readings are taken using the
GPS and sent multi-hop across zebras to the base station. The goal is to accurately
log each zebra’s position and use them for analysis. In the demonstration, a total of
6-10 zebra collars were deployed at the Sweetwaters game reserve in central Kenya to
study the effects and reliability of the collars and to collect movement data. After
deployment, the biologists observed that the collared zebras were affected by the
collars. They observed additional head shakes from those zebra in the first week.
After the first week, the collared zebra showed no difference than the uncollared
zebra. A set of movement data were also collected during this study, from which
the biologists can better understand the zebra movements.
5
Chapter 1. Introduction
In volcanic monitoring, the challenges of a WSN application for data collection
include reliable event detection, efficient data delivery, high data rates, and sparse
deployment of nodes. Given these challenges, a network consisting of 16 sensor nodes
was deployed on Volcn Reventador in northern Ecuador [17]. Each sensor node is
a T-mote sky [18] device equipped with an external omni-directional antenna, a
seismometer, a microphone, and a custom hardware interface board. Overall, the
system performed well in this study. In the 19 days of deployment (Fig. 1.2(c)),
the network observed 230 eruptions and other volcanic events. About 61% of the
data was retrieved from the network due to short outages in the network from
software component failure and power outage. Therefore, the node devices in this
type of application should be smaller, lighter, and consume less power to facilitate
distribution and prolong lifetime of the network.
Macroscope of redwood [19] is an experimental application of a WSN that
monitors and records the redwood trees in Sonoma, California. Sensor nodes are
placed at different heights of the tree. Air temperature, relative humidity, and
photo-synthetically-active solar radiation are measured by each sensor node. Plant
biologists track changes of spatial gradients in the microclimate around a redwood
tree and validate their biological theories.
Sensor nodes may be deployed in a forest strategically to relay the exact origin
of the fire to the end users before the fire is spread uncontrollable. Forest fire
monitoring systems require a large amount of sensor nodes being deployed and
integrated using radio frequency/optical systems. Also, they may be equipped with
effective energy harvesting methods [20], such as solar cells, because the sensors
may be left unattended for months and even years. The sensor nodes collaborate
with each other to perform distributed sensing and overcome obstacles, such as
trees and rocks, that block line of sight of sensors. DIMAP-FactorLink provides
another example of forest fire detection. It has developed and integrated a forest
fires detection system using the products of Libelium [21]. The covered area of the
system was about 210 hectares in the north Spain region. Therefore in this type
of application, we should focus not only on the coverage and communication issues,
but also on the lifetime and cost.
Biocomplexity mapping of the environment is done at the James Reserve in
Southern California [22]. Three monitoring grids with each having 25-100 sensor
6
1.1. WSN applications and deployment experiences
nodes are implemented for fixed view multimedia and environmental sensor data
loggers. The ALERT system [23] is another example deployed in the US for flood
detection. The types of sensors deployed in the system include rainfall, water level
and weather sensors.
The authors in [24] developed an underwater WSN application platform for
long-term monitoring of coral reefs and fisheries. The deployed sensor network
consists of static and mobile underwater sensor nodes. The nodes communicate with
each other via point-to-point links using high speed optical communications with an
acoustic protocol integrated in the TinyOS protocol stack. They have several types
of sensors, including temperature and pressure sensing devices and cameras. Mobile
nodes are needed to locate and move above the static nodes to collect data and
perform network maintenance functions for deployment, re-location, and recovery.
WSNs applications on precision agriculture have recently appeared.
For
instances, the experiences in the design, development and deployment of a WSN
are described in [25] to improve water use efficiency for pasture production. Sensor
pods should be designed carefully in this application to withstand seasonal weather
changes and are resistant to damages that may be inflicted by cattle in the field.
Temperature and humidity were measured using the Tmote Sky’s on-board sensors.
70 sensor pods were deployed in December of 2007, at the TIAR (Tasmanian
Institute for Agricultural Research) Elliott Research Farm and managed to gather
correct data from the field. The iCubes were designed with low-cost humidity sensors
to monitor the soil wetness in the work of [26] and the authors in [27] developed
a monitoring system by using TelosB wireless sensor nodes to acquire data such as
temperature, humidity, illumination and voltage. In this application, a web-based
platform integrated with Google Maps was developed to release the greenhouse
environmental status and provide real-time voice and SMS alarm service.
1.1.3
Health applications
Health applications can be categorized into activities of daily living monitoring,
fall and movement detection, location tracking, medication intake monitoring, and
medical status monitoring [28]. The Ultra Badge System [29] is one example of
location tracking application that is used in a hospital setting. In Ultra Badge, a
3D tag system is designed to localize the patients. When a patient is in a specific area
7
Chapter 1. Introduction
where a fall is most likely to occur, the system alerts the caregivers beforehand. The
Ultra Badge System consists of ultrasonic receivers embedded in the environment
and wireless ultrasonic emitters placed on objects as depicted in Fig. 1.3(a). The
positions of the receivers are fixed and known beforehand, therefore the emitters
can be positioned by using the multi-lateration technique. Two subsystems were
developed for real implementations: the wheelchair locator and the ultrasonic radar.
In the former subsystem, the nurse is notified when a patient uses a wheelchair
approaching a ’detection area’, where a fall is most likely to occur. The latter
subsystem aims to monitor the activities of the patients in their beds by using
ultrasonic pulses.
(a)
(b)
Figure 1.3: Health care applications. (a) The Ultra Badge System[29], (b) The
iPackage of [30].
As patients may get allergic by taking some medications or risk of life with wrong
dosage, if sensor nodes can be used in medication monitoring, the chance of getting
and prescribing the wrong medication to patients can be minimized. An intelligent
packaging prototype (iPackage) is developed by Pang et al.[30]. The system is
capable of both remote medication intake monitoring and vital signs monitoring.
It uses an array of Controlled Delamination Material (CDM) films along with the
control circuits. The CDM film is a three-layer foil composed of aluminum bottom
and top layers and an adhesive middle layer made of electrochemical epoxy. When
a voltage higher than a particular threshold is applied on the bottom layer and top
layer, an electrochemical reaction occurs in the middle layer. When the voltage is
8
1.1. WSN applications and deployment experiences
applied for a certain amount of time, the epoxy layer is destroyed and delaminated.
Therefore, the iPackage sealed with a CDM film can only be opened by the special
control appliance which also enables the control of the dosage. The identification of
the correct pill is accomplished by RFID. The prototype design of CDM and tagged
capsule package is depicted in Fig. 1.3(b).
1.1.4
Other applications
In the recent years, the concept of smart cities are proposed as the next stage in the
process of urbanisation. As a city is a system of systems, the more we understand
how those systems interact and share information, the better people can be helped
to make decisions and to make the city better. The smart city can be identified
along eight main axes or dimensions: smart environment, smart energy and water,
smart transportation, smart education, smart healthcare, smart public safety, smart
buildings and urban planning and smart government.
Beyond the previously mentioned applications for smart environment, smart
energy and water and smart healthcare, there exist smart transportation applications
such as the PGS smart parking system [31], which is developed in ICU
Korea based on a new T-Sensor hardware.
And the California Partners for
Advanced Transportation TecHnology (PATH) was established, with the mission
to develop solutions to the problems of California’s surface transportation systems
through cutting edge research, PATH research is divided into three program
areas: Transportation Safety Research, Traffic Operations Research and Modal
Applications Research. The traffic surveillance by WSNs was developed based on
MICA2 DOTS. It was used to control traffic signal, on-ramp metering system to
regulate the flow of traffic on freeway entrance ramps using traffic signals, the system
was able to reduce delay by 102 million person-hours in 2003. The parking guidance
and information system (PGIS), road condition sensing modality were also developed
in PATH research.
SmartSantander project [32] proposes a city-scale experimental research facility
to support typical applications and services for a smart city.
This unique
experimental facility is sufficiently large, open and flexible to enable horizontal
and vertical federation with other experimental facilities. The project envisions a
9
Chapter 1. Introduction
Figure 1.4: Outdoor parking and environmental monitoring deployed architecture
of Smartsantander project.
deployment of 20,000 sensors in Belgrade, Guildford, Lbeck and Santander (12,000
nodes, Fig. 1.4), which exploits a large variety of technologies.
One of the famous sensor nodes producers, Libelium, creates the inspirational
and market research documents on 50 sensor applications. It has comprised and
concluded necessary applications in a infographic (Fig.1.5) which combines Smart
Cities, Internet of Things (IoT) and other sensing applications to construct a smarter
world.
1.2
Challenges when deploying WSNs
One of the major challenges in designing WSNs is the support of capturing and
gathering data requirements while coping with the computation, energy, sensing
ability and communication constraints. As a result, careful node placement can be
a very effective optimization mean for achieving the desired design goals.
In order to prolong the lifetime of WSN, energy conservation methods must be
taken, scheduling and data aggregation are among the commonly used methods.
Scheduling conserves energy by turning off the sensor whenever possible. While
data aggregation tries to conserve energy by reducing the energy used in data
transmission, efficient routing and topology construction can significantly reduce the
energy consumption due to data aggregation. As the energy harvesting researches
blossom nowadays, with the aim to tackle the energy constraint issues for different
10
1.2. Challenges when deploying WSNs
Figure 1.5: Libelium smart world.
areas, it will be able to solve the limited battery resource issue and allow the WSN
applying to more areas.
Connectivity and coverage problems are caused by the limited communication
and sensing ability of sensor nodes. To solve both problems, the solution lays in how
the sensors nodes are positioned with respect to each others. Coverage problem is
regarding how to guarantee that each of the points to be monitored is covered by
the sensors in the region. It is a trade-off problem, in maximizing coverage with low
cost: the sensors need to be placed not too close to each other so that the sensing
capability of the network is fully utilized while the cost is minimized; at the same
time, they must not be located too far away from each other to avoid the formation
of coverage holes (area outside sensing range of sensors). On the other hand, it is
also a trade-off problem in terms of connectivity and cost: the sensor nodes need to
be placed close enough so that they are within each other’s communication range
and ensure the connectivity of WSN with robustness and reliability.
11
Chapter 1. Introduction
1.2.1
Necessity of simulation
The emergence of wireless sensor networks brought many new challenging issues
to WSN designers.
Traditionally, the three main techniques for analyzing the
performance of wired and wireless networks are analytical methods, computer
simulation, and physical measurement.
Due to many constraints imposed on
sensor networks, as mentioned above, the energy limitation, sensing ability
and communication constraints, the deployment of sensor networks tends to be
quite complex and requires mastering of cross knowledge on networking, radio
propagation and embedded system.
Furthermore, although the aforementioned
WSN applications have been implemented, there is still a lack of analysis and
experiences to guide WSN designers to construct WSN topology successfully without
many trials. Therefore, it appears that simulation is a feasible approach to the
quantitative analysis of the performance of wireless sensor networks.
The authors of [33] introduce their deployment experiences of various WSN
applications and recommend that simulation should come first before real
deployment. But for getting realistic results, one must have a realistic simulation
environment, in which all parameters concur to an accurate description of the
environment, platform and operation. In order to achieve realistic results, one can
barely rely on the pre-defined parameters found in the literature.
Currently, there are many conventional open source and freeware WSN
simulation tools that are publicly available or in academic research use. Some
examples are NS-2 [34], OMNeT++ [35], Worldsens [36] and TOSSIM [37], in which,
the network protocols can be programmed and configured according to users’ desires
and provide the convenience to simulate and evaluate performances at protocol
levels. However, radio signal propagation is a very complex phenomenon since
it is three-dimensional and influenced by many disturbances that are caused by
the environment. Multi-path fading and attenuation directly contribute to the
reliability and range of the wireless network.
The current network simulators
all use very simple physical channel and environment modeling. As default, the
propagation models in these simulators are based on predefined empirical functions
or assume as line-of-sight connection where no obstacles between the transmitter and
receiver. Despite their powerful ability in validating WSN performance at protocol
functionality level, they are not able to consider the impacts of realistic environments
12
1.3. Introduction to the planning algorithms and tools
on topologies, protocols and deployments, and as a result, the performance of
designed WSN can not be practically estimated, which might significantly influence
the real implementation.
Besides the network simulators for validating protocol levels, several other tools
have been developed for radio propagation level or pre-deployment level to tackle
different challenging issues for WSNs.
In [38], the authors introduced several
state-of-the-art radio propagation simulators, such as the EDX Signal pro [39],
Winprop (AWE) [40] and CINDOOR [41], all of which are featured with 3D indoor
modeling and ray-tracing propagation modeling in the simulation. The simulation
and modeling of radio propagation for WSN can significantly reduce the work effort
and costs to ensure the connectivity of WSNs. For obtaining more accurate results,
the suitable radio simulator for WSNs is envisaged to support the importation of 3D
indoor and outdoor environment model, the radio and antenna should be definable,
the radio algorithm should be able to detect the multi-path effects including the
direct path, reflections and diffractions.
After investigating on the deployment issues and performance evaluation issues
of WSNs, two questions arise:
• Why can’t a good radio frequency simulator be used in collaboration with
a network simulator to provide practical WSN performance analysis for any
specific 3D environment and application?
• Once provided with the evaluated performance of WSN topology, is there any
smart tool to assist WSN designers improving the overall performance of WSN
automatically and efficiently instead of manually adjust the designed topology
and estimate repeatedly through network simulator?
The answers to those questions are directedly pointed to smart planning tool for
WSNs, which is desired and strongly demanded by the WSN designers.
1.3
Introduction to the planning algorithms and tools
There are several WSN planning algorithms and tools developed in recent years. The
3D indoor planning heuristic (LowCost) [42], to the best of our knowledge, is the first
indoor 3D WSN deployment heuristic that considers impacts of obstacles on sensing
13
Chapter 1. Introduction
signal and radio communication. It consists of two steps: Provided a 3D indoor
environment model with furniture and obstacles recorded, the first step calculates
the coverage to deployment cost ratio for all the candidate points in the deployable
area. Sensor nodes are iteratively put at the point with the maximum coverage to
deployment cost ratio, so that the target region is covered with the minimum sensor
node cost after this step. Then the connectivity of the deployed nodes is checked
in the second step. The authors consider two options to satisfy the connectivity of
WSN, the prior one is realized by moving the unconnected node towards the closest
connected node without influencing the sensing coverage of the first step; otherwise,
if the preferential option is not applicable, extra sensor nodes will be added along
the line between the unconnected node and the closest connected node. Note that
despite this approach manages to cover the sensing area with the ”minimum cost”,
the connectivity of the WSN is ensured by simply moving or placing extra nodes
without carefully selecting optimal positions to decrease the hardware cost, improve
the link quality or prolong the network lifetime. Moreover, although the modelling
of the sensing signal considers obstacles, the radio propagation model is too simple
because the communication links are established only between line-of-sight (LOS)
nodes, which is obviously not true in the real-world propagation.
The MOGA algorithm [43] employs multi-objective genetic algorithm, which is
proved to be efficient in solving NP-hard problem, to evolve the decision. Topology
solution for the same network varies at different runs which provides more options
than the deterministic approach of LowCost. However it focuses on maximizing
the sensing coverage and prolonging the network lifetime with a predetermined
number of nodes, as a result the hardware cost can not be optimized. Moreover
the modeling of radio signal and sensing signal are based on ideal disc model thus
it is not environmental sensitive.
The previous two methods are developed for planning the homogeneous WSNs.
However, in many prototypical systems available today, sensor networks normally
consist of a variety of different devices. Nodes may differ in the type and number of
attached sensors [32, 44]; some computationally more powerful computenodes
may collect, process, and route sensory data from many more limited sensing nodes
[45].
14
1.3. Introduction to the planning algorithms and tools
There are several works focused on planning heterogeneous network, such as in
[46], the relay node placement problem for WSNs is concerned as placing a minimum
number of relay nodes into a WSN to meet certain connectivity or survivability
requirements. In that work, the authors assume that there may be some physical
constraints on the placement of relay nodes and they study constrained versions
of the relay node placement problem, where relay nodes can only be placed at a
set of candidate locations. In the connected relay node placement problem, they
want to place a minimum number of relay nodes to ensure that each sensor node is
connected with a base station through a bidirectional path. In the survivable relay
node placement problem, they want to place a minimum number of relay nodes to
ensure that each sensor node is connected with two base stations (or the only base
station in case there is only one base station) through two node-disjoint bidirectional
paths. For each of the two problems, they discuss its computational complexity and
present a framework of polynomial time O(1)-approximation algorithms with small
approximation ratios. Numerical results show that their approximation algorithms
can produce solutions very close to optimal solutions. The authors of [47] propose
an approximation algorithm to find a feasible solution for relay node placement
to deploy a minimum set of relay nodes in such a fashion that each sensor node
must have at least one relay node within its one hop distance and all deployed relay
nodes eventually form a connected network among themselves including one or more
base-stations. The work reveals an approximation algorithm that runs in O(n2 ) time
complexity, to find a feasible solution for above challenge. The authors also describe
a framework to solve the above problem in non-convex shaped deployment region.
[48] targets at providing hight efficiency and QoS and it presents a polynomial-time
algorithms which is QoS-aware relay node placement using minimum Steiner tree
on Convex hull.
The work in [49] proposes Multiple-Objective Metric (MOM) for base station
placement in wireless sensor networks to fairly increase various properties.
It
considers four different metrics for base station placement in WSNs. First, the ratio
of sensor nodes which can communicate with a base station via either single-hop
or multi-hop represents the coverage of sensor nodes. Second, the average ratio of
connected sensor nodes after the failure of base stations represents the fault tolerance
of a network. Third, the average distance between sensor nodes and their nearest
15
Chapter 1. Introduction
base station represents the energy consumption of a network. However, as discussed
before, not only the distance but also the obstacles lead to attenuation of received
signal strength. Moreover, more energy is consumed at nodes with larger degree, as
a result the energy consumption is not practically modelled by this work. Fourth,
the standard deviation of the degree of base stations represents the average delay of
a network due to congestion. The limitation of this algorithm is that sensor nodes
should be pre-located by designers, which neither guarantees the sensing coverage
without expert experience nor allows optimizing the hardware cost for WSN.
The authors in [50] develop a tool that integrates a developed 3D indoor
deployment heuristic together with NS-2 simulator to assist designers deploying and
analyzing the performance of network. They propose a heuristic that minimizes
hardware cost while satisfying requirements on coverage and connectivity. The
network topology is constraint to the type of cluster tree and three different devices
are provided: the coordinator, router and sensor. Sensors can only communicate
with routers and coordinator. The heuristic considers radiation pattern of antenna
as well as the effects of obstacles by using accurate ray-tracing algorithm. Once
the topology is generated, the integrated NS-2 simulator is driven to simulate the
packet drop rate and latency, and the results are demonstrated to users. The
merit of this method is the integration of authorized network simulator to evaluate
the performance of generated topology which provides a much more practical
implication on packet delivery performance to designers. However, as the evaluation
from NS-2 has no contribution to improve the generated topology, the proposed
deployment heuristic should be run several times so that by a certain chance,
designers can observe a satisfied solution with low cost, low drop rate and latency.
The user interface of this work is shown in Fig. 1.6. It allows users prosecute
many configurations including map, node properties, topology constraints and
environment types. The generated topology can be shown and results evaluated
by NS-2 are reported on the interface.
Another state-of-the-art method for deploying relay node and sink node for
indoor environment is proposed in [51, 52], the tool allows users defining the
node demand zones, power source, sensing interval and transmission delay. By
encapsulating those metrics into a complete requirements model, the tool optimizes
the infrastructure of WSN and maximizes the utility function which provides
16
1.3. Introduction to the planning algorithms and tools
Figure 1.6: The interface and demonstration of the work in [50].
a normalized equation that observes the coverage, link quality, lifetime and
infrastructure cost. Fig. 1.7 and Fig. 1.8 are two examples of generated solutions
for single-hop and multi-hop topologies respectively.
The lifetime (L) of sensor node is considered in that work and is modelled by
(1.2), The electric charge of a sensor node EC, expressed in mAh, is calculated
according to (1.1) where Ia and Is are the power consumption in active state and
sleep state respectively. ta and ts represents their time durations in a node interval.
The current capacity of the battery CC is expressed in mAh.
EC =
3600
× (ta × Ia + ts × Is )
ta + ts
L=
CC
EC
(1.1)
(1.2)
As it can be noticed from the formulations, the model of lifetime only considers the
impacts of different states and their corresponding time durations, while the number
of packets forwarded for other nodes are ignored which is not realistic especially for
multi-hop topology. Besides, the authors did not consider the impacts of packet
delivery ability (latency and drop rate) to ensure a more reliable WSN topology.
17
Chapter 1. Introduction
Sensor nodes in this work should also be pre-determined by users and therefore
node locations and cost are not optimized.
Figure 1.7: Demonstration of the single-hop solution by the work in [51].
Figure 1.8: Demonstration of the multi-hop solution by the work in [51].
18
1.3. Introduction to the planning algorithms and tools
Some works are developed to tackle the modelling of sensing signal and radio
signal to make the deployment algorithm more practical and accurate. The authors
in [53] develop a probabilistic sensing model for sensors with line-of-sight-based
coverage (e.g. cameras). The probabilistic sensing model takes into consideration
sensing capacity probability as well as critical environmental factors such as terrain
topography. Besides, they also implement several optimization schemes for sensor
placement optimization. Sensor deployment in network-structured environments
is studied in [54] and it aims to achieve k-coverage while minimizing the number
of sensor nodes. The coverage problem of wireless sensor networks for the rolling
terrains is studied in [55] to derive the general expression of the expected coverage
ratio for regular terrains and irregular terrains.
To enhance the WSN lifetime, the authors in [56] propose a deployment
strategy with a non-uniform deployment method and an alternative duty mode
to balance the energy consumption of sensor nodes in chain-type WSNs. To make
the deployed network resilient to faults caused by communication errors, unstable
network connectivity, and sensor faults, the authors in [57] present an approach,
called FTSHM (fault tolerance in Structural health monitoring (SHM)), to repair
the network with redundant backup nodes and guarantee a specified degree of
fault tolerance. FTSHM searches the repairing points in clusters and places a
set of backup sensors at those points by satisfying civil engineering requirements.
FTSHM also includes a SHM algorithm suitable for decentralized computing in
energy constrained WSNs, with the objective to guarantee that the WSN for SHM
remains connected in the event of a sensor fault thus prolonging the WSN lifetime
under connectivity and data delivery constraints. Table. 1.1 provides a comparative
summary of the characteristics of the static node placement mechanisms discussed
in this section.
Optimal node placement is a very challenging problem that has been proven
to be NP-Hard for most of the formulations of sensor deployment [59, 60, 61].
The deployment of WSNs should satisfy the requirements on the 3D sensing
coverage, guaranteeing connectivity, prolonging the network longevity and reducing
the hardware cost.
However the existing planning algorithms and tools are to some extent have
serious limitations to practically design reliable WSN topology. Only a few of
19
Chapter 1. Introduction
[46]
[43]
Generic
Generic
Indoor
Generic
Generic
Application
3D
1D
2D
2D
2D
2D
2D
3D
Space
Deterministic
Deterministic
Deterministic
Deterministic
Random
Random
Deterministic
Random
Deterministic
Deployment
Sensor
Sensor,Sink
Sensor
Sensor
Relay node
Base station
Relay node
Relay node
Sensor
Sensor
Node
type
Connectivity, Fault Tolerence
Engergy consumption
Congestion
Lifetime,Cost
QoS, Cost
Coverage
Cost
Coverage
Data fidelity
Coverage
Cost
Lifetime
–
–
Table 1.1: Comparison of planning algorithms
[47]
Generic
Network
Structure
Random
Sensor
Fault tolerance
[55]
[56]
Deployment cost
Hardware cost
Coverage, Life time
Connectivity
Lifetime
Hardware cost
-
Fixed node count
Place of relay node
One sensor attached to
one relay node
–
Linear align deployment
Convex hull
–
–
3D
Deterministic
Deterministic
Relay node, Sink
Sensor
Coordinator
Router
Link quality,Lifetime
Infrastructure cost
Cost
Coverage, Connectivity
Coverate, Connectivity
Sphere sensing model
Indoor
3D
Connectivity, Hardware cost
Constraints
[49]
Generic
Generic
3D
Deterministic
Sensor
Objectives
[58]
[48]
Generic
1D
Deterministic
Paper
[53]
Generic
3D
[42]
[54]
Outdoor
Generic
Generic
Structure
Monitoring
[50]
Indoor
[57]
[51, 52]
20
1.3. Introduction to the planning algorithms and tools
them tackle the 3D deployment issue, and an overwhelming number of works are
proposed to place devices in 2D scheme. Without considering the full dimension, the
impacts of environment to the performance of WSN are not completely studied, thus
the values of evaluated metrics such as connectivity and sensing coverage are not
sufficiently accurate to make proper decision. Even fewer planning methods model
the sensing coverage and radio propagation by considering the realistic scenario
where obstacles exist.
Radio signals propagate with multi-path phenomenon
in the real world, in which direct paths, reflected paths and diffracted paths
contribute to the received signal strength. Besides, obstacles between the path
of sensor and objects might block the sensing signals, thus create coverage hole
in the application. None of the planning algorithms model the network longevity
properly and practically. They often employ unilateral and unrealistic formulations.
The optimization targets are often one-sided in the current works.
Without
comprehensive evaluation on the important metrics, the performance of planned
WSNs can not be reliable and entirely optimized.
The diversity and variety of the deployed environment of WSNs have significant
impacts on radio communication and sensing coverage. As a result, it is crucial
for WSN designers having accurate environment model to prosecute realistic radio
propagation and coverage estimation.
However, in conventional approaches of
constructing 3D environment model, people have to use a third party CAD tool
or geometric scripts to reconstruct the target environment database. The prior
knowledge of the detailed geographical information can only be obtained via manual
measurements or from the city hall. Instead of doing so, people can buy such
services from professional 3D modelling agencies. When the application scenario
gets larger, the cost grows higher. Therefore, either time and efforts or money
is needed in conventional approaches to construct 3D environment models. While
none of the current network planning works or radio propagation researches figure
out any method to model the 3D deployment environment efficiently and accurately.
Many researchers are trapped by this issue and their algorithms/models can only be
evaluated always in the same scenarios, without the possibility to test the robustness
and feasibility for implementations in different environments.
21
Chapter 1. Introduction
1.4
Proposed methodology and work flow:
Main
contributions
Motivated by all the reasons and challenging issues mentioned in the previous
sections, this work dedicates to explore and develop state-of-the-art planning
methodology and smart planning tool to assist WSN designers efficiently designing
reliable WSN deployments.
• A new method is proposed to efficiently and automatically model the 3D indoor
and outdoor environments. The advantages of image understanding algorithm
are applied to automatically recognize objects from the satellite images of
the outdoor target regions and from the scanned floor plan of indoor area,
thereafter 3D outdoor/indoor scenarios are reconstructed automatically and
efficiently for signal propagation and network planning purpose. Its mechanism
offers users a flexibility to reconstruct different types of environment without
any human interaction. Thereby it significantly reduces human efforts, cost
and time and allows WSN designers concentrating on the planning issues.
• An efficient ray-tracing engine is developed to accurately and practically model
the radio propagation and sensing signal on the constructed 3D map. By using
the kd-tree space division algorithm and modified polar sweep algorithm, the
signal rays are traced efficiently without detecting all the primitives in the
scene. The proposed radio propagation model, which emphasizes not only
the materials of obstacles but also their locations along the signal path, is
applied to compute the received signal strengths for all candidate receivers.
The sensing signal of sensor nodes is tracked by taking advantage of this
obstacle sensitive approach.
• WSN planning method is proposed to tackle the above mentioned challenges
and efficiently deploy reliable WSNs. More metrics (connectivity, coverage,
cost, lifetime, packet latency and packet drop rate) are modeled more
practically compared with other works. Especially 3D ray tracing method
is used to model the radio link and sensing signal which are sensitive to the
obstruction of obstacles; network routing is constructed by using widely-used
AODV protocol; the network longevity, packet delay and packet drop rate are
22
1.4. Proposed methodology and work flow: Main contributions
obtained via simulating practical events in WSNet simulator, which to the
best of our knowledge, is the first time that network simulator is involved in
a planning algorithm. Moreover, a multi-objective optimization algorithm is
developed to cater for the characteristics of WSNs. The capability of providing
multiple optimized solutions simultaneously allows users making their own
decisions accordingly, and the results are more comprehensively optimized
compared with other state-of-the-art algorithms.
• iMOST is developed based the novel planning methodology, by integrating
the automatic 3D reconstruction method, the ray-tracing engine and the
planning algorithm, to assist WSN designers efficiently planning reliable
WSNs for different configurations.
The abbreviated name iMOST stands
for an Intelligent Multi-objective Optimization Sensor network planning Tool.
iMOST features with: (1) Convenient operation with a user-friendly vision
system; (2) Efficient and automatic 3D database reconstruction and fast 3D
objects design for both indoor and outdoor environments; (3) It provides
multiple multi-objective optimized 3D deployment solutions and allows users
to configure the network properties, hence it can adapt to various WSN
applications; (4) Deployment solutions in the 3D space and the corresponding
evaluated performance are visually presented to users; and (5) The Node
Placement Module of iMOST is available online as well as the source code
of the other two rebuilt heuristics. Therefore WSN designers will be benefit
from this tool on efficiently constructing environment database, practically and
efficiently planning reliable WSNs for both outdoor and indoor applications.
With the open source codes, they are also able to compare their developed
algorithms with ours to contribute to this academic field.
An overview of the planning tool is shown in Fig.1.9. As can be seen, it
contains a user interface and three functionality modules: Image Processing
Module, Ray-tracing Propagation Module and Node Placement Module. They
are developed and embedded together to make the planning tool powerful and
useful, thus contribute on the above mentioned aspects.
The user friendly interface: provides user interaction with the tool and
algorithms.
It allows importing objects such as furniture for indoor
23
Chapter 1. Introduction
WSN Planning Tool
Brick wall
Glass
wall
D
4
D
1
v1
w1
v2
w7
w6
TX
ray
ray 1
RX
2
D
w2
2
w3
w5
D
3
w4
Image Processing Module Ray-tracing Propagation Module
Node Placement Module
Figure 1.9: The proposed methodology and tool.
environment and vegetation and car for outdoor environment; it supports
defining the sensing area AS in the space, pre-deploying nodes at some
specific locations and configuring the parameters such as the transmission
power and receiver sensitivity of the antenna and sensing ability of
sensors; the deployment solution is visually provided to the user by
indicating the locations of nodes, the constructed topology and the
evaluation results.
The Image Processing Module (IPM): is
in
charge
of
automatic
3D environment database reconstruction for indoor and outdoor
environments.
The algorithm is able to recognize different obstacles
automatically with high accuracy. The birds’ view RGB images of the
outdoor region (taken from satellite camera or sketched from websites
like Google Maps) or the scanned map of indoor space should be
prepared beforehand. Afterwards, the recognized result is vectorized and
the 3D database is built accordingly without human supervision.
The Ray-tracing Propagation Module (RPM): employs the developed
ray-tracing method to trace the propagation path for both radio and
24
1.5. Conclusion
sensing signal. Obstacles are considered along the ray path, therefore
Non-line-of-sight (NLOS) obstruction is detected for the sensing signal
and the multi-path effects and attenuations due to different obstacles are
calculated in a proper way to enhance the accuracy of radio propagation
estimation.
The Node Placement Module (NPM): provides
multi-objective
optimized WSN deployment solutions according to the user configuration.
This module employs the proposed multi-objective planning method
and iteratively searches the best topologies to simultaneously trade-off
among the sensing coverage, connectivity, cost, WSN longevity and
packet delivery status.
1.5
Conclusion
In this chapter, the widespread applications of WSNs are introduced in terms of
military, environment, health monitoring and tracking, which nowadays are evolved
to integrate different application categories to construct our world a smart world.
Due to the lack of available experiences and guidance to assist WSN designers
efficiently planning reliable and optimized WSN topology for various applications,
practical and efficient planning methodology and tool are strongly demanded to
facilitate WSN design step so as to reduce human efforts, cost and optimize network
performance before real field deployment.
Several state-of-the-art planning algorithms and tools are investigated and
compared in this chapter, from the computation dimension, optimization objectives,
modelling of objectives and application constraints. Besides of the advantages, the
limitations of them are discussed thoroughly.
A novel planning methodology is proposed in this work, to librate WSN designers
from time consuming and costly 3D outdoor/indoor environment modelling, by
using the proposed automatic image understanding and vectorization algorithm; to
increase the accuracy and efficiency in radio propagation estimation and sensing
signal tracking through the developed ray-tracing engine; to practically model
important metrics; and to comprehensively optimize the performance of WSNs by
25
Chapter 1. Introduction
properly selecting nodes and their properties based on multi-objective optimization
method.
An intelligent multi-objective optimization sensor network planning tool
(iMOST) is developed in this work as well. It is a practical realization of the proposed
planning methodology, the user-friendly interface facilitates the user operations, and
visually demonstrates results. The three functional modules of iMOST work together
to realize the proposed planning methodology. The planning tool, the planning
methodology and the algorithms contribute to this work will be described in details
throughout this dissertation.
26
Chapter 2
3D environment reconstruction
method
2.1
Introduction
WSN designers should be provided with the information of the region where the
WSN will be deployed, so that the proper locations for placing sensor nodes can
be decided to achieve application requirements. The important factors that affect
the deployment are the obstacles and irregular terrains which can attenuate radio
signal strength and block continuity of sensing region; there may exist particular
areas that prohibit to place sensor nodes and also exist some other regions to be
monitored.
A traditional and standard approach to create a 3D model for a region is to build
from scratch using tools such as CAD software or scripts that can be imported to a
demonstrator. Roads, vegetation and building blocks can be described in the form
of primitive 3D shapes based on which terrain data or maps should be provided
in advance: either obtain from manual measurements, from city hall or purchase
from professional company. Therefore, this geometry-based modeling technique is
time-consuming and costly, especially for constructing large-scale scenarios where
many obstacles exist. For many radio propagation experts, the major challenge is
that rather than analyzing the radio performance, they are not skillful at preparing
the environment model.
Moreover, according to the survey in the academic
conferences, we notice that some research institutes use the same scenario all the
time to analyse their work without the possibility to test the robustness and accuracy
for different environments.
Chapter 2. 3D environment reconstruction method
2.1.1
3D environment reconstruction from Lidar systems
The 3D environment reconstruction system may consists of automatic building
extraction and reconstruction [62], road extraction and reconstruction [63],
vegetation extraction and reconstruction [64]. Such applications require detailed and
reference models that are still usually created manually. Several recent techniques
[65] aim to increase the level of automation and realism by starting with actual
images of the object or directly digitizing it with airborne laser scanning. Fig. 2.1
and Fig. 2.2 shows the general principle of the airborne laser scanning.
The
Figure 2.1: Airborne laser scanning.
Figure 2.2: Example of the DSM and its original image.
standard features of recent airborne lidar systems are their ability to discriminate
between first and last pulse reflections. A laser pulse that is fired over an object
usually has multiple reflections. Some of the laser pulses may be reflected by the
28
2.1. Introduction
top of the object and therefore represents the first returning pulse. The remainder
is likely to be reflected by the ground and hence generates the last returning pulse.
Through computing the arrival time of the laser signal, lidar produces a fast and
highly accurate three-dimensional Digital Surface Model (DSM) [66]. Thereafter the
objects are extracted by using several image processing algorithms such as the edge
detection and shape based classification [67],[68]. The limitation of this method is
the high cost and efforts to obtain the scanned data, as helicopter or airplane is
needed to carry the laser scanner and many human efforts are needed to realize
the task. Therefore it is impractical to use such expensive technology for radio
propagation simulation and network planning issues.
2.1.2
2.1.2.1
3D environment reconstruction from images
Multi-view stereo reconstruction
There are some works proposed to use multi-view stereo reconstructions that are
efficient to provide dense and full 3D reconstructions from multiple views. These
methods can be classified into:
1. Visual hull reconstruction. Obtain the 3D representation of an object through
exploiting the silhouette information. The principle of this method is shown in
Fig. 2.3. This technique assumes the texture of objects can be separated from
the background. Under this assumption, the original image can be converted
into a binary image, which we call a silhouette image. The foreground mask,
known as a silhouette, is the 2D projection of the corresponding 3D foreground
object from a specific view point. Along with the camera parameters, the
silhouette defines a back-projected generalized cone that contains the actual
object. This cone is called a silhouette cone and the intersection of the two
cones is called a visual hull [69] which is a bounding geometry of the actual
3D object.
2. Space carving reconstruction.
Generates an initial reconstruction that
envelopes the object to be reconstructed.
Then it iteratively removes
unoccupied regions from an explicit volumetric representation. All voxels
falling outside of the projected silhouette cone of a given view are considered
inconsistent and are eliminated from the volume, see Fig. 2.4.
29
Chapter 2. 3D environment reconstruction method
Figure 2.3: The intersection of silhouette cones defines an approximate geometric
representation of an object called the visual hull. A visual hull has several desirable
properties: It contains the actual object, and it has consistent silhouettes [70].
3. Image-based rendering. Graph-cut algorithm is used in combination of carving
approach, silhouette information to obtain higher precision. Fig. 2.5 shows the
principles of the light field rendering method by [72]. This technique rotate the
source of light along with the object platform, therefore different projections
of the object with constant incident angle of light are taken by fixed camera.
As can be seen, the multi-view stereo reconstruction requires multiple images
of different views for the same scenario, where the objects are a few, in most cases
there is only one object, with a texture quite different than the background. When
the scenario becomes large and complex, or when there is no multiple stereo view
available, this technique is not applicable any more.
2.1.2.2
Single image reconstruction
Most recently, researchers begin to explore reconstruction from monocular view from
a single image [73, 74]. The 3D reconstruction from single image is a challenging and
attractive issue, as unlike human vision system and brain which have been trained for
years and decades to predict the invisible part of an object by observing a single view,
30
2.1. Introduction
Figure 2.4: Example of space carving reconstruction [71]. (a) The plane-sweep
algorithm ensures that voxels are visited in order of visibility with respect to all
active cameras. The current plane and active set of cameras is shown in orange. (b)
The shape evolves and new cameras become active as the plane moves through the
scene volume.
Figure 2.5: Object and lighting support and the prototype camera gantry [72].
the computer is much simpler, and usually more than most other computer vision
problems, single-view reconstruction is a highly ill-posed problem. To intimate the
human vision, researchers integrate machine learning method to train the dense map
database, to recognize objects or the shapes of specific images. Ashutosh Saxena
et al.[75, 76] patch the images for both 3D location and 3D orientation and use a
Markov Random Field (MRF) to infer a set of ”plane parameters” that belong to
each small homogeneous path in the image. After training the MRF via supervised
learning, the approach (see Fig. 2.6) is able to estimate the relationships between
31
Chapter 2. 3D environment reconstruction method
different parts of the image thus generate the depth map for the test images. This
method lacks the ability to model the invisible structure of the objects. In some
works, human interaction is necessary to indicate the important feature markers.
Zou et al. [77] uses a set of auxiliary reference grids to precisely reconstruct both
polyhedral objects and curved-surfaced objects from a single image with unknown
camera parameters. In that method, users should first define the edges and vertices
of the objects, based on which, the camera is calibrated and reference grid is obtained
accurately. Afterwards, the 3D wire frames of object is generated and surfaces are
rendered, as a result it can be applied only when there are a few objects on the image,
otherwise the human efforts spent on marking feature points will be unaffordable.
Original
image
Ground
truth of
depth map
(a)Training Database
(b)Training procedure
(c)Test result
Figure 2.6: (a) The database of the depthmap is obtained by using the color map
of laser scan; (b) The feature vector for a superpixel, which includes immediate and
distant neighbors in multiple scales. The relationships are learnt through training
with the groundtruth database; (c) An example of the test result which indicate the
depth map of the test image with color scale.
2.2
3D outdoor environment reconstruction
The outdoor environments, where the WSNs will be deployed in our work, are the
European urban cities. The dominant types of obstacles for such environments are
buildings, trees and cars of various colors, textures and shapes.
32
2.2. 3D outdoor environment reconstruction
Although the airborne laser scanning (ALS) can accurately reconstruct large
scale outdoor environment, it requires expensive hardware equipments (e.g.
helicopter, laser scanner), time and a lot of manpower which violate the purpose of
reducing the cost and efforts for deploying WSNs. Whereas the reconstruction from
multi-stereo images are only suitable for simple scenarios with a few objects. It is also
a very tough task to fetch city images with different stereos and views. For example,
Google street view research group record street-level imagery by mounting the street
view camera system on custom road vehicles, trike and modified snowmoble Fig. 2.7.
A lot of human efforts are needed to register the information with the global scale
and to construct the 3D models. Therefore this method is not suitable for our work
either.
In the early 21st century satellite imagery became widely available when
affordable, easy to use software with access to satellite imagery databases was
offered by several companies and organizations. Several other countries have satellite
imaging programs, and a collaborative European effort launched the ERS and
Envisat satellites carrying various sensors. All satellite images produced by NASA
are published by Earth Observatory and are freely available to the public; Satellite
images of different resolutions are available on Google and Yahoo Maps, which
provide us the opportunity to access the satellite images freely. Since all the objects
are visible on the images in pixels, it is convenient to indicate and locate the objects
without measuring the geographic information in the real campaigns, which provides
us the opportunity to reconstruct the cities and outdoor environments conveniently.
However, as the scenario can be very large and complex, it is impractical and
tedious for users manually indicating each primitive on the images. To avoid such
a time and effort consuming procedure, we proposed a solution to automatically
reconstruct the scenarios. This method should be able to simultaneously distinguish
different objects on a image. Moreover, the recognitions must be realized pixel
wisely so that it is suitable for further implementation for radio propagation and
network planning. Thanks to the long historic development of image understanding
researches, there are several state-of-the-art algorithms appeared in the most recent
years which make it possible to recognize multiple objects pixel wisely without any
human supervision.
33
Chapter 2. 3D environment reconstruction method
Figure 2.7: Google street view tools. From top to bottom and from left to right:
street view car, snowmobile, trekker, trike and trolley.
He et al. [78] and Belongie et al. [79], are examples of those who propose to
identify the objects by shape feature descriptor. Although the detailed shape-based
matching algorithm can be quite different, the principles of them are similar, which
basically consist of 3 steps:
1. Solve the correspondence between shapes;
2. Identify the model class to which the input object belongs;
3. Provide the point correspondences on the matched contours or further refine
the matched points to provide accurate matched result between the actual
shape and reference shape model.
34
2.2. 3D outdoor environment reconstruction
The limitation of using shape-based algorithm to understand urban city environment
lays in the variety of shapes for the same type of objects.
Arivazhagan et al. [80] use color and texture features to extract fruit from simple
background. He et al. [81] and Shotton et al. [82] recognize different obstacles based
on machine learning incorporating texture, layout, and context information, which
provide more comprehensive methods to distinguish multiple objects from complex
scenarios. Shotton et al. [82] proposed a texton-boost algorithm that iteratively
selects discriminative texture-layout filters v[r,t] (i) to compute weak learners, and
M
m
combines them into a strong classifier of the form H(c, i) =
m=1 hi (c). Each
weak learner hi (c) is a decision stump based on the response
hi (c) =
⎧
⎨a[v
[r,t] (i)
> θ] + b
⎩k c
if c < C
(2.1)
otherwise
For those classes that share the feature c ∈ C, the weak learner gives hi (c) ∈
{a + b, b} depending on the comparison of feature response to a threshold θ. For
classes not sharing the feature, the constant k c makes sure that unequal numbers of
training examples of each class do not adversely affect the learning procedure. The
machine learning algorithm, to some extent, intimates the learning procedure of
human brain, turns out to be more suitable for our work. Ideally, once the method
learns the features of different objects in different environments, it is able to indicate
pixel wisely each objects on the images robustly no matter how the WSN deployment
environment varies.
2.2.1
Proposed algorithm for outdoor environment reconstruction
In this thesis, the algorithm proposed by Shotton et al.
[82] is extended and
sub-sampling and random feature selection techniques are used for iterative learning
of the images. The estimated confidence value of each class for each pixel can be
reinterpreted as a probability distribution using soft max transformation [83] to
calculate the texture layout potentials. The work flow of the proposed method is
indicated in Fig. 2.8.
First of all, a set of images of the target region should be taken: either through
super high resolution satellite camera or through standard hand-held camera. The
35
Chapter 2. 3D environment reconstruction method
Data
compression
and
Vectorization
A large Scenario (a composition
of small images of similar sizes)
Recognition and Segmentation
3D reconstruction
Figure 2.8: The work flow of the 3D outdoor environment reconstruction.
properties of those images should be similar to that of the training images on the
size, the angel of view, the resolution and so on. The construction of training image
set will be discussed in Section 2.2.2.
Thereafter the developed image understanding algorithm and segmentation
algorithm are applied to automatically recognize objects in the provided images
and extract them pixel-wisely.
Image regularization and vectorization algorithms are employed to regularize
the segmented objects into compact primitive shapes. Their position, rotation and
scale information is registered with reference shapes, so that the feature points can
be extracted and objects are vectorized with a 2D planar coordination. The GIS
information is obtained and transform to the format of longitude and latitude.
At the end, a ’KML’ log file is generated according to the vecotization result.
The file can be demonstrated visually in software like Google Eearth and it provides
an open access of GIS data for all kinds of purpose, especially for radio propagation
simulation and network planning community that have urgent demands on GIS
information of various environments.
2.2.2
Image database
Our method starts from constructing the training of an image database, through
which the features of images are learnt and discriminated for the concerned types of
objects. In this work a bench mark image database and a user defined database are
36
2.2. 3D outdoor environment reconstruction
used. The color code for different objects are indicated in Fig. 2.9(c) and example
images of the two databases are shown in Fig. 2.9 and Fig. 2.10 respectively.
The Microsoft Research Cambridge (MSRC) database is composed of 591
photographs of the following 21 object classes: building, grass, tree, cow, sheep, sky,
airplane, water, face, car, bicycle, flower, sign, bird, book, chair, road, cat, dog, body,
and boat. A subset of original images are shown in Fig. 2.9(a). The corresponding
ground truths are shown in Fig. 2.9(b), where each color maps uniquely to an object
class label according to the color codes. In this database all the images have similar
size which is approximately 320 × 240 pixels.
Note that, in reality there may exist some scenarios contain other objects rather
than those in the 21-object database and different visual angles than that provided
by the images. To tackle the problem, one can label new object classes manually
by assigning different and unique color codes to create a new database that satisfy
user’s requirements. However, the 21 objects are far many in this work for real world
radio propagation and network planning in the European urban cities where only a
few objects (e.g. buildings and cars) dominant the impacts on radio propagation and
network topology. Besides we intend to use the satellite images, where bird’s view
of the scenario is taken, the perspective of those 21-class images are not suitable for
recognizing objects efficiently. As a result, CEIeurope database is constructed for
recognizing the European style urban city and only four object classes are focused.
The visual angle is the bird’s view from the high resolution satellite camera. A
selection of images and their ground truths are shown in Fig. 2.10 all the images
are 800 × 550 pixels with JPEG format and the resolution is the 18th level (among
the 20 levels) on Google Map. The four-object classes are road, building, car and
tree, they are labeled by using the same color code as that in MSRC database.
2.2.3
Image understanding and segmentation algorithm
The meaningful object classes are recognized through a machine learning mechanism,
which consists of a training phase followed by an evaluation phase. In the
training procedure, the Joint-Boost algorithm [82] is employed to compute the weak
learners, which are combined together at the end to compose a strong classifier that
allows multi-object recognition from images. The evaluation procedure recognizes
the objects based on a combination of possibility matrix, which indicates the
37
Chapter 2. 3D environment reconstruction method
(a)
(b)
(c)
Figure 2.9: The MSRC labeled image database. (a) A selection of images in the
21-class database. (b) The ground truth annotations corresponding to (a),(c) The
color codes for the 21 classes.
possibility of how each pixel belongs to each object class. The possibility matrix
is provided by the trained strong classifier from training phase and a sub-cluster
dominant class computation algorithm to refine the unprecise pixels without any
human supervision.
38
2.2. 3D outdoor environment reconstruction
(a)
(b)
Figure 2.10: The CEIeurope labeled image database. (a) A selection of images in
the database. (b) The ground truth annotations corresponding to (a).
2.2.3.1
The training phase
All the images, including the training images and the test images, are automatically
converted from RGB color space to Lab color space at the very beginning. Lab color
space with dimension L for lightness and a and b for the color-opponent dimensions,
based on nonlinearly compressed CIE XYZ color space coordinates, is the only way
to communicate different colors across different devices.
The training images are convolved with a 17-dimensional filter bank (see Fig.
2.11) to extract the features of texture. The filterbank consists of: 3 Gaussians,
4 Laplacians of Gaussians (LoG) and 4 first order derivatives of Gaussians. The
three Gaussian kernels (with σ = 1, 2, 4) are applied to each L, a, b channel, thus
producing 9 filter responses. The four LoGs (with σ = 1, 2, 4, 8) are applied to the
L channel only, thus producing 4 filter responses. The four derivatives of Gaussians
are divided into the two x-and y-aligned sets, each with two different values of
σ (σ = 2, 4). Derivatives of Gaussians are also applied to the L channel only, thus
producing 4 final filter responses. Therefore, each pixel in each image has associated
to a 17-dimensional feature vector. The choice of filter-bank is somewhat arbitrary,
as long as it is sufficiently representative. This filter-bank was determined to have
full rank in a singular-value decomposition (see [67]), and therefore there are no
redundant elements.
39
Chapter 2. 3D environment reconstruction method
(b)
(a)
(c)
Figure 2.11: The 17D filter bank. (a) The 3 Gaussians are applied to L,a,b channels.
(b) 4 LoGs are applied to L channel. (c) 4 derivatives of Gaussians are for L channel.
50
40
Kd-tree
clustering
30
20
Filter bank
10
Input image
Texton Map
0
Figure 2.12: The process of image textonization.
The 17D responses for all training pixels are then whitened (to give zero
mean and unit covariance), and an unsupervised clustering is performed.
The
Euclidean-distance K-means clustering algorithm is employed, which can be made
dramatically faster by using the techniques of [84]. Finally, each pixel in each image
is assigned to the nearest cluster center, producing the texton map. The procedure
of image textonization is shown in Fig. 2.12. The texton map is denoted as T where
pixel i has value Ti , i ∈ {1, ..., K}.
40
2.2. 3D outdoor environment reconstruction
Afterwards, texture-layout filter is applied to learn the layout of texture and
context information of T . Each texture-layout filter is a pair (r, t) of an image region
r, and a texton index t. Region r is defined in coordinates relative to the pixel i being
classified. For computational efficiency, only rectangular regions are investigated in
our work, though an arbitrary region could be used. A set R of candidate rectangles
are chosen randomly at the beginning of the training procedure to reduce the endless
exploration of the shapes. As illustrated in Fig. 2.13, the feature response of texton
index t at the location i is the proportion of the pixels under the offset region r + i
that have texton index t and it is expresed by (2.2). Outside the image boundary
there is zero contribution to the feature response.
v[r,t] (i) =
1
[Tj = t]
area(r)
(2.2)
j∈(r+i)
We define that C is the set of object classes that is within the focus of application.
c ∈ C represents a member of C. N ⊆ C represent a sub-set of the classes.
Joint-Boost algorithm iteratively selects discriminative texture-layout filters v[r,t] (i)
to compute weak learners. Each weak learner hi (c) is a decision stump based on the
response
hi (c) =
⎧
⎨a[v
[r,t] (i)
> θ] + b
⎩k c
if c ∈ N
(2.3)
otherwise
At each iteration m, the subset N is randomly picked from C. For those classes
c ∈ N that share the feature, the weak learner gives hi (c) ∈ {a + b, b} depending
on the comparison of feature response to a threshold θ. For classes not sharing the
feature, the constant k c makes sure that unequal numbers of training examples of
each class do not adversely affect the learning procedure. At each round, a new
weak learner is chosen by minimizing an error function
2
Jwse = Σc Σi wic (zic − hm
i (c))
(2.4)
Where each pixel i is attached with a target value zic {−1, +1} (+1 if i belongs to the
class c, and −1 otherwise), wic is the weight function that specify the classification
accuracy of i after m − 1 rounds of learning.
41
Chapter 2. 3D environment reconstruction method
t1
r1
(a) Feature responses of v1
t2
r2
Texton Map
(b) Feature responses of v2
Figure 2.13: Calculating the feature responses for textons. (a) the feature responses
for v[r1 , t1 ]. (b) The feature responses for v[r2 , t2 ].
Theoretically, all the candidates r in R and t in T should be traversed at each
iteration to find the best v[r,t] that produces the minimum Jwse for all the possible
sharing class sets. However, the traversal is expensive and time consuming as there
are |T | × |R| different combination candidates. To efficiently tackle this problem,
each candidate pair of [r, t] is tested with a probability during an iteration and with
thousands of iterations, all the possibilities are likely to be evaluated.
Given the feature filter v and the threshold. The solutions of a + b, b and k c are
expressed as follows:
c c
w z [v(i, r, t) ≤ θ]
c∈N
i i c i
b=
c∈N
i wi [v(i, r, t) ≤ θ]
c c
w z [v(i, r, t) > θ]
a + b = c∈N i i c i
c∈N
i wi [v(i, r, t) > θ]
42
(2.5)
(2.6)
2.2. 3D outdoor environment reconstruction
c c
w z
k = i i c i
i wi
c
(2.7)
We assume wic = 1 at the very beginning, and after a new weak learner is selected
at round m, it is updated as
wic := wic e−zi hi
c m (c)
(2.8)
Thus at the end wic = e−zi Hi (c) and all the weak learners are combined into a strong
c
classifier of the form:
H(c, i) =
M
hm
i (c)
(2.9)
m=1
The texture-layout filters together with their thresholds and sharing classes
compose the weak learners provided from the training phase, they are eventually
stored in a training database for further evaluation.
2.2.3.2
The evaluation phase
At the evaluation step, all the test images are converted into Lab color format and
convolved with the 17D filter bank, and the responses are clustered to generate
texton maps as the training procedure. The training database generated in the
previous step are applied to the texton maps of all the test images. Hx (c, i) is
computed on each pixel i in a test image x and the estimated confidence value can
be reinterpreted as a probability distribution using soft max transformation [85] to
give the texture layout potentials:
Px (c, i) ∝ exp Hx (c, i)
(2.10)
Note that for each image a probability matrix Px is generated with three
dimensions Hx × Wx × |C|, with H and W as the height and width of x, |C| is
the number of classes in C. The temporary recognition result of each pixel i ∈ x,
up to now, is the class with the maximum potential value.
Ct (i, x) = arg max Px (c, i)
c
43
(2.11)
Chapter 2. 3D environment reconstruction method
An example of the result is demonstrated in Fig. 2.14(b). Although most of the
pixels are classified correctly and different objects are recognized, the precision of
the edges between adjacent objects are not sufficient. Shotton et al. in [82] proposed
a supervised method by manually indicating the misclassified parts, thereafter the
algorithm adjusts those miss-classified parts into the right classes. Note that when
there are a batch of images to be adjusted, the efforts of manually indication for each
misclassified segments will be considerable and time consumption will be very high.
Therefore, we proposed an automatic adjustment method to improve the results
without any human interaction.
(a) Input image
(b)preliminary recognition
and segmentation
(c) Edge refined by
Graph-Cut algorithm
(d) Improved result by
subdividing the clusters
Figure 2.14: Multi-object recognition procedure.
2.2.4
Performance enhancement
2.2.4.1
Use of color information for refining edges
Color cues are frequently used in image processing area for edge detection, as the
color gradient variation at the border of adjacent objects are obvious, and that is
also how human eyes distinguish the border of objects. In this work we propose to
utilize the property of color variation information to improve the previous result Ct
efficiently.
First of all, the image is smoothly clustered into K clusters based on the RGB
color distribution. Instead of using normal k-mean cluster method, a graph cut
algorithm [86, 87] is employed to smoothly cut the edges of different clusters.
Because our work requires the labels vary smoothly almost everywhere while
preserving sharp discontinuities that may exist at object boundaries. Fig. 2.15
compares the results obtained from traditional k-mean cluster method to the
Graph-cut algorithm, in both of the results, each pixel is assigned to a cluster and
44
2.2. 3D outdoor environment reconstruction
(a) Input images
(b) k-mean cluster
(c) Graph cut algorithm
Figure 2.15: Comparison clustering results between k-mean cluster and graphcut
algorithm. (a) A selection of the input images from MSRC database. (b) The
clustering result obtained by k-mean cluster.(c) The clustering result obtained by
graphcut algorithm.
labeled with different color code. The cluster boundaries of the graph-cut contain
less noise and are more desired by this work.
Basically the selection of the number of clusters K is arbitrary: the larger the
value is, the more details there will be in the clustering results. As our purpose is
to distinguish the non-trivial color information while maintain a certain tolerance
of variation to avoid the over clustering issue, the proper range of K should be
within 5 to 8 depending on the complexity of the target area. We denote Cdm (k) as
45
Chapter 2. 3D environment reconstruction method
the dominant class of cluster k, and it is calculated by selecting the class with the
maximum summation of likelihood for each cluster (see (2.12) and (2.13)),
Px (cj |k) =
1
Px (cj , i)
area(k)
(2.12)
i∈k
Cdm (k) = arg max Px (cj |k)
c
(2.13)
Px (cj |k) is the likelihood that the pixels in cluster k belong to class cj , and
Px (cj , i) is the likelihood matrix of image x calculated by (2.10). Cdm (k) is the
dominant class decision for cluster k. Therefore each object, constraint by color
clusters and class likelyhood matrix, has much clearer boundary than the result
when only Ct is used.
The example results (Fig. 2.14(c)) visually indicate that the boundaries of
adjacent objects are clearer than in Fig. 2.14(b) while the recognition accuracy
is reduced.
Due to the color similarity among different classes, for instances the color of sky
and that of the windshields of cars are similar, parts of buildings and roads have
similar colors, some pixels not in the same classes are labeled with same clusters.
As a result, the dominant class method makes the adjustment inappropriate for the
dominated members.
To tackle this problem, we further divide each cluster into various numbers of
subclusters based on their connectivity property (see Fig. 2.16). The likelihood
matrix Px (cj |ks ) is calculated for each subcluster ks by using (2.12). Then Px (cj |ks )
is sort in descent order, and the class with the max potential value is the dominant
class Cdm (ks ) of ks .
An exception occurs when the class with the second max potential is also the
dominant class of the parent cluster Cdm (k) with ks ∈ k, and if Px (Cdm (k)|ks ) ≥
μPx (Cdm (ks )|ks ), the class of ks is set to Cdm (k) rather than Cdm (ks ), otherwise
C(ks ) = Cdm (ks ). The recognition result after this step is shown in Fig. 2.14(d)
and more results are shown in Fig. 2.17.
46
2.2. 3D outdoor environment reconstruction
k
k1
(a) input image
k
k
k2
(c) A selected cluster k
(d) sub cluster map ks of k
(b) graph-cut clustering
Figure 2.16: Example of sub-clustering based on connectivity property.
2.2.4.2
Image understanding results and comparisons
We perform experiments on a subset of the 21-object MSRC database, and only focus
on 6 out of the 21 classes: building, grass, tree, sky, car and road, which might be
the main objects that affect outdoor radio propagations. The experimental results
Building
Grass
Tree
Sky
Car
Road
True class
Building
Grass
Tree
Sky
Car
Road
Inferred
class
are given in Table. 2.1 in a format of confusion matrix.
75.7
2.4
16.8
10.0
12.8
7.1
0.1
44.3
0.6
0
0
0
8.1
35.0
71.6
5.7
6.0
1.5
4.9
0
8.9
84.3
0.4
0
9.5
1.1
1.8
0
76.0
2.6
1.7
17.2
0.3
0
4.9
88.8
Table 2.1: Confusion matrix. Number of cluster in texton booster is 400, the average
accuracy is 76.1%.
With 1000 rounds of boosting 45% of training images in the database, the number
of texton clusters is set to be 400 to achieve the best average accuracy of 76.1%
whereas the Joint-Boost algorithm gives an overall accuracy of 69.2%. Shotton et
al. [82] then manually moved the misclassified parts to the right classes and the
47
Chapter 2. 3D environment reconstruction method
improved result has an accuracy of 72.2%. We notice that the proposed method
by our work outperforms the Joint-Boost algorithm and the edges of adjacent
objects are much more accurate and clearer.
Moreover, the proposed method
has the capability to adjust the misclassified pixels to the correct classes: For
instance in the second image of Fig. 2.17, some pixels belonging to the building
are recognized as car by Joint-Boost algorithm. The proposed method is able to
change the decision and move the pixels to the correct classes, and some pixels are
moved to the building class and some are moved to tree class. Hence the proposed
multi-object recognition method is fully automatic and more accurate compared with
the Joint-Boost method, and it can be used to decide the location and the material of
objects. It provides the opportunity to automatically assign the attenuate coefficient
for each object, which will be useful in the following work to increase the efficiency
of radio propagation simulation.
2.2.4.3
Eliminating shadow effect
The available satellite images are taken for different places at different time, and the
solar incident angles are abundantly different. If an object is partially in the shadow
of another object, the discontinuity of texture variation occurs. The shadow effect
has undesired impacts on the image understanding results. Therefore it is important
to detect and eliminate shadow effect.
We employ a scan scheme to scan for each row and column the line segments
that fall inside the shadow region rs , the detection is based on color information.
Although this scheme is arbitrary, it works well for urban city environment where
hardly exists surface with large black color. Before detecting the shadows, a test
image is converted from RGB space to Gray scale space. Each column and row are
scanned separately, and Fig. 2.18 shows the principle of a column scan and row
scan.
The pixels of a column/row are filtered by a 1 pixel × 5 pixel window to realize
zero-phase filtering on the high frequency variations, thus the filtered result (red)
is more sensitive to the significant variations. Thereafter, the line segments, which
begin with a decline trend below the threshold value and end with a climbing trend
above the threshold, are considered belonging to rs . The scanning and detection
procedure is repeated until the entire image is checked. As the histogram varies
48
2.2. 3D outdoor environment reconstruction
Input Images
Results of TextonBoost
Improved Result
Figure 2.17: The comparison of results between the proposed algorithm and [82] in
a selection of images.
49
Chapter 2. 3D environment reconstruction method
Column Scan
Row Scan
(a)
original variation curve
filtered curve
start point
end point
300
250
200
150
Threshold value
100
50
0
0
100
200
300
400
500
600
(b)
Figure 2.18: The principle of scanning shadow. (a) Row scan and column scan of
the shadowed line segments. (b) Filtered curves are calculated and compared with
the threshold. The starting point and end point of each line segment are determined
for each shadowed region.
50
2.2. 3D outdoor environment reconstruction
(a)
(b)
Figure 2.19: The result of shadow detection. (a) There exist small holes after the
column and row scanning. (b) The final result by filling the holes to reduce noises
in the detection.
with different images, there is no fix threshold value that can be pre-determined for
all the satellite images, hence the value of ϕ should be determined for each image
before the shadow detection. In our work, the mean value of the entire gray-scale
image is considered as the value of threshold ϕ in the plot.
The selected line segments compose grid-style shape with lots of small ”holes”,
which can be seen from Fig.
morphological operation [88].
2.19(a).
We eliminate those noises through
Besides depending on the order of provided by
the potential matrix in (2.12), the regions belong to shadow area have very high
possibility to be road class. Assuming that rs is the connected region of the shadow
area. If Cdm (rs ) = road, while the second most possible class is road, the pixels in
rs are assigned to road class.
2.2.4.4
Road detection and regularization
Road detection algorithm is used to enhance the performance of the recognition
algorithm for outdoor environment.
city (i.e.
If the input image is recognized as urban
through detecting the proportion of buildings, cars), road detection
51
Chapter 2. 3D environment reconstruction method
(a)
(b)
Figure 2.20: Road segmentation and orientation estimation.(a) The original test
image. (b) The classification result after applying the color information on
segmenting and eliminate the shadow. The pixels of road class are extracted and
highlighted with white color, the background is in black color, and the orientation
vector is estimated for each pixel that belongs to road class.
algorithm can be used to regulate the street network of the city and reduce unrealistic
recognition result and improve the accuracy.
Once provided image understanding result from the previous step, the pixels
belong to road class are segmented from other objects with binary value (see Fig.
2.20(b) as an example). The orientation vector is evaluated through R. Wilson’s
orientation estimation method [89]. We assume that all the pixels belong to the
same road are likely to have similar direction vector with smooth change, based on
which, the direction map is grouped into 8 clusters (Fig. 2.21) by using the Euclidean
k-mean cluster algorithm, thus the 360 degree space is divided into 8 sections and
each with a range of 45 degree. Each cluster is then segmented separately and
regions are labeled based on the connectivity as the sub clustering method in the
previous section.
The orientation θe of each connected region is computed by the orientation of the
smallest bounding ellipse (SBE), which is the angle between the x-axis and the major
52
2.2. 3D outdoor environment reconstruction
axis of the ellipse, see Fig. 2.22(a) as an example. θe is then compared with the
main direction of this region θm , as indicated in Fig. 2.22(b). If θd = |θe − θm | > θT ,
the centroid orientation Oc (ri ) of this region ri is defined as θm to maintain the
continuity with its neighbor clusters, otherwise Oc (ri ) = θe .
8
50
7
100
6
150
200
5
250
4
300
3
350
2
400
1
450
500
100
200
300
400
0
500
Figure 2.21: The orientations are clustered into 8 groups
Region expansion is realized to optimize the traffic network, and regularize the
width, direction and connect the isolated road segment. The centroid location Cri is
−−−−→
−−−→ , given the
calculated for each region. Direction of Cri Crj is represented as O−
C C
ri
rj
−−−→ − Oc (ri )| and |O−−−−→ − Oc (rj )|
centroid orientation Oc (ri ) and Oc (rj ), if |O−
C C
C C
ri
rj
ri
rj
are less than an angle threshold ranging from 5 to 10 degree, and the connection
between the two regions does not have important effect to the recognized buildings,
the two regions are supposed to be connected. This assumption is useful when only
a small proportion of pixels belong to the road class are misclassified, and they, by
mistake, disconnect ri and rj that are assumed to be in the same road segment.
53
Chapter 2. 3D environment reconstruction method
Tm
TT
Te
(a)
(b)
Figure 2.22: A toy example of the comparison between θe of the SBE and the main
orientation θm . (a) The SBE of a region where the orientations belong to the same
direction cluster. (b) Computation of the difference between θe and θm .
Two regions are expanded and connected by rectangles once the aforementioned
constraints are fulfilled. The rectangle is expressed by
r = [O, C, W, L]
−−−→ is the orientation of the rectangle, C is the central of the
where O = O−
C C
ri
rj
rectangle, W is the width that equals the median distance from the boundary to the
skeleton and L is the length which equals the distance between furthest endpoints
of the two regions. The pixels locate within the rectangle are assigned to road class.
The pseudo code for the aforementioned road detection and expansion algorithm
is:
Listing 2.1: Road detection and region expansion
Segment road class from image x;
compute the orientation map Mo ;
k-means cluster with 8 sections(45◦ );
for each connected region ri
{
compute Oc (ri )
}
54
2.2. 3D outdoor environment reconstruction
for each region pair (ri , rj ) and i = j
{
if (orientation is similar)
{
WR = roadwidth(ri , rj )
HR = roadlength(ri , rj )
−−−−
→
OR = O−
C C
ri
rj
−−−−→
CR = median(Cri Crj )
rectangle: R(CR , OR , WR , HR )
if (Σi∈R [C(i) ∈
/ road] < 0.5Area(R))
∀i ∈ R, C(i) = road
}
}
Finally, the result of the road detection algorithm is shown in Fig.
2.23.
Compared with the result without any enhancement, the accuracy is significantly
improved after applying the color information and road detection algorithm.
(a)
(b)
(c)
Figure 2.23: The result of road detection. (a) The test image. (b) The result without
any enhancement. (c) The result of road detection.
2.2.5
Shape matching and vectorization
Real world objects can be expressed by geometric primitives in the computer graphic
area and CAD systems.
In constructive solid geometry, primitives are simple
geometric shapes such as a cube, cylinder, sphere, cone, pyramid, torus for 3D
applications. Whereas a polygon is traditionally a plane figure that is bounded by a
55
Chapter 2. 3D environment reconstruction method
closed path, composed of a finite sequence of straight line segments (i.e. by a closed
polygonal chain), which is useful in 2D applications.
The objects recognized by our image understanding algorithms, are to be
described as 3D geometric primitives as well. By means of recording the closed vertex
path for each object, the digital environment database is constructed. Even though
the boundaries of recognized objects can be directly used to compose polygons, they
contain far more number of pixels than that is actually needed, and the information
stored in database will be highly redundant by doing so. A logical and natural
way to tackle this problem, is to extract the feature points from the boundaries of
segmented objects. As the proposed image understanding algorithm is not able to
obtain 100% accuracy, there are still errors and coarse edges at the boundaries which
make the detection of feature points a big challenge for some very simple primitives.
Further more, in urban cities and indoor environments, an overwhelming number
of objects have regular planar shapes such as quadrilateral, triangle, circle that can
be described conveniently with only a few vertexes. Therefore we develop a shape
matching algorithm to detect the shape for all the recognized objects and register
them with the corresponding corners by rotation and deformation.
In this section, the methodology for shape matching and registration to vectorize
the recognized objects and compact database is introduced. At the end, the 3D map
is written to KML format log file to be demonstrated visually on software (e.g. on
Google Earth) and be accessed by other people.
The façades of buildings in European urban cities are often seen with the shapes
shown in Fig. 2.24, the set of such shapes is named as S in this work.
Basically, shape-based image retrieval consists of measuring the similarity
between shapes represented by their features. Some simple geometric features can be
used to describe shapes. Usually, the simple geometric features can only discriminate
shapes with large differences. Therefore they are usually used as filters to eliminate
false hits or combined with other descriptors and they are not suitable to be used
as stand-alone shape descriptors. A shape can be described from different aspects
including: Rectangularity, Solidity, Circularity ratio and Zernike moments (ZM).
56
2.2. 3D outdoor environment reconstruction
Figure 2.24: Frequently seen façade shapes of buildings. From top to bottom and
from left to right are: circular, triangular, rectangular, H, L, Cross and G shape
2.2.5.1
Rectangularity
Rectangularity represents how rectangular a shape is, i.e. How much it fills its
minimum bounding rectangle:
Rectangularity = AS /AR
(2.14)
where AS is the area of a shape; AR is the area of the minimum bounding rectangle
box.
2.2.5.2
Solidity
Solidity describes the extent to which the shape is convex or concave and it is defined
by
Solidity = AS /H
(2.15)
where H is the convex hull area of the shape. The solidity of a convex shape is
always 1.
57
Chapter 2. 3D environment reconstruction method
2.2.5.3
Circularity ratio
Circularity ratio represents how a shape is similar to a circle. It is the ratio of the
area of a shape to the area of a circle having the same perimeter:
(2.16)
Circularity = AS /AC
where AC is the area of the circle having the same perimeter as the shape. Assume
the perimeter of the region is P, AC = P2 /4π. As 4π is a constant, the Circularity
can be rewritten as
Circularity ∝ AS /P2
2.2.5.4
Zernike moments
Zernike Moments (ZM) are orthogonal moments [45]. The complex Zernike moments
are derived from orthogonal Zernike polynomials:
Vnm (x, y) = Vnm (r cos θ, sin θ) = Rnm (r)exp(jmθ)
where Rnm (r) is the orthogonal radial polynomial:
(n−|m|)/2
Rnm (r) =
s=0
(−1)s
(n − s)!
s! ×
( n−2s+|m|
)!( n−2s−|m|
)!
2
2
rn−2s
Zernike polynomials are a complete set of complex valued functions orthogonal over
the unit disk. The Zernike moment of order n with repetition m of shape region is
given by:
Znm =
n + 1 rs (r cos θ, r sin θ) · Rnm (r) · exp (jmθ) r ≤ 1
π
r
(2.17)
θ
2.2.5.5
Shape matching and registration
As mentioned before, there is no a single shape descriptor that is able to tell so
many shape types, as the tuning of shape parameters is a challenging problem. In
this work, an hierarchical matching heuristic is employed by swapping the objects
into the proper branch at each level to hierarchically detailing the shape description.
58
2.2. 3D outdoor environment reconstruction
As a matter of course, different discrimination metrics are applied at different levels
and the state flow is shown in Fig. 2.25.
Dr
Do
Figure 2.25: Hierarchical shape matching.
At the root of the hierarchical model, the discriminator Dr is in charge of
distinguishing circle, triangle and the rest of shapes in S. Then at the second level,
the 3rd branch for the rest shapes, discriminator Do is trained for the remaining 4
shapes. By using the Rectangularity, Solidity, Circularity ratio and Zernike moments
(ZM) (Eq. (2.14) to Eq. (2.17)) of the regions, the discriminator is trained in Neural
Network to learn the shape features. The training shape set is built by rotating,
transforming and deforming the shapes shown in Fig. 2.24. Each training sample
is black-white image of size 255 × 255, where the shape is in black color and with
variation on orientations, scales. Dr is obtained by learning circles as type Sc ,
triangles as type St and the remaining types as So ; Do is obtained by learning
rectangle as type Sr , H shape as type Sh , cross shape as Scro and the G shape as
SG .
Dr and Do are trained separately by using Bayesian regulation backpropagation
function. The results of the shape matching are listed in table 2.2 and shown in Fig.
2.26.
The accuracy is 100% without adding noise at the boundary of the test shapes,
and 90% when adding the noise for applying to the recognized results where there
are no serious errors. All the typical shapes in S are stored in data structure that
59
Chapter 2. 3D environment reconstruction method
Table 2.2: Shape matching result
XX
XXX
Input
XXX
Cicle
XXX
Output
Circle
Triangle
Others
Rectangles
H
L
Cross
G
Triangle
Rectangles
H
L
Cross
G
contains the features like the centroid, orientation, scale of sides and so on, which
also depend on how those shapes described in geometry.
Once provided with the shape matched result.
The objects are registered
with the corresponding shape features to search the most suitable rotation and
deformation by minimizing the evaluation metric. Assume S is the candidate shape,
AS = i∈x [i ∈ S ] represents the area, and AIS = i∈x [i ∈ S && i ∈ S] represents
the actual area of pixels that belong to S and S.
The metric that evaluates
the candidate shape is expressed by (2.18). The formula sums the ratio between
candidate shape area and actual union region and the ratio between original shape
area and the union region, which indicates how well the candidate shape matches
with the original region.
E(S ) =
AS AS
+
AIS
AIS
(2.18)
The object becomes finding the S b that provides minimum E
S b = arg min E(S )
S
(2.19)
Thereafter, a iterative procedure is imposed on the shape matching and
recognition through Eq. 2.18 and Eq. 2.19.
2.2.5.6
Delineating the buildings
Recalling that the centroid of ri is Cri : {x, y} and the orientation of ri is Oc (ri ). If
ri ∈ Sr , the polygon can be described by four vertexes (A, B, C and D) that are
60
2.2. 3D outdoor environment reconstruction
Cross
Rectangle
L shape
U shape
H shape
Figure 2.26: Successful result of shape matching. Each shape of Do is correctly
matched.
constraint by the following conditions:
C=
⎧
yI −yA
⎪
⎪
⎪
xI −xA = tan θ,
⎪
⎪
⎪
−→
⎪
−→
⎨−
AD = 2AI −−→
−−−→
⎪
⎪
DB = 2DCri
⎪
⎪
⎪
⎪
→
−−−→
⎪
⎩−
AC = 2ACri
yI −yCr
i
xI −xCr
i
= − tan1 θ
(2.20)
−−→
Where I is the median point of AD and θ = Oc (ri ) is the orientation of the shape.
Note that the recognized region r is not perfect and we need to match the shape
61
Chapter 2. 3D environment reconstruction method
with the condition in Eq. 2.20 by adapting θ and side lengths with coefficients:
C =
⎧
yI −yA
⎪
1
yI −yCri
⎪
⎪
xI −xA = tan θ , xI −xCr = − tan θ ⎪
i
⎪
⎪
−→
⎪
−−→
⎨−
AD = αAD
(2.21)
−−→
−−→
⎪
⎪
AB = β AB
⎪
⎪
⎪
⎪
−→
−−→
⎪
⎩−
DC = β DC
Where θ ∈ [θ − φ, θ + φ], normally φ ≤ 45o , and α(or β) ∈ [1 − τ, 1 − τ ] where τ
ranges from 0 to 0.5. Fig. 2.27 shows the procedure of how a rectangle is registered
by rotation and deformation.
Rotation
A
B
100
I’
200
300
Cr
Deformation
C
Rotation
D
400
500
600
100
200
300
400
500
Figure 2.27: An example of registering the rectangle primitives.
62
2.2. 3D outdoor environment reconstruction
The similar heuristic can be applied to H shape, L shape and G shape by finding
the vertexes that satisfy the conditions. Circular buildings are tested by a set of
circles that centered at Cri and radius R ranging from the Bmin to Bmax which
represent the boundary points that have the minimum and maximum distances
with Cri .
2.2.5.7
Writing geometric primitives to KML file
All the vectorized information are written into Keyhole Markup Language (KML)
format, which is an XML notation for expressing geographic annotation and
visualization within Internet-based, two-dimensional maps and three-dimensional
earth browsers. Google Earth was the first program able to view and graphically
edit KML files. Other projects such as Marble have also started to develop KML
support. The advantage of using KML format is that the format is simple and
descriptions are convenient to create for polygons, points and lines (see Listing 2.2
as an example of the script). It provides an open access possibility to the public and
can be demonstrated in many popular free earth browsers as shown in Fig. 2.28.
Listing 2.2: Common used Definitions in KML file
<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://www.opengis.net/kml/2.2">
<Style id="xxxx"><!--Define style of polygons, lines and
points-->
<LineStyle>
</LineStyle>
<PolyStyle>
</PolyStyle>
</Style>
<Placemark>
<Point><!--Draw point-->
<coordinates>longitude, latitude, altitude</coordinates>
</Point>
<Polygon><!--Draw polygon-->
<coordinates>
longitude1, latitude1, altitude1
longitude2, latitude2, altitude2
...
</coordinates>
63
Chapter 2. 3D environment reconstruction method
</Polygon>
</Placemark>
</kml>
Figure 2.28: KML shape description of the reconstructed result demonstrated on
Google Earth.
2.3
Indoor environment reconstruction
Unlike the outdoor environment reconstruction, where the objects locate apart
from each other and the scale is large, the indoor environment reconstruction
is much easier. A method is developed to automatically model the 3D indoor
environment from a scanned 2D map if the digital map is not available from the
administrator. The reconstruction method consists of four steps: image calibration
and classification step which recognizes walls from the scanned map; thinning and
feature points extraction to compress the edge information; edge smoothing and
vectorizing to build the 3D database. The reconstructed 3D scene is demonstrated
to users at the end.
64
2.3. Indoor environment reconstruction
Horizontal
Distortion
Vertical
(a)
(b)
Figure 2.29: Image calibration and segmentation. (a) Scanned map. (b) Calibrated
and segmented result.
2.3.1
Image calibration and classification
Walls in the maps are normally labeled with black lines as shown in Fig. 2.29(a).
Once the map is scanned, the image is converted from RGB color to gray color.
Because the floor plan on paper has some distortion and transformation during
scanning, the image should be calibrated with 2D horizontal-vertical direction to
make sure that the axis of map matches well with real world axis. Thereafter, each
pixel in the image is checked and pixels that belong to walls are recognized by using
a single classifier h(i),
⎧
⎨1 p(i) < v
h(i) =
⎩0 p(i) ≥ v
(2.22)
where p(i) is the color of ith pixel in the image, v ∈ [0, 255] is the color threshold.
If the pixel value is less than v, pixel i is marked by 1 which indicates the wall,
otherwise it is marked with 0 which is the background. Hence, a segmentation
result is obtained after this step, see Fig. 2.29(b).
65
Chapter 2. 3D environment reconstruction method
2.3.2
Thinning and feature points extraction
In 2D vision of indoor scene, a wall can be expressed by an edge E < V1 , V2 > with
V1 and V2 as two endpoints. As can be seen in Fig. 2.30, the extracted pixels from
the previous step contain redundant information for constructing walls. Thus they
are thinned to lines with 1-pixel width based on conventional thinning method and
deliver the skeleton of wall efficiently.
--Extracted pixels
--Thinned line
--Feature point
--non-critical point
Figure 2.30: Thinning step and feature point extraction
However, as can be seen, the thinned lines (skeletons) still contain a large number
of pixels which are not necessary to describe the critical variation of edges and some
pixels are noises with high level fluctuations in a very short distance. Therefore,
those non-critical points should be eliminated and only critical points along the
thinned lines should be maintained. The critical points are also called feature points,
which are either conjunctions shared by edges of different directions or the endpoints
of edges. As a result, they can be used to represent the endpoints of edges. In this
step, Harris-corner algorithm [90] is employed to search critical points throughout
the segmented pixels and the results are demonstrated in Fig. 2.30 and Fig. 2.31.
2.3.3
Smoothing and vectorizing
Critical points are clustered and map is vectorized in this step. Four windows
with different directions are used to cluster feature points that belong to the same
line. Fig. 2.31 shows the shapes of windows and an example of smoothing and
regularization. As can be seen, the green dots are critical points obtained from
previous step. After applying the windows, the locations of points are adjusted
and marked by blue dots. Based on the smoothing algorithm, two points belong
66
2.3. Indoor environment reconstruction
Windows with
different directions
Critical points
Smoothed points
Endpoints for regularizing edges
Figure 2.31: Smoothing and regularization.
to the same cluster and with largest distance are selected as endpoints of an edge.
This step reduces the number of unnecessary planes and the database information
is therefore compressed.
3D scene consists of obstacles which have shape description and material
information that describe how signal strength is attenuated. Different materials have
different attenuation values. The vectorization results are stored in ’.txt’ file, and the
format is shown at the right side of Fig. 2.32. Vertices in 3D format are expressed
as Vi = [xi , heighti , yi ]. In this work, all the walls are assumed to be quadrangle and
have 4 vertices as indicated in Fig. 2.33. However, all the edges are discovered in 2D
vision and only V1 = [x1 , 0, y1 ] and V2 = [x2 , 0, y2 ] are known. Therefore, once the
height of wall is known, V3 = [x1 , height, y1 ] and V4 = [x2 , height, y2 ] are generated
by assigning value to height. The structure of wall can be expressed by
plane =
V1
V3
V 2 V4
material
where material is the index of material such as glass, wood, brick, concrete and so
on.
67
Chapter 2. 3D environment reconstruction method
Mismatched edges
NUMBER OF PLANES 88
0
0
16.7 1
0
3
16.7
57.7 3
16.7
57.7 0
16.7
27.8
27.8
30.9
30.9
…
0
3
3
0
…
12.8
12.8
12.8
12.8
…
1
Figure 2.32: Vectorization result.
Figure 2.33: A wall is described by four vertexes.
2.3.4
Demonstration and analysis
The proposed 3D indoor reconstruction method is programmed in MATLAB, and
run on a PC equipped with Intel i5-760 CPU of 2.8 GHz frequency. Fig. 2.32
compares the computed edges with the map. Only two edges have relative big
errors (0.35 m) and one wall is missed classified, the rest edges match well with
the map with an average error of 0.13 m. In this example, 86 edges are extracted.
As the heights of walls are identically 3 m, they are included automatically to the
result. Besides, after the endpoints are fetched, the horizontal range (X − range)
and vertical range (Y − range) of target space are also obtained, therefore floor
and ceiling are added automatically to the vectorization result and 88 planes are
constructed at the end.
The 3D views of the reconstructed indoor environment are demonstrated in Fig.
2.34. The size of the target space is 57.7 m×3 m×16.7 m. The time for constructing
68
2.4. Conclusion
Figure 2.34: Reconstructed 3D indoor map in different views.
this indoor map is 5.8 s, compared with conventional method which took almost 1
day per person by manually typing 88 × 4 endpoints.
Fig. 2.35 is another example of 3D indoor reconstruction by randomly searching
floor plan on Internet.
page [91].
We select the East Lansing map provided on the web
The size of the region is 77.4 m × 3 m × 36.6 m, the proposed
indoor reconstruction method took place on the downloaded map, it took 23.5 s
to reconstruct 156 planes on the floor plan. The recognized edges match well with
the original image (see Fig. 2.35(b)) and the 3D reconstruction is viewed in Fig.
2.35(c). As a result the proposed method significantly reduces time and human
efforts on modeling different indoor environments.
2.4
Conclusion
3D environment modelling is a difficult issue for radio engineers and network
designers who do not have expert knowledge on this topic.
In conventional
approaches of constructing a 3D environment database, either a large amount of
money should be spent to buy the digital vector map from professional companies,
or time and human efforts are needed to manually measure and reconstruct the
real campaigns. The expenses over this issue constraint the researchers and private
users on validating their proposals or models, and they can be analyzed on a limited
number of scenarios that are freely accessed by public such as the Munich scenario
measured by COST 231 group [92]. To the best of our knowledge, this is the first time
69
Chapter 2. 3D environment reconstruction method
(a) Original map of East Lansing
(b) Vectorizing
(c) 3D view of the reconstructed result
Figure 2.35: (a) A toy example by using the map downloaded from [91]. (b) The
vectorization result and the 3D view of reconstruction (c).
70
2.4. Conclusion
that the advantages of image understanding algorithm is applied to automatically
reconstruct 3D outdoor and indoor scenarios for signal propagation and network
planning purpose. The principle of reconstruction method is introduced in this
chapter and the proposed algorithms are detailed as well. The experimental results
on outdoor reconstruction indicate that the algorithm is able to accurately recognize
different objects from the satellite images of the target regions, it offers a flexibility
to reconstruct different types of environment by training different environment
database. The indoor reconstruction algorithm is proposed to reconstruct the indoor
database from scanned floor plan, the efficiency and accuracy is satisfied without
any human interaction. Thereby, the 3D environment reconstruction methodology
proposed in this work significantly reduces human efforts, cost and time spent on
reconstructing a 3D geographic database.
71
Chapter 3
Ray-tracing engine and radio
propagation modelling
There are numerous algorithms and methods that are developed to optimize the
deployment, routing protocols, power consumption plans and etc. for WSNs. Before
real implementation, those algorithms are evaluated through simulations which
generally take place in network simulators such as NS-2, TOSSIM, EmStar or
OMNeT++. Kamarudin et al. [93] review a variety of realistic propagation models
for WSNs and discuss the modeling of vegetation propagation model in OMNeT++
simulation platform. They prove that propagation model has strong impact on the
evaluation of network performance. However, the aforementioned simulators employ
propagation models as simple as Free-space model and Log-normal model, which are
too optimistic and independent of environment. The evaluations of those protocols
and algorithms based on those empirical propagation models are not rigorous, which
will lead to inappropriate algorithm design. Therefore accurate radio frequency (RF)
propagation modelling and simulation are very important at the pre-deployment
phase of WSN for predicting the design performance.
There are many RF propagation simulation works focused on mobile
communication. RF measurements have been made in several cities such as Munich
scenario [92] and Ilmenau scenario [94]. Based on those experimental results, other
works are done to model the radio propagation, tune critical parameters and evaluate
coverage performance of base stations. Furthermore, signal attenuation parameters
are also tested when signal penetrates through obstacles of different materials and
sizes, such as different thickness of bricks, concrete, metal, etc.
Most of the miniaturized wireless sensor devices implement ZigBee or 802.15.4
communication protocols, while there are only a few research works focused on
modeling RF propagation for ZigBee WSNs, and even fewer work on measuring
Chapter 3. Ray-tracing engine and radio propagation modelling
ZigBee radio propagation in reality. For this reason, recently more researchers
focus on modeling radio propagation for WSN and several experiments are made to
characterize ZigBee propagation features in both indoor and outdoor environments
[95], [96].
Korkalainen et al.
[38] discuss advanced mobile signal tools that
might be suitable for estimating 2.4 GHz ZigBee protocol. In order to ensure the
results accurate and the simulation efficient, the tools should have features like
3D calculation, accurate environment modeling and radio modeling. In [97], the
authors characterized wireless channels for indoor propagation at 2.4 GHz, but only
direct path is considered. They conclude that free space propagation is unreliable,
log-normal model is not completely matched with the trend of curvature of real
measurements and the multi-wall-floor model is the most reliable and accurate
among the discussed models. However, only considering the direct path is unilateral,
as diffractions and reflections are also vital.
Generally speaking, ray tracing algorithm has very high accuracy [98, 99, 100]
compared with the empirical propagation models. It is obstacle sensitive which
is able to trace multi-path effects in real world signal propagation. While the
computation load is also very high as the method tests every intersection along
the ray path. Especially when scenario becomes large, traditional method might
take hours or days to finish simulation. Therefore many algorithms are developed
to overcome the aforementioned drawback by slightly reducing accuracy as the
compensation.
Beam tracing algorithm [101] extends ray tracing algorithm to reduce intersection
tests, as well as overcome sampling problems. Dominant path tracing algorithm [102]
is developed to avoid redundant calculation, because the authors believe that 98%
of received power is contributed by only a few radio rays. Ray tube tree method
[103] increases the preprocessing speed in constructing trees for ray-tracing.
A 2.5D outdoor ray launching tool is presented in [104]. The tool is very fast but
the resolution is low (7 m) and the computation load is decreased by reducing 3D
rays to 3D Line of sight (LOS), 2D Horizontal diffraction and reflection (HDV) and
modified 2D Vertical diffraction (VD), therefore the accuracy of simulation result
is constraint by the reduced resolution and the types of rays. A 3D ray-optical
approach is presented in [105].
The calculation is in 3D, however in order to
accelerate the calculation, the method preprocesses the environment by dividing
74
3.1. Space division
the obstacles into tiles and the edges into segments, and ray paths are limited to
only search over the tiles and segments.
As a result the time consumptions of existing ray tracing methods are decreased
by either reducing dimension of rays or reducing the details of the environment,
the simulations are efficient while inaccurate. Therefore, in order to make the RF
simulation method advanced, efficient efficient 3D ray tracing and accurate radio
modelling should be developed and integrated. In this chapter, a 3D ray tracing
engine is developed based on space division and polar sweeping is developed to
efficiently search rays and the modelling of radio propagation is proposed to consider
the obstacles and their order along the signal path. Our work is benefited from this
part in terms of:
1. High accuracy in radio estimation for both indoor and outdoor environment.
2. High efficiency which allows the planning algorithm employing such technique
to practically model the radio connectivity and sensing coverage with
acceptable computational speed.
3. Generality. It can be used for the automatically reconstructed environment
database from previous chapter as well as other GIS databases provided by
designers.
3.1
Space division
Before tracing rays, a 3D environment model, either from the aforementioned
automatic reconstruction method or from other modelling methods, is loaded to
the ray tracing engine, and the target space is split by using kd-tree algorithm.
The target space is divided into small cubes with different volumes to balance the
number of obstacles among the cubes. Vlastimil Havran did an extensive study of
available spatial subdivision schemes (including regular grids, nested grids, octrees
and kd-trees) and concluded in his thesis [106] that the kd-tree beats the other
schemes in most cases. And Ingo Wald claimed that it is possible to limit the
number of ray-triangle intersections to three or less using a well-constructed kd-tree
[107].
75
Chapter 3. Ray-tracing engine and radio propagation modelling
A kd-tree is an axis-aligned Binary Space Partitioning (BSP) tree. Space is
partitioned by splitting it in two halves, and the two halves are processed recursively
until no half-space contains more than a preset number of primitives. While kd-trees
may look like octrees at first, they actually are quite different: An octree always
splits space in eight equal blocks, while the kd-tree splits space in two halves at a
time. The most important difference though is that the position of the splitting
plane is not fixed. In fact, positioning it well is what differentiates a good kd-tree
from a bad one. Consider the images in Fig. 3.1:
(a)
(b)
(c)
Figure 3.1: Space division by kd-tree. (a) A scene with some primitives. (b) A bad
example of kd-tree division. (c) A well constructed kd-tree.
Fig. 3.1(a) shows a scene with some objects in it. In Fig. 3.1(b), this scene is
subdivided; the first split is the vertical line, then both sides are split again by the
horizontal lines. The split plane position is chosen in such a way that the number of
primitives on both sides of the split plane should be roughly the same. Although this
may sound like a good idea at the first glance, it actually isn’t: Imagine that a ray
traverses through this subdivided scene but it never has chance to pass through an
empty voxel. If we keep adding planes to this kd-tree following the same rule as Fig.
3.1(b), the result will end up with a tree that does not contain a single empty node,
which is the worst possible situation, as the ray tracer has to check all primitives
in each voxel it travels through. The subdivision in Fig. 3.1(c) is well constructed:
A different heuristic by Jacco Bikker [108] is employed to determine the position of
the split plane. The algorithm tries to isolate geometry from empty space, so that
76
3.2. Polar sweeping
rays can travel freely without expensive intersection tests and therefore our space
division mechanism employs the kd-tree algorithm proposed in [108].
3.2
Polar sweeping
At the beginning of ray tracing, the target environment is polar swept. Conventional
polar sweep method is well known to solve geometric problems, by sweeping a line
across the plane and halting at points where the line makes the intersection with
any object within the target region. As depicted in Fig. 3.2, the solution is partially
computed at those intersection points, so that at the end of sweep, a final intersection
result is available.
Polar sweep
Figure 3.2: Conventional Polar sweep.
In this work, conventional polar sweep algorithm is modified by bending the
direction of the line whenever intersection occurs. A 3D line is rotated clock wisely
and bottom up centering at the transmitter (TX) to discover for each direction the
first intersected point and its corresponding plane. The rule of reflection is then
applied to the line, which bends original direction of the line. Intersected planes
are recorded whenever the direction changes at halting points. This procedure is
repeated for each candidate direction, and each 3D line terminates shooting after
77
Chapter 3. Ray-tracing engine and radio propagation modelling
Brick wall
Glass wall
D4
w7
w6
D1 ray1
w5
TX
w1
ray2
D2 ray3
w2
w4
D3
w3
Figure 3.3: Polar sweep.
a maximum number of intersections is reached or when the boundary is touched.
Therefore, by sweeping the entire scene, all the possible orders of reflection planes
and diffraction cones are discovered. The order of reflected plane is stored as a
matrix with dimension of N ∗ depth, where N is number of possible reflection paths,
depth is the maximum depth of reflections predefined in the ray tracing engine. For
instance, the depth = 4 in Fig. 3.3 indicates no more than 4 reflections in a ray
path, and the reflected plane is expressed as Ref plane. When visible plane exists
and the length is less than depth, the ID of intersected primitive plane is recorded
in a order along the signal path, such as w1 → w2 → w5 → w6. When the number
of planes in a path is less than depth, N U LL is assigned to the remaining elements:
⎡
w7 N U LL N U LL N U LL
⎢
⎢w1
⎢
Ref plane = ⎢
⎢w5
⎣
..
.
w2
w3
..
.
⎤
⎥
w6 ⎥
⎥
⎥
N U LL N U LL⎥
⎦
..
..
.
.
w5
(3.1)
Diffraction happens at the convex edges, the diffraction points are extracted
based on the resolution of z direction and Dif cone is a structure used to store all
the information of a diffraction cone.
structure Dif_cone{
point;
78
3.2. Polar sweeping
visible_plane;
invisible_plane;
}
Where
Dif cone.point
indicates
the
location
of
the
diffraction
Dif cone.visible plane is the ID of visible plane it incidents with,
point,
and
Dif cone.invisible plane is the invisible plane connected with the visible plane, if
there does not exist such a plane, Dif cone.invisible plane = N U LL.
In ray tracing method, there exist many ways to search reflection paths. In this
ray tracing engine, ray launching and ray tracing method are combined in order
to be efficient. Ray launching is executed in the polar sweep step. Ray tracing
happens when knowing the location of receiver (RX). After polar sweeping, the
rays are traced for each RX, direct path, reflection paths and diffraction paths are
selected and the received power strengths (RSS) are calculated separately according
to models given in the following subsections.
3.2.1
Direct path
A direct ray is launched from TX to RX, and each intersection is recorded when
the ray penetrates through objects. The radio propagation model (3.2) which is
developed by us in [109], is used to estimate power loss of direct path between TX
and RX.
Lp = 10 n log10 d + Lobstacle
(3.2)
Where n is path loss coefficient ranging from 2 to 5 and the value is dependent on
the propagation environment, i.e. in free space n = 2, in others such as urban or
rural environments 2 < n < 5. d is the direct 3D distance between TX and RX.
Lobstacle is the power loss due to obstacles encountered along signal path.
Lobstacle =
N
l(i)α(i)(i−1)
(3.3)
i=1
It is obtained by accumulating power reduction of each obstacle along the signal
path. l(i) is attenuation parameter of the ith obstacle, α(i) ranges from 0 to 1,
which is penetration rate of the material of ith obstacle. α(i)(i−1) decreases when i
79
Chapter 3. Ray-tracing engine and radio propagation modelling
Brick wall
Glass wall
RX2
TX
RX1
Figure 3.4: Direct path.
increases, meaning that the first object, with which the signal intersects, produces
the most significant power loss.
Fig. 3.4 gives an example of calculating (3.2) for direct path. There are 2 RXs:
RX1 and RX2 , and one transmitter T X. Signal penetrates through two glass walls
before reaching RX1 , whereas another signal path penetrates through one brick wall
to reach RX2 . For RX1 , l(1) = 2 dB, l(2) = 2 dB, assuming α = 0.9 as a constant
value, we have Lobstacle = 2 + 2 × 0.9 = 3.8 dB. For RX2 , l(1) = 10 dB, we have
Lobstacle = 10 dB. Eq. 3.2 indicates that not only distance, but also the materials
of obstacles and their orders have impacts on received signal strength.
In the ray tracing engine, the electric fields of radio wave are computed instead
of calculating the path loss in dB, as the phase of propagation wave significantly
affects the final result, thus resulting:
Edir (d) =
d
√
n
N
Et
i=1 Lobstacle(E) (i)
e−j2πkd
Where Et is the transmitted electric field at the sender.
(3.4)
Lobstacle(E) (i) is
converted from Lobstacle (i), k is the wave length of radio, which is C/f , C is the
speed of light and f is the frequency in Hz.
80
3.2. Polar sweeping
Brick wall
Glass wall
D4
w7
w6
D1
TX
v1
w5
ray1
w1
ray2
RX
D2
w2
w4
D3
w3
v2
Figure 3.5: Reflection path searching.
3.2.2
Reflection path
Starting from RX, each possible path discovered in Ref plane is traversed. The
procedure is shown in Fig. 3.5, only two rays are shown as example. According to the
second possible path in Fig. 3.3 and Eq. 3.1, Ref P lane(2) = [w1 w2 w5 w6], virtual
source v1 of w1 is generated by mirroring T X along w1, v2 is obtained by mirroring
v1 along w2. There are two reflection paths in this plane order, one is through
T X → w1(v1) → RX, and the other one is T X → w1(v1) → w2(v2) → RX. w5
and w6 are ignored for current order. As a result, the format of reflection path
matrix is expressed as:
Ref path =
w1 v1
w1 v1 w2 v2
(3.5)
Note that although the example plot is by projecting 3D space into 2D, all the rays
are traced in 3D approach. With this searching method, all candidate 3D paths
from TX to RX are recorded at the end, and RSSs due to reflections are computed
according to the model in Uniform Theory of Diffraction (UTD) [110]. For a RX at
point P , its electric fiel Eref reflected from point Q is expressed by:
Eref (dQP ) = Ei (Q)Re−j2πkdQP
81
(3.6)
Chapter 3. Ray-tracing engine and radio propagation modelling
Brick wall
Glass wall
D4
w7
w6
D1
w5
TX
w1
w4
D2
w2
D3
w3
RX
Figure 3.6: Diffraction path searching.
Where Ei (Q) is the field of ray incident at Q. R is the reflection coefficient related
to the material of encountered plane.
3.2.3
Diffraction path
Diffractions happen at the diffraction points found in Dif point. When RX is
shadowed by planes, diffraction will play a relative important role in RSS. Fig. 3.6
shows that a RX is in the diffraction region of diffraction point D2. The diffraction
field is calculated by (3.7),
Edif (dQP ) = Ei (Q)De−j2πkdQP
(3.7)
where Q represents D2 and P stands for RX in this case, D is diffraction coefficient,
D=
(1 + cos (θ))
√
,
2.0 2πf
and θ = |(∠(T X, RX) − ∠(T X, Q))| is the angle differential between the rays from
TX to Q and from TX to RX.
At the end, according UTD theory, the Etot at P is the combination of Edir ,
Eref and Edif .
Etot = Edir + Eref + Edif
82
(3.8)
3.3. Measurements and experimental results
The RSS is calculated as
RSS = 10log((Etot ·
3.3
3.3.1
λ 2
) )
4π
(3.9)
Measurements and experimental results
Outdoor RF propagation verification
The proposed method was verified by comparing the results to that of Munich
scenario [92], which was comprehensively studied by the European COST 231
working group. Fig. 3.7 illustrates the top view of the target region in Munich
city. The size of the region is 2400 m × 3400 m and there are 3 different routes
measured by COST 231 group (e.g. the red color is the first route rout0 named as
METRO200, the black one rout1 is the second route METRO201, and the green one
rout2 is the third rout METRO202).
A new training database is created through similar methods described in Chapter
2. The training images are fetched randomly from Google Maps within the region of
Munich city. Buildings, roads and trees are labeled in order to guarantee the learning
of reliable and transferable knowledge. Fig. 3.8 shows the evaluation procedure: At
the beginning, the same area in which the COST 231 group did measurements is
captured on Google Maps. The images are downloaded and divided into small
sub-images, each of which is of the similar size as the training images.
Afterwards, they are passed through the image classifier to recognize and segment
the objects belong to three classes (building, tree and road). The recognized objects
are then vectorized and the 3D database is automatically generated. Then the
propagation simulator is run to simulate the signal transmission in the target area.
Finally, the signal coverage map is visually presented to the user.
The proposed method is programmed by combining C#, Matlab and C++ code.
It was run on a PC equipped with Intel i5-760 CPU, 2.8 GHz. It took less than
1 hour to construct the 3D database and approximately 15 minutes to finish radio
estimation with a resolution of 1 m. Hence it took around 0.3 s to process 2356
points for all the three routes.
The simulation results are compared with the practical measurements. As shown
in Fig. 3.9 ∼ Fig. 3.11, the results of the 3 different routes correlate well with the
83
Chapter 3. Ray-tracing engine and radio propagation modelling
Figure 3.7: Three different routes measured by COST231 group.
real measurements. The mean error (ME) and the standard deviation (STD) of the
results are the two metrics used to evaluate the accuracy of the simulation method.
The ME of METRO200 is -0.3 dB and the STD is 5.7 dB. The ME is -0.4 dB and
STD is 5.5 dB in METRO201. The ME and STD of METRO202 are 2.2 dB and
6.7 dB respectively. The performances on the first two routes are similar and are
84
3.3. Measurements and experimental results
A small region of
original image
Classification
result
3D reconstruction
Radio simulation on the constructed 3D scene
Figure 3.8: Classification result and radio propagation procedure over Munich
scenario.
85
Chapter 3. Ray-tracing engine and radio propagation modelling
comparison on METRO200
180
170
real measurement
simulated result
160
pathloss(dB)
150
140
130
120
110
100
90
80
70
0
200
400
600
800
1000
number of location
Figure 3.9: Simulation result for the first route METRO200.
comparison on METRO201
180
170
real measurement
simulated result
160
pathloss(dB)
150
140
130
120
110
100
90
80
70
0
50
100
150
200
250
300
350
400
number of location
Figure 3.10: Simulation result for the second route METRO201.
86
3.3. Measurements and experimental results
comparison on route METRO202
180
170
real measurement
simulated result
160
pathloss(dB)
150
140
130
120
110
100
90
80
70
0
200
400
600
800
1000
1200
number of location
Figure 3.11: Simulation result for the third route METRO202.
Table 3.1: Comparison of radio estimation result with other methods.
Method
This thesis
Ericsson
CNET
COST-WI
RAY-TRI [111]
METRO200
STD Mean
5.7
-0.3
6.7
0.3
6.9
-2.1
7.7
10.8
7.1
-2.6
METRO201
STD Mean
5.5
-0.4
7.1
2.3
9.5
-3.6
5.9
15.4
6.2
-0.7
METRO202
STD Mean
6.7
2.2
7.5
1.4
5.6
-0.2
7.3
16.3
8.3
-1.4
better than the third route which has more measured points and some are located
in the streets with much more buildings.
Table.
3.1 compares the proposed method with other state-of-the-art
technologies.
The STD and ME of the proposed method are the smallest on
METRO200 and METRO201, while they are slightly greater than CNET in
METRO202. The experimental results indicate that this novel approach has good
efficiency and accuracy.
A quantitative explanation for this improvement can
be directed towards three reasons: the construction accuracy of the environment
database is 82%, the miss-classified pixels are 18%. The resolution of simulation
87
Chapter 3. Ray-tracing engine and radio propagation modelling
Figure 3.12: Four-layer architecture and physical view of the Cookie node.
is 1 m which is higher than in other methods.
Furthermore, the propagation
model is adequate in describing reduced dimensionality environments, and most
of the calculations used in the radio propagation model are based on efficient image
manipulation rather than slow geometrical computation.
3.3.2
Indoor RF propagation verification
In this section, indoor experiments are made to verify the simulation method.
Sensor nodes named Cookies[112], developed by CEI-UPM, are used in this
experiment. Cookieshave modular architecture of four layers as shown in Fig.
3.12. The layers are bonded through vertical connectors which contain all the
signals within the node. As all the layers use the same connectors in the same
position, reusability and interchangeability are much easier for catering different
application requirements. Since the experiments are focused on signal strength
measurement, the communication layer with ZigBee communication protocol is to
be concerned. The communication layer includes a ZigBee module ETRX2 from
Telegesis as shown in Fig. 3.13, which is a low power 2.4 GHz band transceiver based
on the Ember EM250 SoC ZigBee/IEEE802.15.4 solution. The antenna pattern is
near Omni-directional (see Fig. 3.14) with linear polarization.
The transmitter is connected and power supplied by laptop through USB cable,
the transmit power is set to be 3 dBm, the sensitivity of antenna is -97 dBm, the
receiver is powered by two 1.5 V batteries. TX sends packet to RX to request link
88
3.3. Measurements and experimental results
Figure 3.13: ETRX2 ZigBee communication module on communication layer.
Figure 3.14: Radio pattern of antenna of ETRX2 module.
information. Then RX fetches RSSI value of the received packet and replies to
the transmitter. By placing the receivers at different locations, different RSSIs are
obtained.
The measurement is carried out in the lab of CEI-UPM. Three different scenarios
are measured, as shown in Fig. 3.15. The Scenario A is realized in a room equipped
with several desks and computers. The transmitter is fixed at 1.04 m high above the
ground. The receiver is kept at the same height as the transmitter, and RSSIs are
measured at five locations as indicated in Fig. 3.15(a). The purpose of this scenario
is to verify the propagation model with trivial obstacles, exam the value of path loss
coefficient n and RSS(d0 ). In scenario B (Fig. 3.15(b)), the position of transmitter is
unchanged, while the locations of receiver are selected further away from transmitter,
and are in different rooms around the lab. This is to verify the attenuation model
89
Chapter 3. Ray-tracing engine and radio propagation modelling
BS
(a) Scenario A
BS
(b) Scenario B
ZigBee Coordinator
ZigBee Sensor Device
12
1
BS
(c) Scenario C
Figure 3.15: Three scenarios for radio measurements.
of non-trivial obstacles, and obtain empirical penetration parameters. Scenario C
(Fig. 3.15(c)) is realized in the corridor where the communication between TX and
RX are LOS, and because the width is much smaller compared with the length, the
tunneling effect occurs. Not only both transmitter and receiver are maintained at
same height, but also antennas are pointed toward directions with maximum RSSI
to minimize the radiation pattern degradation.
The ray tracing engine is programmed in C++ for Windows operating system.
Fig. 3.16 shows the visible planes (with blue color) and the related information
90
3.3. Measurements and experimental results
rx
tx
tx
rx
tx
rx
Figure 3.16: Ray tracing demonstrations from different TXs and RXs.
discovered at the polar sweep step. Actually, the floor and ceiling are also visible
planes to TX, but they are not highlighted for providing a friend vision. Those
three figures show all the ray paths from TX (magenta point) to a RX (yellow
point). As can be seen, the number of rays that could arrive at a RX is varied.
Directed, reflected, diffracted rays are computed through full 3D approach without
simplification during ray tracing.
Fig. 3.17 and Fig. 3.18 show the simulated RSS maps for the campaigns.
Both ray tracing algorithm and radio propagation model are developed for 3D
computation. As can be seen, the walls significantly reduce signal strength when
encountered by the signal path. During simulation, some parameters are constant,
the resolution is set to be 0.2 m.
Different groups of simulation results are obtained by varying depth from 0 to
6, which are then compared with the real measurements, as well as the Free-space
model (3.10) and Log-normal model (3.11), where P Lref is the path loss at reference
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Chapter 3. Ray-tracing engine and radio propagation modelling
Figure 3.17: Simulation result: example 1.
Figure 3.18: Simulation result: example 2.
distance and Xσ is gaussian random value with 0 mean and σ as the standard
deviation which is equal to 3 in this study,
P Lf s (dB) = 20 log10 (d) + 20 log10 (f ) − 147.55
(3.10)
P LLN (dB) = P Lref + 10 nln log10 (d/d0 ) + Xσ
(3.11)
Scenario A is a typical indoor environment without important obstacles between
transmitter and receiver. Fig. 3.19 compares the proposed ray tracing method with
Free-space path loss model, Log-normal model and real measurements. Table. 3.2
shows their mean error M E and standard deviation error ST D in dB, compared
with the real measurements. Free-space model is too optimistic in estimating indoor
propagation with the highest M E and ST D.
While Log-normal model is much
better than the Free-space model. The ray tracing is better than the Log-normal
model. Moreover when depth = 3, the ray tracing method is the most accurate
result. If all the RXs are in the same room of TX, the dominant contributions of
92
3.3. Measurements and experimental results
Result Comparison for Scenario A
-30
-40
-50
RSSI (dBm)
-60
-70
-80
-90
Direct path
Depth 3
Free Space
Log-normal
Real Measurment
-100
-110
-120
1
2
3
Sensor ID
4
5
Figure 3.19: Results and comparisons of Scenario A.
RSS are direct, reflected and diffracted paths without considering penetration losses.
Some parameters of the proposed radio propagation model are determined through
this case study to calibrate with the real world impacts, thus n = 2, Lobstacle(watts) =
3.6 and α = 0.91. The reference distance loss of the Log-normal model P Lref
is calibrated to 60 dB and nln = 3 which will be maintained unchanged for the
remaining two scenarios. One should also notice that the gaussian random variable
Table 3.2: Results comparison: Scenario A.
XXX
Proposed
XXXMethod
XXX
depth
= 0 depth = 3
Result
XX
ME (dB)
STD (dB)
3.18
3.22
93
1.99
2.00
FS
LN
30.87
6.28
6.42
6.67
Chapter 3. Ray-tracing engine and radio propagation modelling
Result Comparison for Scenario B
-30
-40
-50
RSSI (dBm)
-60
-70
-80
-90
Direct path
Depth 3
Free Space
Log-normal
Real Measurment
-100
-110
-120
1
2
3
Sensor ID
4
5
Figure 3.20: Results and comparisons of Scenario B.
in the Log-normal model may slightly change the performance for the same scenario
at different simulations.
In Scenario B, RXs are in different rooms than TX, all possible paths may make
contributions to RSS. In this scenario, the penetration attenuations have strong
impacts on the RSS, see Fig. 3.20. Table. 3.3 shows the M E and ST D for each
method. As expected for the ray tracing method, the results that only consider
direct path are slightly worse than the ones considering multi-path effects. Since
Log-normal model does not consider obstacles between TX and RX, it can not
accurately model the indoor environment when obstacles exist and the ray tracing
method turns out to be the best in this case.
In Scenario C, there is no any obstacle between the TX and RXs. However due
to the narrow width of corridor, reflections and diffractions play more important
94
3.3. Measurements and experimental results
Table 3.3: Results comparison: Scenario B.
XX
XXX Method
Proposed
XXX
XXX depth = 0 depth = 3
Result
ME (dB)
STD (dB)
2.71
2.91
2.42
1.65
FS
LN
37.29
4.02
7.49
4.61
role than the previous two scenarios. As can be seen from Fig. 3.21, the ray tracing
results match quite well with the real measurement even with small fluctuations. The
slope of Free-space model is almost the same as ray tracing, which is reasonable, as a
corridor can be considered as a free space environment in a long distance. However,
the entire indoor environment is definitely not a free space. Table. 3.4 indicates
that the proposed ray-tracing method outperforms the other two algorithms.
Result Comparison for Scenario C
-30
-40
-50
RSSI (dBm)
-60
-70
-80
-90
Direct path
Depth 3
Free Space
Log-normal
Real Measurment
-100
-110
-120
1
2
3
4
5
6
7
Sensor ID
8
9
10
Figure 3.21: Results and comparisons of Scenario C.
95
11
12
Chapter 3. Ray-tracing engine and radio propagation modelling
(a) Bird’s view of the deployment on the toy scenario
(b) Zoom in of the deployment
Figure 3.22: Demonstration of the toy example which has similar deployment as
scenario C.
As it can be noticed, the accuracy rises as the value of depth grows. Through
the comparisons of the three scenarios, it is concluded that the proposed ray tracing
method performs more accurately and robustly than other methods. Therefore it is
suitable to be applied for different scenarios in indoor environment.
The ray tracing engine is run in a PC with Intel Core i5-760 CPU of 2.8GHz
frequency. Besides the three deployments in CEI-UPM environment, a virtual WSN
deployment is realized in East Lansing scenario with the topology similar as scenario
C, see Fig. 3.22.
Table 3.4: Results comparison: Scenario C.
XXX
Proposed
XXXMethod
XXX
depth
= 0 depth = 3
Result
XX
ME (dB)
STD (dB)
1.19
0.80
96
1.15
0.80
FS
LN
30.23
1.41
10.65
1.41
3.3. Measurements and experimental results
20
Scenario A
Scenario B
Scenario C
East Lansing
Time (s)
15
10
5
0
direct
1
2
3
4
Reflection depth
5
6
Figure 3.23: Average time consumption of polar sweeping, by varying the depth of
reflection rays.
The average time consumptions of polar sweeping and ray tracing, by varying
the reflection depth for each tested scenario, are shown in Fig. 3.23 and Fig. 3.24
respectively. Both figures indicate that the computation time increases as depth
increases. The time consumptions of polar sweeping for scenarios A∼C in CEI-UPM
are quite close to each other, while it took 2.8 times longer in average to finish the
polar sweeping in East Lansing environment.
The time cost of ray tracing of
East Lansing goes much higher than that of Scenario C when depth exceeds 3, as
much more new reflection planes are involved in the former environment. Fig. 3.25
shows that the polar sweeping without kd-tree traversing takes 3.35 times longer
than the opposite choice, therefore the kd-tree traversing can significantly improve
the efficiency of the ray tracing engine.
The engine takes 37 ms in average to construct a kd-tree for CEI environment
and 78 ms by doing so for the East Lansing environment. Note that the kd-tree
construction is realized based on splitting properly all the primitives in the target
region, the time spent on kd-tree construction is dependent on the number of planes
in the environment. While the time spent on tracing rays and polar sweeping
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Chapter 3. Ray-tracing engine and radio propagation modelling
40
Scenario A
Scenario B
Scenario C
East Lansing
35
Time (ms)
30
25
20
15
10
5
0
direct
1
2
3
4
Reflection depth
5
6
Figure 3.24: Average time consumption of ray tracing, by varying the depth of
reflection rays.
depends on the value of depth and the complexity of surrounding environment of
the transmitter. The more reflections and planes exist around a TX, the longer the
simulation time will be.
Table 3.5: Attenuation parameters of major objects indoors.
Materials
Antenuation
Parameter (dB)
Brick
Glass
8
2
Metallic board
Human Body
of PC
5
2∼10
During the experiment, the attenuation parameters of different obstacles are also
measured along the propagation path, as indicated in Table 3.5. The main obstacles
are walls, glass of windows, metallic boards and human bodies. The attenuation
caused by stilled human body even varies widely. Human movements are dynamic
and there are some researchers focusing on the interference of human body on the
radio propagation. The significance of the effect of human body towards signal
strength depends on how high the sensor nodes are installed, and whether human
98
3.4. Conclusion
20
polar sweep with kd-tree
polar sweep without kd-tree
Time (s)
15
10
5
0
direct
1
2
3
4
Reflection depth
5
6
Figure 3.25: Average time consumption of polar sweeping with and without kd-tree
traversing.
movement is important for sensor applications, such as in localization systems or
health care applications.
3.4
Conclusion
The developed ray tracing engine propagation simulation method contributes on
efficiency and accuracy to the radio estimation. By using image processing concepts
including the kd-tree space division algorithm and modified polar sweep algorithm
the rays are traced efficiently without detecting all the primitives in the scenario.
The ray-tracing algorithm ensures accurate and practical results.
The radio
propagation model emphasizes not only the materials of obstacles but also their
locations. Hence, the performance of simulation is robust and accurate compared
with conventional propagation models. The experimental results imply that this
methodology is suitable for both outdoor urban scenes and indoor environments,
moreover it can be applied to GSM communication and ZigBee protocol by varying
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Chapter 3. Ray-tracing engine and radio propagation modelling
frequency parameter in the model. Moreover, the ray tracing engine is featured with
generality that can be used for any environment database with the similar description
format on primitives. The experimental data are available in this chapter, which can
be used for comparisons by other researchers. The time consumptions are recorded
for all the steps of ray tracing, the measurements on RSSs for different deployments
and the attenuation parameters of different materials are also available in this work.
Beyond the RF propagation estimation, the sensing signal of sensor nodes, which
are sensitive to the obstacles, can also be benefit from the ray-tracing engine.
The details for modeling the sensing coverage is introduced in the next chapter
together with the planning algorithm. The indoor furniture also have influences on
RF propagation and sensing signal, although it is not introduced in this chapter,
with the ray tracing method, they can be detected. Therefore once provided with
their models together with the environment database, the calculated results will
automatically consider their impacts without any simplification.
100
Chapter 4
Planning the WSN
One of the major challenges in designing wireless sensor networks is the support
of various application requirements while coping with the computation, energy,
communication, sensing and cost constraints. Careful node placement can be a
very effective optimization means for achieving the desired design goals. However,
optimal node placement is a very challenging problem that has been proven to
be NP-Hard for most of the formulations of sensor deployment [59, 60, 61], and
the modelling of important metrics turns out to have significant impacts on the
deployment decision.
In this chapter, design parameters including the network topology, heterogeneity
of nodes and the modelling of important metrics are discussed.
The current
researches on optimized node placement have been reported in Chapter 1. According
to the reviews, those algorithms and heuristics somehow have serious limitations.
From the aspect of metric modelling, only a few of them tackle the 3D deployment
issue and they are developed only for 3D indoor applications [42, 51, 52]. Even fewer
works model the sensing coverage and radio propagation by considering the realistic
scenario where obstacles exist. None of the aforementioned algorithms modeled
the network longevity properly and practically, they often employ unilateral and
unrealistic formulations. Moreover, the optimization targets are one-sided. Without
comprehensive evaluation on the important metrics, the performance of WSNs can
not be entirely optimized and reliable.
An efficient WSN planning algorithm is proposed in this work to tackle the above
mentioned challenges and efficiently assist designers on deploying reliable WSNs.
This algorithm contributes on the following aspects:
• Comprehensive
metrics
are
considered.
This work considers
connectivity, sensing coverage, cost, lifetime, packet delay and packet loss rate,
Chapter 4. Planning the WSN
which to our best knowledge, is the most comprehensive evaluation scheme for
analyzing the performance of WSN.
• Practical metrics modelling by integrating network simulator. The
connectivity and sensing coverage are modelled in assistance of 3D ray-tracing
method which is sensitive to the existence of obstacles; hardware cost refers to
the number of devices as well as their types. Routes of network are constructed
by using AODV protocol based on the computed connectivity information.
Network longevity, packet delay and packet drop rate are obtained through
triggering events in WSNet simulator according to a user defined sensing task
and the provided topology. It is the first time that network simulator is
involved in a planning algorithm to tackle the difficulty on modelling those
vital metrics and provide practical evaluations.
• Efficient
and
multi-objective
optimization.
A multi-objective
optimization algorithm is developed for WSN to optimize the cost, coverage,
lifetime, packet delay and packet drop rate.
The individual length is
changeable so that the cost can be optimized, meanwhile crossovers and
mutations are designed to eliminate invalid modifications to improve the
computation efficiency. NSGA-II ranking method, which is proved with high
efficiency, is employed by this work.
4.1
Topology
One important property of a sensor network is its diameter, that is, the maximum
number of hops between any two nodes in the network. In its simplest form, a sensor
network forms a single-hop network, with every sensor node being able to directly
communicate with every other node. An infrastructure-based network with a single
base station forms a star network with a diameter of two. A multi-hop network may
form an arbitrary graph, but often an overlay network with a simpler structure is
constructed such as a tree or a set of connected stars. The topology affects many
network characteristics such as latency, robustness, and capacity. The complexity
of data routing and processing also depends on the topology. Fig. 4.1 shows four
different topologies that have been applied to WSN applications.
102
4.1. Topology
SN
BS
BS
SN
(a) Star Network
(b) Tree Network
SN
CH
SN
CH
CH
BS
BS
CH
(c) Mesh Network
CH
(d) Cluster Network
Figure 4.1: Different topologies of WSN.
4.1.1
Star network
In star networks, nodes are connected to a centralized communications hub. Each
node cannot communicate directly with one another; all communications must be
routed through the centralized hub. Each node is then a clientwhile the central
hub is the serveritself or a gateway node that is in direct communication with
the base station. An example of a star network is shown in Fig. 4.1(a). The failure of
a link does not affect the entire network and leaves the rest of structure unchanged.
However, it is not fault tolerant as there is no alternative path from unconnected
node to the base station once the link is failed or obstructed. Due to the constraint
on radio communication ability, sensor nodes in star topology can not be placed far
away from the sink, thus its applications are limited to small networks. As there is
no delay due to buffering at routers along the path, the data latency in star topology
103
Chapter 4. Planning the WSN
is quite low, nevertheless there is likely to be more loss due to collision when network
density increases.
4.1.2
Tree network
A natural and logical extension of the star topology is the tree structure where sink
node is the root and a collection of star networks are arranged at different levels in
hierarchy, see Fig. 4.1 (b). All the communications between children (lower level)
must be routed through their parent (upper level). The parent node has higher traffic
load thus the energy is depleted faster than its children. Once die out of battery,
the children attached to it will be affected and become isolated. Accordingly, as the
level of node becomes higher, more nodes will be influenced once the critical link is
broken.
4.1.3
Mesh network
Mesh Networks are multi-hop local area networks in which each sensor node not
only sends and receives its own message but also functions as a router to relay
messages for its neighbors through the network. Mesh topology facilitates multiple
communication paths from the sensor nodes to the base station. A special case of
mesh is the grid topology where each grid point represents a sensor node, and the
links are the edges of the grid. An example of mesh topology is shown in Fig. 4.1
(c).
Multi-hop routing methods effectively overcome shadowing and path loss effects,
thus mesh WSNs are self-configuring networks that dynamically optimize routes
through the network based on the best link quality between neighbor nodes. If the
node density is high enough, multi-hop routing uses a large amount of nodes to
balance the relay traffic all over the WSN. However data latency increases as the
number of hops increases, WSN designers should carefully plan the transmission and
duty-cycle scheduling to overcome the collision and interference. Generally speaking,
the amount of data, which are relayed for each node, increases as a node getting
closer to the base station, therefore some redundant nodes are necessary to backup
in the bottleneck area of WSN. Many applications employ mesh topology, such as
104
4.2. Heterogeneity
the one for monitoring active volcano [17] and the GreenOrbs deployment [113] for
ecological surveillance in the forest.
4.1.4
Cluster network
In clustered hierarchical topology, all nodes in a WSN are joined at the lowest level.
The sensors use their local neighborhood information to form a set of clusters and
elect a cluster head (CH) for each cluster. The CH election process can be based on
various parameters such as available energy resources, proximity to the base station,
and number of neighbors. The CH in the lowest level are arranged into clusters in
a higher level. The process is repeated for each level in the hierarchy. The number
of hierarchical levels depends on several criteria including coverage requirement,
deployment region, node density, and transceiver and sensing range. The clustered
hierarchical architecture maintains a tree routed at the sink node, with a hierarchy
of CH as the internal nodes and sensor nodes as leaf nodes of the tree. Nevertheless,
different from the tree topology, it still maintains the multi-hop mesh within the
same cluster.
Compared with mesh mode, data aggregation is more efficient in cluster topology
as only the CHs are in charge of aggregating and forwarding data to the sink, hence
it can be extremely effective in one-to-many, many-to-one, or one-to-all (broadcast)
communications.
On the other hand, all data directed to the sink results in the CH near the sink
come to have high relay traffic. As a result, CH around sink uses much more energy
compared with other CHs, and this is one of the main problems that shorten the
network life time in clustered WSN. Due to its complexity in CH election, each
node should be aware of status of its neighbors and to our best knowledge, cluster
topology has not been practically employed by any WSN application.
4.2
Heterogeneity
When considering the heterogeneity, WSN can be classified into two categories:
homogeneous WSN and heterogeneous WSN. Early sensor network visions
anticipated that sensor networks would typically consist of homogeneous devices
that were mostly identical from a hardware and software point of view. Some
105
Chapter 4. Planning the WSN
projects, such as Amorphous Computing [114], even assumed that sensor nodes
were indistinguishable, that is, they did not even possess unique addresses or IDs
within their hardware. This view was based on the observation that otherwise it
would not be feasible to cheaply produce vast quantities of sensor nodes.
However, in many prototypical systems available today, sensor networks consist
of a variety of different devices. Nodes may differ in the type and number of attached
sensors [32, 44]; some computationally more powerful nodes may collect, process,
and route sensory data from many more limited sensing nodes [45]; some sensor
nodes may be equipped with special hardware such as a GPS receiver to act as
beacons for other nodes to infer their locations such as the projects in [115, 116],
which use GPS and satellite to locate the animals; some nodes may act as gateways
to long-range data communication networks (e.g. 3G gateways are used in [117],
satellite networks, or the Internet [118]). The degree of heterogeneity in a sensor
network is an important factor since it affects the complexity of the software executed
on the sensor nodes and also the management of the whole system.
4.3
4.3.1
Introduction and modelling of important metrics
Preliminaries and assumptions
We summarize the important symbols that are used in this section in Table 4.1. Our
Table 4.1: Important symbols
Symbol
Si
Ni
As
Φ Si
C
cost
L
Pl
Pd
DC,cost,L,Pl ,Pd
Meaning
sensor node with ID = i
node device with ID = i and S ∈ N
sensing/monitoring area
a set of points that are the covered points of Si
coverage
cost
lifetime
packet latency
packet drop rate
desirability formulation of C, cost, L, Pl , Pd
106
4.3. Introduction and modelling of important metrics
sphere ( S i , rS I )
m  ISi
rS i
Si
O(mSi )
Figure 4.2: The searching of covered point
method employs a ray-tracing method described in our previous work [109] as well
as in Chapter 3 to emulate the propagation paths of both radio and sensing signals.
In the real world propagation, multi-path phenomena occurs on the radio signal
transmission: when a radio signal encounters obstacles, reflections and diffractions
happen. Thus received signal strength at the receiver is computed by accumulating
the arrived waves from all directions. Unlike the radio propagation, the sensing
signal is usually only considered by using direct path, which halts at the intersected
points with the surfaces of obstacles. Thus a covered point of a sensor node Si is
defined as following:
Definition 1 Covered point: A point m is said to be covered by Si , if and only
if it is within the sensing range of Si and is not obstructed by any obstacle. ΦSi
represents a set of all the points that are the covered points of Si .
−−→
ΦSi = {m|m ∈ sphere(Si , rSi )∧!O(mSi )}
As indicated in Fig. 4.2, sphere(Si , rSi ) is the sphere with radius rSi centered at
−−→
node Si and indicates the ideal sensing area of Si . O(mSi ) indicates whether the
sensing path from m to Si is obstructed.
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Chapter 4. Planning the WSN
Routing scheme: There have been many algorithms proposed for routing data
in sensor networks, which consider the characteristics of sensor nodes together with
the application requirements. Nowadays, an overwhelming number of commercial
sensor devices support the distance vector based routing protocol such as the Ad
hoc On-Demand Distance Vector (AODV) routing [119] and link-state based routing
protocol Dynamic Source Routing (DSR) [120]. However, the routing results of
minimum-weight/minimum-hop based routing protocols are very similar and can be
computed based on the shortest-path searching by Dijkstra’s algorithm, in which
data are collected and forwarded to BS via the path with the best distance metric.
In this work, the distance metric is modelled by the qualities of established links
according to the ray-tracing results, which not only considers the distance between
transmitter and receiver but also indicates the impact of surrounding environment.
According to the comparison and traversing procedure over the WSN, the pseudo
code of Dijkstra algorithm is shown in Listing. 4.1.
Listing 4.1: Pseudo code of Dijkstra algorithm
S={all nodes except the base station V};
For each node u{/*search the path from u to V*/
int temp = maxint;
int u = v;
For each node j in S {
/*search a un-traversed node with shortest path to V*/
if ((!S[j])&&(dist[j]<temp)){
u = j;
temp = dist[j];
}
}
S[u] = true;
/*Remove u from the list*/
For each node j {
if ((!S[j])&&
j is directely connected wih u){
int newdist = dist[u][v] + max(c[u][j], c[j][u]);
/*if the path through u is smaller than the older
distance from j to v
the shottest route is updated for node j*/
if (newdist<dist[j][v]) {
dist[j][v] = newdist;
108
4.3. Introduction and modelling of important metrics
next[j] = u;
/*the next hop of j is u */
}
}
}
}
4.3.2
The cost of WSN
One of the design goal, from economic point of view, is to reduce the cost while
fulfilling requirements of an application. Many companies and research organizations
arise in the recent decade to design and manufacture sensor nodes, which provide
various options on the budget. Besides, the installation of sensor nodes requires
extra human efforts. For instance, the cost of placing sensors on the ceiling and
walls is different from placing them within a human-active space; even attaching
sensor nodes to different heights can vary the costs. As a result, the Cost of WSN
is categorized into hardware cost and deployment cost in this work.
4.3.2.1
Hardware cost
MICA and MICA2 were once the most successful families of Berkeley motes. The
MICA2 platform, whose layout is shown in Fig. 4.3, is equipped with an Atmel
ATmega128L and has a CC1000 transceiver. Berkeley motes up to the MICA2
generation cannot interface with other wireless-enabled devices [121]. However,
the newer generations MICAz and Telos support IEEE 802.15.4, which is part of
the 802.15 Wireless Personal Area Network (WPAN) standard being developed by
IEEE. At this point, these devices represent a very good solution for generic sensing
nodes, even though their unit cost is still relatively high (about $100 ∼ $200).
Various platforms have been developed for the use of Berkeley motes in mobile
sensor networks to enable investigations into controlled mobility, which facilitates
deployment and network repair and provides possibilities for the implementation
of energy-harvesting. UCLAs RoboMote [122], Notre Dames MicaBot [123]
and UC Berkeleys CotsBots [124] are examples of efforts in this direction.
UCLAs Medusa MK-2 sensor nodes [125], developed for the Smart Kindergarten
project, expand Berkeley motes with a second microcontroller. An on-board power
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Chapter 4. Planning the WSN
management and tracking unit monitors power consumption within the different
subsystems and selectively powers down unused parts of the node.
Intel has designed its own iMote [126] (see Fig. 4.3) to implement various
improvements over available mote designs, such as increased CPU processing power,
increased main memory size for on-board computing and improved radio reliability.
In iMote, a powerful ARM7TDMI core is complemented by a large main memory
and non-volatile storage area; on the radio side, Bluetooth has been chosen, thus
the price of an iMote is about $299 which is very high.
MICA2
TelosB
iMote2
Figure 4.3: Products by Crossbow.
The BTnode rev3 hardware designed in 2007 by ETHZ [121] (see Fig. 4.4),
is based on an Atmel ATmega128L microcontroller, a Bluetooth module and a
low-power radio which is the same as that used on Berkeley MICA2 Motes. BTnode
costs around $200 which is slightly higher than MICA2 due to an extra cost on
Bluetooth module.
Figure 4.4: BTnode rev3.
110
4.3. Introduction and modelling of important metrics
Waspmote [127] in Fig. 4.5 is a sensor device developers oriented product. It
works with different communication protocols (ZigBee, Bluetooth and GPRS) and
frequencies (2.4GHz, 868MHz, 900MHz) and creates links with distance up to 12
km. Waspmote is compatible with more than 50 sensors. The flexible property
makes Waspmote suitable for different types of applications and the price varies
according to the configuration. For example, sensors with ZigBee module cost the
least compared with that attached with Wi-Fi and Bluetooth module, while the one
with 3G+GPS costs around $300 and is the most expensive configuration.
WiSMote [128] in Fig.
4.5 appears since 2011.
It is a sensor/actuator
module well adapted to WSN applications. The wireless link operates over the
2.4 GHz ISM band. With its wide range of embedded sensors and its variety of
extension connectors, it is able to monitor any kind of physical measurements in
fields like environment, healthcare, domotics, smart building, logistics or industrial
applications. WiSMote embeds an small footprint operating system (Contiki) plus
an IEEE 802.15.4 protocol stack compatbile with Zigbee and 6LoWPAN (IPv6).
The availability of inexpensive hardware such as CMOS cameras and
microphones has fostered the development of Wireless Multimedia Sensor Networks
(WMSNs), i.e., networks of wirelessly interconnected devices that are able to
ubiquitously retrieve multimedia content such as video and audio streams, still
images, and scalar sensor data from the environment. One of the recent multimedia
wireless sensor is the SEED-EYE [129] in Fig. 4.5, which provides an advanced
board for implementing low-cost (about $150 per unit)WMSN. It hosts a powerful
Microchip PIC32, and has a full set of communication interfaces such as Ethernet,
IEEE802.15.4 / Zigbee, and USB. Moreover, it is integrated with a CMOS Camera,
making it an ideal board for implementing next generation imaging WSNs.
Beyond the on-shelf sensor products, there are enormous research organizations
dedicating to develop and prototype their own sensor platforms. Typically, those
prototypes are based on add-on/modular hardware design. Although the designs
are not highly optimized in terms power consumption or size and price, one can still
foresee the bright future once they are launched in the market. The Tyndall’s mote
family [130] developing a range of ISM band wireless sensing systems for deployment
in the environment focusing on the key areas of the environment and fitness and
health, the structure is shown in Fig.
4.6; Cookies[112] developed by the
111
Chapter 4. Planning the WSN
Waspmote
Wismote
SEED-EYE
Figure 4.5: Waspmote(left),Wismote(middle), SEED-EYE(right).
researchers at Centro de Electronica Industrial of Universidad Politecnica de Madrid
(CEI-UPM), have a modular architecture of four layers, as depicted in Fig. 3.12.
Each layer fulfills a specific functionality in the node, and the layers are changeable
for different applications. Moreover, it is possible to have a heterogeneous network
with nodes composed of different layers. In the current motes, 2.4 GHz ZigBee
communication protocol is used and AODV protocol is embedded.
Tyndall Mote
Figure 4.6: Tyndall mote.
During the investigation of different sensor node manufacturers, we find out
that some of them also produce other types of node to cater the heterogeneity and
various applications of WSN. WiSGate of Arago Systems and Meshlium produced by
Libelium act as a gateway between a wireless sensors/actuators network and other
type of network. In [131], the authors analyzed hybrid sensor networks consisting
of transceiving (cluster-heads) and transmit-only sensors. By using the developed
112
4.3. Introduction and modelling of important metrics
mathematical model of physical and MAC layer, they demonstrated how much the
dollar-cost and the power consumption of a sensor network can be decreased while
maintaining the same network coverage.
Table 4.2 summarize the hardware features of above mentioned sensor
productions and compare their prices. Due to the heterogeneity property and various
topologies of WSNs, motes with different functions should be considered to obtain
a optimum cost solution rather than simply using a uniform type of all-function
sensor mote for the whole network. Based on the above survey, motes are classified
into three types in this work:
• Sensor Node (SN): equipped with sensors to monitor the surrounding
environment.
In this work, each sensor node is static and has wireless
communication and routing ability.
• Relay Node (RN): has the ability of communication and routing. RN is usually
needed to fill radio communication hole or to balance traffic load.
• Base Station (BS): is in charge of aggregating data and is directly connected
with the central server. A WSN has one BS and its location is predetermined
by users.
Since the number and location of BS is assumed to be fixed in this work, the cost
of BS is also fixed and is not included in the model of hardware cost. SN contains
extra sensor module besides the communication and routing module, therefore a SN
costs more than a RN. The hardware cost model costhw is expressed by accumulating
the price of each node deployed in the area A:
costhw =
M
(P (Ni .type))
(4.1)
i=1
where P (Ni .type) indicates the relative price that is dependent on the type of node
Ni , for instance P (RN ) = 1, P (SN ) = 3.
The available budget is considered as the maximum cost costmaxhw .
The
normalization of costhw provides a desirability component of the design goal to
minimize the hardware cost, and Dcost is used in this work to represent the
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Chapter 4. Planning the WSN
BTnode rev3
MICA2
Telos
Tmote Sky
Sun Spot
Imote2
Platform
2009
2011
2012
2007
2002
2004
2005
2006
2007
Year
ATmega1281
MSP430 5x
PIC32MX795F512L
ATMega128L
ATMega128L
MSP430F149
TI MSP430
AT91RM920T
Intel PXA271
CPU
CC1000
CC2420
CC2420
CC2420
CC2420
Bluetooth
CC1000
8 radio modules
CC2520
MRF24J40MB
Communication
128 KB Flash
External
Memory
512 kB Flash
512 kB Flash
1 M Flash
4 M Flash
32 M Flash
2×AA
2×AA
N/A
2×AA
Power
Supply
2×AA
2×AA
2×AA
Lithium ion
2×AA
$130˜$300
N/A
$200
$215
$150
$110
$99
$750
$299
Price
128 KB Flash
2 M Flash
N/A
Table 4.2: Features of various platform.
Waspmote
WiSMote
SEED-EYE
114
4.3. Introduction and modelling of important metrics
desirability on hardware cost.
Dcost =
4.3.2.2
costmaxhw − costhw
costmaxhw
(4.2)
Deployment cost
During the deployment of nodes and equipments, human efforts and extra tools are
needed in order to mount the nodes in the decided locations. The investigation on
the lessons learnt by other researchers indicates that mounting problem occurs in all
the works. The authors of [132] deployed WSN for precision agriculture, and they
observed that multi-path fading which was exacerbated by the movement of leaves
of the maize plants played a very crucial role on RSSI. From 2004-2005, Langendoen
et al. [133] tried to deploy more than 100 sensor nodes to monitor the potato crops
for precision agriculture. To avoid obstruction of when the potato crop is flowering
and leaves cover the (ground-based) antennas, the nodes were installed on poles
at a height of 75 cm. Besides, they included a safety margin to ensure that the
nodes could not be hit by farming equipment attached to a tractor. The authors
also learned very interesting lessons in [134], they developed a WSN to monitor the
indoor environment. However, they found in two occasions that the sensor nodes
are taken from where they were. As a result, they decided to place the sensor nodes
not for the best coverage but for the best security. Hence those sensor nodes are
eventually either hidden from field of vision or placed high up on the wall.
There might exist non-deployable area where nodes are not allowed to be placed,
those area should be pre-defined and the corresponding deployment cost can be
assigned with the maximum value. In this work, deployment cost costd is modeled
as boolean value and the forbidden area is assigned by users through GUI interface
as shown in Fig. 4.7, costd = 1 if the area is not accessible, otherwise costd = 0.
4.3.3
Coverage
Sensing coverage is one of the key issues that should be considered when deploying a
WSN, as it corresponds to the quality of service that can be provided by a WSN. The
coverage concept can be defined and categorized based on the node density level.
If only parts of the area are covered by the sensor nodes, the coverage is sparse;
if the area is completely (or almost completely) covered by sensors, the coverage
115
Chapter 4. Planning the WSN
cost=0
h=1 m
Width=3 m
cost=0
Forbidden
Area
h=2 m
cost=1
cost=1
Figure 4.7: Deployment cost configuration in vertical view.
is dense; otherwise, if the same detected location is covered by multiple sensors,
the coverage is called redundant. The density of coverage is normally determined
by user requirements which may vary across different applications. An adequate
coverage is a key to robust WSN application, and it may also be exploited to extend
the network lifetime by switching redundant nodes to sleep modes to reduce power
consumption. In this work, K-coverage problem is investigated and is concerned as
the potential problem to be tackled by planning algorithm.
Definition 2 Target K-coverage Requirement: At any given moment, any
target point m ∈ As is the covered point of at least k different SNs (k = 1 · · · M ).
Therefore the desirability of K-coverage requirement C is expressed as:
DC =
4.3.4
|
M
i=1 {m|m∈As ∧m∈ΦSi }|
|{m|m∈As }|×k
(4.3)
Connectivity
Wireless sensor networks are represented by a graph G = (V, E) where V is the set
of nodes and E ⊆ V 2 is the set of edges: (Nu , Nv ) ∈ E means that Nu and Nv are
neighbors. The neighborhood set N(u) of Nu is expressed by
N(u) = {Nv |(Nu , Nv ) ∈ E ∨ (Nv , Nu ) ∈ E}
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4.3. Introduction and modelling of important metrics
wireless links are determined according to received signal strength calculated by
accurate ray-tracing method, thus edges are defined as :
E = {(Nu , Nv ) ∈ V 2 |u = v ∧ RSS(uv) ≥ RXs }
Where RSS(uv) is the received signal strength from Nu to Nv , and RXs is the
sensitivity of antenna at the receiver.
As can be seen from the edge definition, communication links can be established
if the RSS is above the sensitivity of antenna. A WSN is said to be connected, if
any two nodes belong to a WSN are linked together by edge(s) via single hop or
multiple hops. Connectivity is intermittent if the network is occasionally partitioned.
If nodes are isolated most of the time and enter the communication range of other
nodes occasionally, the communication is said sporadic ([135]). Note that despite
the existence of partitions, messages may be transported across partitions by mobile
nodes, which is not the case in this work where only static network is considered.
Connectivity mainly influences the design of communication protocols and methods
of data gathering. Generally speaking, the this concept can be categorized into two
directions:
Definition 3 Connected K-Coverage Problem:
Given a sensor network
consisting of n sensors and an interest region, the network should satisfy the
following two conditions at any moment:
1. Satisfy the conditions of the K-Coverage requirement
2. The communication graph G is connected
Definition 4 K-connected Problem: A graph G is said to be k-connected if
for each pair of vertices there exist at least k mutually independent paths of edges
connecting them. In other words, the graph G is still connected even after removal
of any k − 1 vertices from G.
In [136], three localized K-CDS construction protocols were proposed. The
first one is a probabilistic approach which is based on K-Gossip. The second is
a deterministic approach which is an extension from the K-coverage condition. The
last one is Color-Based K-CDS Construction. The authors in [137] proposed two
117
Chapter 4. Planning the WSN
algorithms: one centralized algorithm CGA and one distributed algorithm DDA,
to tackle the limits of k = m in [136].
Although the protocols and theorems
have been focused for redundant connection for a robust WSN, the practical
heuristic to construct a k-connected WSN is rarely studied. The work in [138]
proposes deployment patterns to achieve full coverage and three-connectivity, and
full coverage and five-connectivity under different ratios of sensor communication
range (denoted by Rc ) over sensing range (denoted by Rs ) for WSNs. The authors
also discover that there exists a hexagon-based universally elemental pattern which
can generate all known optimal patterns. However, as stated in the paper, when
considering the non-disc sensing model or geographical constraints or heterogeneity
of sensor nodes, the proposed deployment pattern is not optimal.
In this work, connectivity of WSN is constructed to tackle the Connected
K-Coverage Problem with K-connected network topology by using the
previous mentioned sensing model, practical multi-path radio propagation model
on heterogeneous WSNs.
4.3.5
4.3.5.1
Lifetime, Packet latency and Packet drop rate
Lifetime
As most of the sensor nodes are powered by batteries, they will exhaust energy
after a certain time once deployed in the environment. Therefore the WSN will be
disconnected and no longer satisfy the sensing requirement. The lifetime of sensor
network is a very important metric and WSN designers have done many efforts
to prolong it. The methods include developing proper MAC periods, optimizing
the topology to reduce bottleneck nodes, developing back up plans and routing
algorithms. The network lifetime in our work is defined as the time that the first
node exhausts its energy. The desirability of this metric is expressed by a ratio
between the actual lifetime (L) and the maximum expected lifetime (Lmax ) by the
WSN designer. The expression of DL is:
DL =
L
Lmax
118
(4.4)
4.3. Introduction and modelling of important metrics
4.3.5.2
Packet latency
Packet latency is defined as a average end-to-end delay from the source to the
destination (BS). There are many factors that affect packet latency and the most
important factors are: the usage of channel, the hops between source and destination
and the scheduling of nodes along the routing path. The desirability of packet
latency is expressed as:
D Pl = 1 −
Mp
i
Pl (i)
Mp
(4.5)
where Mp is the total number of data packets generated by all the sensors and Pl (i)
is the latency of packet i.
4.3.5.3
Packet drop rate
Packet drop can be caused by signal degradation over the network medium due to
multi-path fading, channel congestion, corrupted packets rejected in-transit, faulty
networking hardware, faulty network drivers or invalid routes. The packet drop rate
is a ratio between the number of dropped packet (Pd ) and the number of generated
data packets, and the desirability over this metric is:
D Pd = 1 −
4.3.5.4
Pd
Mp
(4.6)
Proposed strategy by using WSNet simulator to model L, Pl and
Pd
Because L, Pl and Pd can be affected by network topology, real-time communication
and packet load, it is difficult to precisely model them through simple formulas. That
is the reason why protocol designers usually estimate such performance through
network simulator, where WSN can be simulated with a determined or random
topology with nodes being scheduled and data load being assigned based on user
specifications.
Inspired by those works, we integrate WSNet1 simulator in this work to observe
the three complex metrics through practical network simulation. WSNet simulator
allows researchers to analyze WSN performance based on network configuration
1
http://wsnet.gforge.inria.fr/
119
Chapter 4. Planning the WSN
which can be read from the files that indicate topology, routing and connectivity
information. Users can either use the embedded provided layer modules or extend
the operation by developing customized modules that will be used in the real
application. The network protocol can be the IEEE 802.15.4 standard for physical
layer and MAC layer, or other desired protocols. The energy model should be
programmed previously to imply how energy is consumed for transmitting/receiving
data packets and for different status such as wake, sleep and idle. The key functions
of energy model are in Listing 4.2.
Listing 4.2: Key functions of energy models in WSNet simulator
/* Battery model*/
/* The editable energy comsumption function for
*transmitting and receiving packets, as well as the idle state
*/
void consume_tx(id,duration);
void consume_rx(id,duration);
void consume_idle(id,duration);
/*802.15.4 MAC layer key functions*/
int check_channel_busy(id); /*check the status of channel for a
node*/
int state_machine(id); /* the state assignment for a node*/
As a result the strategy of the modelling method can be described by the
flowchart in Fig. 4.8. When a candidate topology is generated, the node location,
routing and connectivity files are created in a shared folder between the planning
algorithm engine and WSNet simulator. A ’xml’ script is created to configure the
property of WSN for the simulation in WSNet, including the network size, region
scale, network protocols, energy consumption models and directory of the generated
output files. After WSNet finishing the simulation, it returns the values of network
lifetime, packet latency and packet drop rate to the planning algorithm engine, for
analyzing desirability values by the aforementioned formulas (4.4, 4.5 and 4.6).
120
4.4. The proposed multi-objective optimization methodology
User Requirements
Xml configuration file
Lifetime
Packet latency
Log files: connection,
routing table, location
Packet drop rate
Figure 4.8: Modelling of L, Pl and Pd by using WSNet simulator.
4.4
The
proposed
multi-objective
optimization
methodology
Once provided application requirements and the deployment environment model,
the problem of planning a WSN is formulated as: Determine the topology of
the network to maximize the 5 desirability values calculated by (4.2 ∼ 4.6).
This is a multi-objective optimization problem, which is proven to be NP-hard.
Multi-objective optimization genetic algorithms are proposed to effectively and
efficiently solve the NP-hard problems, among which the Nondominated Sorting
Genetic Algorithm II (NSGA-II) by [139] is an ideal approach that features with
elitism selection, high computation efficiency O(M N 2 ) (where M is the number of
objectives and N is the population size) and does not need to specify the sharing
parameter. As a result, the multi-objective optimization method in the present
work is based on NSGA-II. It concerns the constraints and rules on formulating the
genes as well as the mutations and crossovers, to cater to the features of WSN, and
therefore the results are achieved efficiently and effectively.
Fig.
4.9 shows an overview of the proposed planning method.
Network
deployment is generated based on the deployment constraints and user requirements
on the detected regions and forbidden regions, the value of cost is obtained at this
step. There are three ways to create the deployment:
1. User can determine the locations and properties of nodes manually via the
GUI interface;
121
Chapter 4. Planning the WSN
User Requirements
Detected
region
Deployment
constraints
Detection load
Cost
Network deployment
generation
Locations
Coverage
Radio and sensing coverage
analysis
Topology generation
Multi-Objective
Evaluation
Function
L
Pl
Pd
Xml file
Log files
Feedback Loop
Figure 4.9: The strategy of the proposed planning method.
2. Nodes can be randomly generated based on the constraints;
3. Crossover and mutation modify the node properties during the evolutionary
strategy.
Thereafter, radio propagation and sensing signals are computed for each node by
using the ray-tracing method so that the connectivity and sensing coverage are
obtained, and the topology of the WSN is constructed according to the routing
protocol pre-defined by WSN designer. As discussed in section 4.3, the ’xml’ file
and log files are generated to trigger WSNet simulator so that lifetime, packet latency
and packet drop rate are analysed after WSNet finishing simulation. With all those
metrics provided, the multi-objective evaluation method computes the objectives
(desirability) and selects those candidates with best performance base on NSGA-II,
and the selected population are fed back to the network deployment generation
function to create new populations, thus the objectives are gradually progressed. At
the end, the algorithm can provide multiple elitist solutions to WSN designers.
In genetic algorithm, a candidate solution can also be called individual, creature,
or phenotype. Each candidate solution has a set of properties (its chromosomes
122
4.4. The proposed multi-objective optimization methodology
or genotypes) which can be mutated and altered. Traditionally, individuals are
represented in a vector of binary value, but other representations are also possible.
The evolution usually starts from a population of randomly generated individuals
and in each generation, the fitness value of every individual in the population is
evaluated. The individuals with better performance are selected from the current
population, and each individual’s genome is modified (recombined and possibly
randomly mutated) to form a new generation to be evaluated in the next iteration.
Commonly, the algorithm terminates when either a maximum number of generations
has been produced, or a satisfactory fitness level has been reached.
In this work, an individual is expressed as DV represented by (4.7), where Ni is
the node with ID = i and it is a ”chromosome” of DV . M is the number of nodes,
which also indicates the size of WSN. Each ”chromosome” has properties including
3D location of the node, type of node, transmission power Ptx , radio sensitivity RXs
and sensing range Rsense . We assume that M , Ni .location, Ni .type can be modified,
as a result this method is based on changeable length which will bring difficulties to
crossover and mutation. Besides the locations and type must be modified according
to certain rules to construct a valid WSN. The details of the strategy are introduced
and problems are tackled later in this section.
⎧
⎪
⎪
location : x, y, z
⎪
⎪
⎪
⎨ type : BS, SN, RN
DV = [N1 , N2 , · · · NM ] , Ni =
⎪
Ptx , RXs
⎪
⎪
⎪
⎪
⎩ Rsense
⎫
⎪
⎪
⎪
⎪
⎪
⎬
⎪
⎪
⎪
⎪
⎪
⎭
(4.7)
The algorithm defines two types of population: P arent and Children, for storing
the parents and children respectively. The format of population is defined by Np
individuals attached with their corresponding desirability values:
⎡
1
Dcost,c,L,P
l ,Pd
2
Dcost,c,L,Pl ,Pd
DV1 ,
⎢
⎢ DV ,
2
⎢
population = ⎢
⎢ ···
⎣
Np
DVNp , Dcost,c,L,P
l ,Pd
123
⎤
⎥
⎥
⎥
⎥
⎥
⎦
(4.8)
Chapter 4. Planning the WSN
4.4.1
Initialization of individuals
Initial population of candidates are traditionally generated in a random way, and
some of them may occasionally satisfy the constraints and requirements, not to
mention optimizing the performance at the same time. When the scale of region or
the size of WSN becomes large, there will be less chance that an initial candidate
has a valid WSN topology. To efficiently tackle this issue, a high ”quality” initial
seed is generated to guarantee the basic requirements on connectivity and coverage.
The LowCost heuristic proposed in [42] is employed to add a valid individual at the
initial phase:
At the beginning, the Coverage is computed for each deployable point m ∈ At
and the heuristic selects mi that with the maximum Coverage as the best location
and a sensor node Su is then placed on mi . The coverage level of the monitoring
points newly covered by Su is updated, and those points with a sufficient coverage
level are removed from the set of sensing area As . This procedure is repeated until
all the monitoring points are k-covered.
Afterwards, LowCost focuses on the connectivity problem. Let Nu be the node
of unconnected nodes U. The algorithm selects a node Nc in the connected sensor
nodes C that is the closest to Nu and computes the new virtualposition m of
Nu by moving it towards Nc as long as the set of monitoring points initially covered
by Nu remains unchanged. If Nu is still unconnected after changing its position,
extra relay nodes are put on the line between Nu and Nc so that Nu and Nc are
connected.
The resulted initial seed is expected to be better than a randomly generated
seed which do not guarantee the coverage and connectivity. However, as discussed
in Chapter 1, the result does not solve the optimization between connectivity and
cost. Besides the individual generated by LowCost heuristic, the rest individuals are
generated based on the constraints on location with various length (size of WSN),
thus the initial population of parents (P arent) are obtained and evaluated.
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4.4. The proposed multi-objective optimization methodology
I offset
Lcrop
SN
RN
A
I 'offset
Lcrop
I 'offset
Lcrop
B
I offset
Lcrop
A’
B’
Figure 4.10: Crossover with different lengths.
M'tM
M' M
A
A
A’
A’
A’’
A’’
SN
RN
Figure 4.11: Mutation with changeable length.
4.4.2
Crossover and mutation
At each generation, the parents are recombined (crossover) and mutated with
different probability. The demonstrations of crossover and mutation with variable
individual lengths are shown in Fig. 4.10 and Fig. 4.11 respectively.
125
Chapter 4. Planning the WSN
4.4.2.1
Crossover
Two candidates (P arent(i) and P arent(j)) are randomly selected from the
population of parents and crossover occurs between them with a chance of Pco .
Note that the length of both parents might be different, and the crop length Lcrop is
limited by the shorter length: Lcrop < min(Mi , Mj ). And the offsets can be different
in both parents with constraints: Iof f + Lcrop ≤ M . Afterwards, the two generated
children (A and B ) are stored into the population of Children. If crossover does
not happen on the current couple, the two parents are stored into Children directly
for further modification. Similar procedure repeats on other possible pairs of P arent
until Children is filled by individuals.
4.4.2.2
Mutation
Mutation occurs on the individuals of Children. The size of Children(i) is Mi which
mutates with a probability of Pmu . Note that the change on Mi will to some extent
increase the risk of constructing an un-connected topology. The new length Mi is
obtained by randomly selecting a value within the range limited by [Tmin , Tmax ],
where Tmin = max(Mi − T, 1), and Tmax = min(Mi + T, Mmax ). T is a small integer
value and is equal to 2 in our work. Mmax is the maximum number of nodes that
allowed to be used and is determined by user. If Mi is less than Mi , Mi nodes are
randomly picked from Children(i); otherwise, if Mi > Mi , Mi − Mi random nodes
are added to Children(i).
Thereafter mutation happens on each node of Children(i) with probability of
Pmu , on the 3D location and the type of node. The movement of Nj ∈ Children(i)
is limited within the sphere of radius dmax centered at Nj .location and the type can
be selected randomly between SN and RN.
4.4.3
Evaluation based on desirability models and constraints
Routes are searched from each node to the BS by using the Dijkstra’s method.
Several further steps are prosecuted to make the algorithm converge faster: A SN is
changed to RN when it does not cover any point m ∈ As ; A RN is deleted if it does
not act as a router for other nodes; If two nodes of the same type are located too
126
4.5. Experimental results and analysis
close to each other, one of them are moving apart in a similar way as the mutation
on location.
The desirability values of all the 5 metrics are computed and then attached to
each corresponding individual. P arent and Children are mixed so that all the
individuals from both populations are ranked based on nondominated sorting by
NSGA-II. As a WSN must focus on fulfilling the sensing tasks, the desirability of
coverage Dc is considered as the only constraint among the five objectives. By doing
so, if Dci > Dcj , the rank of individual i is always higher than individual j no matter
how the other metrics are; otherwise, the ranking is based on all the desirability
values equivalently, the greater a desirability is, the better the corresponding metric
will be. At the end of each generation, Np best individuals are selected and formulate
new P arent for the next generation.
The evolutionary procedure repeats until the maximum generation is reached.
The proposed method is able to provide multiple WSN deployment solutions with
optimized performance from different aspects.
As a result, it gives designers
flexibility to observe different optimized deployments and assist them making
deployment decision accordingly.
4.5
Experimental results and analysis
The performance of planning algorithm is evaluated through observing the fitness
value and time efficiency compared with other heuristics, as well as checking the
feasibility in real applications. The performance comparisons are realized with three
comparable state-of-the-art algorithms which have 3D computation ability. All the
algorithms including the proposed one are programmed in C++ and they are run on
a PC with Intel Core i5-760 2.8 GHz CPU so that the results are fairly compared.
4.5.1
The impact of maximum number of generation
As the proposed algorithm is based on evolutionary strategy, the larger the number
of generations, the more outstanding the population will evolve. Therefore, we first
evaluate how the maximum number of generations N U MmaxGen impacts on WSN
performance. The application requirements are shown in Fig. 4.12: The 3D map is
the floor plan of CEI-UPM with a scale of 57 m × 16 m × 3 m and a resolution of
127
Chapter 4. Planning the WSN
1 m. The red point indicates the location of BS (17.52 m, 10.02 m, 1.5 m) and blue
rectangles are the sensing areas As which contain 70 points to be covered.
Target region
BS
Target region
Figure 4.12: Configuration of Scenario CEI-UPM.
In this study, the size of population is Np = 8 for both P arent and Children.
Crossover and mutation possibilities are Pco = 0.1 and Pmu = 0.2 respectively. The
data period for each sensor node is 1 s and the simulation lasts 2400 s in WSNet
simulator. The maximum value of generation N U MmaxGen increases from 10 to
150 with a step of 10, hence there are 15 different N U MmaxGen s. The algorithm
runs 5 times for each N U MmaxGen , and as a result 8 × 5 × 15 optimized solutions
are obtained after the simulation. The results are grouped for each N U MmaxGen
and the pareto front is shown in Fig. 4.13, the mean value of each group data
are calculated for each desirability metric, the five metrics construct a plot with five
axes. The area constructed from each group indicates that as N U MmaxGen increases
and the area grows larger, and the overall performance becomes more stable when
N U MmaxGen ≥ 100. Fig. 4.14 indicates that although time consumption fluctuates
at the same N U MmaxGen , consumed time increases approximately linearly with
N U MmaxGen . Therefore, a proper trade-off decision should be made between the
performance and efficiency of computation, and N U MmaxGen = 100 can be selected
for this configuration.
128
4.5. Experimental results and analysis
Cost
Lifetime
Coverage
maxGen
maxGen
maxGen
Packet
latency
Packet
drop rate
Figure 4.13: Desirability values vary with N U MmaxGen .
350
Time Consumption (s)
300
250
200
150
100
50
0
10
20
30
40
50
60
70
80
90 100 110 120 130 140 150
Max number of generation
Figure 4.14: Time consumption varies with N U MmaxGen .
129
Chapter 4. Planning the WSN
Table 4.3: Features of algorithms for comparison
Algorithm
WSN type
Solutions
MOGA
SN,
RN, BS
SN,
RN, BS
SN, BS
Single
LowCost
SN, BS
Single
proposed
WMOGA
4.5.2
Multiple
Single
Radio
Raytracing(RT)
Raytracing(RT)
distance
Line-ofSight(LoS)
Objectives
C
Cost
L
Pl
Pd
Y(RT)
Y
Y
Y
Y
Y(RT)
Y
Y
Y
Y
Y(dist.)
N
Y
N
N
Y(LoS)
Y
N
N
N
Performance comparison with other heuristics
Two comparable heuristics (LowCost [42] and MOGA [43]) are selected and
programmed in the same platform as the proposed algorithm, so that their
performance are fairly evaluated in exactly the same configuration. Besides, a
weighted multi-objective fitness function (WMOGA) developed in our previous work
by [140] is modified and implemented to evaluate the impacts of NSGA-II on the
final solutions, the formula of the weighted function is:
f = w1 DC + w2 Dcost + w3 DL + w4 DPl + w5 DPd
(4.9)
Table 4.3 compares the features of the algorithms, and the proposed method
considers more objectives and more practical modelling for heterogeneous WSN. We
do not analyze the impact of modelling the radio and sensing signal in this thesis,
however it has proved in the previous work, the practical ray-tracing algorithm
outperforms other distance based empirical models.
By setting reflection and
diffraction depth as depth = 0 for the ray-tracing engine, only direct paths are
traced in this evaluation. Therefore the algorithms are compare in time efficiency
and optimization performance.
The population of Children is 8 for the first 3 algorithms, and the parent is 8
for the proposed method, 1 for the other two. N U MmaxGen = 150 for the first 3
algorithms. Three scenarios are tested for all the algorithms and the configurations
are: the first scenario is in the CEI-UPM floor plan, As = 70 and BS.location =
(17.52 m, 10.02 m, 1.5 m) (Fig. 4.12); the second scenario is based on the floor plan
of East Lansing which has a scale of 77.4 m × 36.6 m × 3 m and the resolution is
130
4.5. Experimental results and analysis
1 m. As = 232 and BS.location = (23.43 m, 13.60 m, 1.5 m) (Fig. 4.15); outdoor
region of Madrid city is the third scenario. Its scale is 233.36 m × 297 m × 73.67 m
and resolution is 3 m. As = 310 and BS.location = (107.8 m, 60.1 m, 3 m) (Fig.
4.16).
Target region
BS
Target region
Target region
Figure 4.15: Scenario East Lansing.
The five desirability metrics used by the proposed method are also computed
based on the topologies generated by the other three algorithms, the solutions are
compared according to those values as indicated in Table 4.4, 4.5 and 4.6 respectively.
Red color marks the best value and blue color marks the worst. The performance
of MOGA is limited by using a constant WSN size and unilateral objectives.
WMOGA considers all the objectives without the ability of providing multiple
solutions simultaneously, which explains why this method obtains good optimization
performance. As expected, most of the best results are obtained by the proposed
algorithm, allowing designers making decisions from different aspects to construct
a reliable topology. The computation time of all the algorithms increases as the
scale of scenario grows. LowCost is the most efficient with deterministic heuristic
while it obtains worst performance on some objectives at each scenario. Especially
when there are significant obstacles between the unconnected and connected part of
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Chapter 4. Planning the WSN
Target region
BS
Target region
Figure 4.16: Scenario Madrid.
a WSN, more nodes are placed across the obstacles instead of placing around them
to optimize the cost. The proposed method provides 8 different multi-objective
optimized solutions simultaneously and leads to the highest time consumption at
each scenario. However if divided by 8, the average time consumption per solution
is at least 45% better than MOGA and at least 60% better than WMOGA. As a
result, even by integrating external WSNet simulation in the loop of evolutionary
strategy and with changeable size of WSN, the proposed method is still much more
efficient than MOGA and WMOGA.
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4.6. Conclusion
Table 4.4: Results comparison for Scenario CEI-UPM.
Algorithm
Time (s)
Proposed
255.09
WMOGA
MOGA
LowCost
456.64
67.66
1.3
Objectives
DC DCost
1
0.84
1
0.835
1
0.83
1
0.84
1
0.845
1
0.84
1
0.85
1
0.85
1
0.835
1
0.82
1
0.82
DL
0.24
0.29
0.25
0.22
0.24
0.22
0.26
0.25
0.26
0.28
0.21
D Pl
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.98
0.99
D Pd
0.96
0.86
1
0.92
0.88
0.99
0.98
1
0.98
0.9
0.96
Table 4.5: Results comparison for Scenario East Lansing.
4.6
Algorithm
Time (s)
Proposed
799.32
WMOGA
MOGA
LowCost
581.03
177.14
3.64
Objectives
DC DCost
1
0.805
1
0.81
1
0.83
1
0.81
1
0.815
1
0.815
1
0.82
1
0.815
1
0.815
1
0.82
1
0.82
DL
0.26
0.24
0.21
0.26
0.30
0.30
0.21
0.32
0.36
0.28
0.19
D Pl
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
D Pd
0.91
0.97
0.94
0.92
0.96
0.87
0.89
1
1
0.94
0.96
Conclusion
A planning algorithm is proposed in this thesis, by taking the advantage of proposed
ray-tracing scheme for both radio and sensing signal propagation, the modeling on
coverage and connectivity turns out to be accurate. Moreover, we also consider
the important impact of lifetime and link quality on the WSN, the optimization
is more complete compared with other works. This algorithm is suitable for both
outdoor and indoor environment with the ability to consider deployable area and
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Chapter 4. Planning the WSN
Table 4.6: Results comparison for Scenario Madrid.
Algorithm
Time (s)
Proposed
4048.57
WMOGA
MOGA
LowCost
1236.32
1581.14
35.29
Objectives
DC
DCost
1
0.83
1
0.83
1
0.83
1
0.86
1
0.85
1
0.86
1
0.83
1
0.865
0.99 0.755
1
0.58
1
0.58
DL
0.19
0.18
0.20
0.22
0.21
0.23
0.20
0.20
0.18
0.17
0.05
D Pl
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
D Pd
0.95
0.97
0.95
0.91
1
0.99
0.94
1
1
0.99
0.83
forbidden area. The scalability and probabilistic model of sensing signal are also
very challenging topic in this area, and should be tackled in the future.
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Chapter 5
iMOST: an Intelligent
Multi-objective Optimization
Sensor network planning Tool
iMOST is developed by integrating the introduced algorithms, to assist WSN
designers efficiently planning reliable WSNs for different configurations.
The
abbreviated name iMOST stands for an Intelligent Multi-objective Optimization
Sensor network planning Tool. As mentioned in Section 1.4 of Chapter 1, the
structure of iMOST is illustrated in Fig. 1.9, which is composed of an user friendly
interface and three core functional modules: Image Processing Module (IPM),
Ray-tracing Propagation Module (RPM) and Node Placement Module (NPM).
iMOST contributes on: (1) Efficient and automatic 3D database reconstruction
and fast 3D objects design for both indoor and outdoor environments; (2) It
provides multiple multi-objective optimized 3D deployment solutions and allows
users to configure the network properties, hence it can adapt to various WSN
applications; (3) Deployment solutions in the 3D space and the corresponding
evaluated performance are visually presented to users; and (4) The Node Placement
Module of iMOST is available online as well as the source code of the other
two rebuilt heuristics. Therefore WSN designers will benefit from this tool on
efficiently constructing environment database, practically and efficiently planning
reliable WSNs for both outdoor and indoor applications. With the open source
codes, they are also able to compare their algorithms with ours to contribute this
academic field.
The current version of iMOST works on Win7 operating system and its
mainframe is programmed by using MFC of Visual Studio. The details of important
features of iMOST are introduced in this chapter.
Chapter 5. iMOST: an Intelligent Multi-objective Optimization Sensor
network planning Tool
The mainframe of the user interface is shown in Fig. 5.1. It is consist of
Menu bar, Tool bar where functions can be realized based on user demands and
Demonstration area where 3D view of scenario and WSN evaluation result can be
demonstrated. The meaning of each icon and the corresponding function is indicated
in the figure as well. The IPM is called by the NewMap menu in File option (ref.
to sec. 5.1.1), map will be loaded to the Demonstration area by Open. The tool
bar provides 7 options including: manually place nodes on the loaded map, view the
performance of WSN, define sensing region, construct WSN topology based on the
planning algorithm, reset the scenario and navigate the scenario and deployment in
3D.
5.1
5.1.1
Menu bar
Image Processing Module
By selecting NewMap menu under File, the IPM is triggered and automatic
environment reconstruction algorithm is run for either indoor or outdoor scenario
based on the procedure introduced in Chapter 2. As shown in Fig. 5.2, user should
first define the directory of tested images, the folder of trained database and the
folder of output results. The automatic 3D environment reconstruction method
is launched after user finishing configuration, images are loaded and objects are
recognized automatically. This procedure allows users freely preparing their own
environment and reconstructing the environment via the trained database, so that
the 3D database is constructed efficiently and accurately without spending many
human efforts or high cost compared with conventional methods.
5.1.2
Environment property configuration
Once the 3D environment database is constructed or there is any 3D map available
in ’txt’, ’kml’ or ’3DS’ file with the polygonal description format, the map can be
imported to the tool and demonstrated in the visual interface by clicking Open
menu. During the importing of map, the Environment Property dialog is pop up,
which is shown in Fig. 5.3. It provides opportunity for users setting the environment
properties including the resolution and scale ratio compared with the real world size.
136
5.1. Menu bar
View candidate result
Manual node
deployment mode
Finish configuration
Sensing region define
Clean the deployment
Planning algorithm
Navigation of
scenario
Scenario and result demonstration
IPM: Automatic environment reconstruction
Import available 3D map: ‘txt’, ‘kml’, ‘3DS’ format
Figure 5.1: The mainframe of the planning tool.
Figure 5.2: User command on constructing new map.
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Chapter 5. iMOST: an Intelligent Multi-objective Optimization Sensor
network planning Tool
Figure 5.3: Environment property setting dialog.
The resolution also determines how many points the ray-tracing engine will calculate
for, and the number of possible locations of nodes as well. Therefore, resolution will
impact on the computation time of the algorithms, whereas the scale ratio only
affects the real scale of the scenario and topology of nodes.
5.2
5.2.1
Toolbar
Node deployment
Beyond the automatic topology generation by the proposed planning algorithm,
nodes can be placed by users for other purpose such as to evaluate the performance
of a designed topology. The node deployment button
allows users placing nodes
on determined location, and a Node Property dialog ( see Fig. 5.4) will pop out
each time the left mouse button clicks on the valid region, to allow configuring
the property of the placed node on type: SN, RN and BS, Tx power in dBm,
radio sensitivity Rxs in dBm and if it is a sensor node the sense range can be
set in meters. As expected each node may have different communication ability
and sensing ability defined by users. Different types of nodes are distinguished by
different colors and point sizes in this work, for example red color point with the
largest size
represents a BS, the point with purple color and middle size
RN and yellow color point with the smallest size
5.2.2
indicates a SN.
Network Planning Module
The NPM requires users make 3 steps to configure the network:
138
is a
5.2. Toolbar
Figure 5.4: Node property configuration dialog.
• Define sensing area As . By pressing
, the function of definning sensing
area is triggered. User click the left mouse button on the scenario to mark
the border corners of sensing region as shown in Fig. 4.12. In the current
version, each region is represented by four vertexes to construct a quadrangle.
However, the shape of region is not limited in the real world, this function can
be extended by allowing more vertexes for a region.
• Node pre-deployment. Several nodes can be pre-deployed by users by
pressing
. This tool supports semi-auto and auto planning of WSN, where
semi-auto means that user pre-define parts of WSN on specified locations with
determined properties, whereas only the BS is determined manually in auto
planning mode.
• Terminate user configuration and launch the planning algorithm.
Once the button
is pressed, the configuration is done and user can launch
the proposed planning algorithm by pressing
.
At the end, multiple
multi-objective optimized solutions can be viewed visually by clicking on the
icon
and then selecting the individual ID from the dialog shown in Fig.
5.5. See Fig. 5.6 the results of individual ID = 4 as an example, with the
topology and the value of each objective demonstrated.
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Chapter 5. iMOST: an Intelligent Multi-objective Optimization Sensor
network planning Tool
Figure 5.5: Node configuration dialog.
Coverage
1.00
Cost
0.855
Lifetime
0.22
Packet latency
0.98
Packet drop rate
1
Figure 5.6: Generated topology of individual with ID=4.
5.2.3
Ray-tracing Propagation Module
The RPM can not be visually seen from the mainframe, however it is embedded
in the function behind the icons of view results
and the planning algorithm
. Whenever the radio propagation or sensing coverage is needed, they are all
computed via RPM so that the accuracy and reliability of the estimated result is
guaranteed.
5.2.4
3D navigation
The tool offers the capability to navigate the loaded 3D scenario by pressing
.
The scenario or deployment of WSN can be viewed with different angles of view
and zooming scales. The zooming function is realized by rolling the scroll wheel of
the mouse forward or backward. The view point is adjusted through pressing the
right mouse button and moving the mouse at the same time. The angle of views
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5.2. Toolbar
Figure 5.7: 3D navigation for outdoor scenario.
are changed by pressing the left button and moving the mouse simultaneously. Fig.
5.7 and Fig. 5.8 demonstrate the 3D navigation at outdoor and indoor environment
respectively. The generated topology, the radio rays computed from RPM and the
evaluated network performance can all be shown in 3D view. Moreover, additional
objects in the scenario can be eliminated according to the user requirement. By
doing so, they don’t have any impacts on radio propagation and sensing signal
modelling.
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Chapter 5. iMOST: an Intelligent Multi-objective Optimization Sensor
network planning Tool
Figure 5.8: 3D navigation for indoor scenario.
5.3
Conclusion
iMOST is a tool developed to assist designers planning WSN topology with
optimized performance. It features with high efficiency, flexibility and reliability
with low cost by integrating the proposed IPM, RPM and NPM by this work.
With the ability of automatic 3D outdoor and indoor environment reconstruction,
it significantly reduces economic cost, human efforts and time that spent on this
crucial but non-planning issue. This tool also explores the feasibility of a new
direction of image processing application. User-friendly interface allows designers to
configure the scenario and WSN properties conviniently. It visually demonstrates
the deployment solutions which not only satisfiy the design requirements but also
overall optimize the WSN performance by NPM on sensing coverage, connectivity,
hardware cost, network longevity, packet latency and packet drop rate. The node
placement module and the rebuilt heuristics are accessible through internet so as to
benefit other WSN designers from different aspects. As part of the work, the WSN
deployment solutions will be evaluated under real measurements.
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Chapter 6
Real measurements and results
analysis
Two environment monitoring demonstrations were set up to validate the
performance of the proposed planning algorithm and the developed planning
tool.
The overall methodology, from 3D environment reconstruction and user
configuration to radio propagation, topology generation and performance estimation,
is employed for both demonstrations.
In both tests, ’Cookie’ is equipped with ZigBee communication protocol layer,
environment sensors that are able to sense temperature, light and humidity, and
external antennas made by EAD, see Fig. 6.1. The BKR2400 antenna is 1/2 wave
dipole with 2 dBi peak gain, it has linear polarisation with omni-directional radiation
pattern at horizontal plane. Lithium-based batteries have been used to supply the
energy to the modular nodes during the WSN deployments and experimental tests,
providing up to 500 mAh which covers the power consumption requirements of the
devices. Moreover, they can be charged by using the power supply layer of the
Cookie architecture, so that the autonomy of the nodes is enhanced.
6.1
Aggregation mechanism of measured data
Besides gathering the ambient data, the most important parameters that should
be observed are RSS value of data packets, neighborhood table of each node,
routing table with the BS as destination, battery level and packet delivery states.
The request of those observations is implemented by means of using the HW-SW
co-design platform proposed by [141], which is a framework based on libraries and
controllers that allows designers realizing applications by programming in C code
Chapter 6. Real measurements and results analysis
Figure 6.1: BKR2400 antenna.
and compiling to generate the bitstreams for the microcontroller of sensor node. The
mechanism of aggregating the aforementioned data is described as follows:
1. RSS. Whenever a node Ni receives packets from other nodes, it records the
RSS in dBm, and noted as
RSS(Nj , tk )
Where Nj is the source of a packet, tk ∈ [0, tp ] is the arrival time stamp of the
packet, tp is the overall testing time of WSN.
2. Neighborhood table of each node. The neighborhood table Ti of Ni
contains the IDs of neighbors and corresponding RSS records. The format of
Ti is expressed as following and for simplicity in real application, 10 samples
of RSSs are cached for each neighbor of Ni .
Nj1
Ti =
Nj2
..
.
RSSI(Nj1 , t0 ), RSSI(Nj1 , t1 ), · · · , RSSI(Nj1 , tp ) RSSI(Nj2 , t0 ), RSSI(Nj2 , t1 ), · · · , RSSI(Nj2 , tp )
..
.
3. Routing table of the network. It records for the whole WSN the routes
from each node to BS. Each node maintain the next hop information, so that
at the base station, routing table R of the WSN is constructed:
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6.2. Application interface
Table 6.1: Routing table format.
Source
Destination
Next hop
Ni
BS
Nk
Nj
..
.
BS
Nv
..
.
BS
4. Power consumption (Battery level). This observation is to estimate the
lifetime of the network. By observing battery level, the battery status of each
node can be evaluated. Each node records its battery status BL at every
period tB and reports to the BS every n · tB . By doing so, the battery level
table B of the whole network is expressed as following:
Nj1
B=
Nj2
..
.
BL(Nj1 , tB ), BL(Nj1 , 2tB ), · · · , BL(Nj1 , ntB ) BL(Nj2 , tB ), BL(Nj2 , 2tB ), · · · , BL(Nj2 , ntB )
..
.
5. Format of data packet. The BS gathers sensed data from sensors. Each
SN periodically (Td ) sends data packet to BS, the data packet contains the
following information:
Table 6.2: Packet format.
Source ID
Arrival time stamp
Sequence
Sensed data
TX time stamp
Once the test is terminated, the packet loss rate can be analyzed based on the
continuity of packet sequence for each node, and packet delay of the WSN is
computed from the differences between TX time stamp and arrival time stamp.
Moreover, environmental monitoring data are obtained as well to prove that
those solutions can satisfy the application requirements.
6.2
Application interface
There is usually a user application of observing data and maintain the functions of
nodes in any type of WSN applications. We also developed a user interface for our
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Chapter 6. Real measurements and results analysis
demonstrations to allow simple operations from users. The application interface is
programmed by using JAVA with all the aforementioned function to fetch all the
required information. It works in cooperation with the BS and Fig. 6.2 shows the
structure of the interface.
Figure 6.2: Application interface.
• Data packet is shown once received by BS. As can be seen, the source ID,
TX time, temperature(T), humidity (H), light (L), battery level and sequence
number are included. Arrival time stamp is added at the end.
• Routing table can be generated according to user’s command, which is realized
by clicking ’Generate Routing Table’ button.
• User can select node ID and click the ’Neighbor Table’ to observe the
neighborhood table of a node.
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6.3. Indoor measurements
• Time is synchronized by clicking ’Configure Time’.
• All the information is saved to ’.txt’ file once ’Save Current Information’ button
is pressed.
After preparing all the aforementioned hardware devices, aggregation mechanism
and user interface software are used to monitor the WSN. Two real deployments
are launched for an indoor and an outdoor environment to monitor temperature,
humidity and light level and validate the planned topology and performance
estimated by iMOST.
6.3
Indoor measurements
The first test is realized in the indoor environment of CEI-UPM. The 3D
indoor environment database is constructed based on the Chapter. 2, where the
reconstructed results are analyzed in details. The scanned map is shown in Fig.
6.3(a). The reconstructed result (Fig. 6.3(b)) has 88 planes accurately constructed
and 2 edges misclassified.
Beyond the automatically provided environmental
database, we manually import other 3DS models (Fig. 6.3(c)) to show the presence
of office desks and consider their impacts on the topology planning. Environment
database is loaded to iMOST, resolution is equal to 1.0 m and scale ratio is 1.0.
User requirements on sensing regions are demonstrated in Fig. 6.4, As = 90
and BS.location = (18.73 m, 12.00 m, 1.5 m). Nodes are set with TX power as
−12 dBm, RX sensitivity as −98 dBm, Rsense = 3 m.
A population of planed topologies are generated by iMOST based on the user
configuration with optimized coverage, cost, lifetime, packet latency and packet
drop rate. In this work, we select two candidates for real deployments, which
have topologies indicated in Fig.
6.5(a) and Fig.
6.6(a).
All the nodes are
placed approximately at the locations indicated by the planned solutions and the
constructed topologies are shown in Fig. 6.5(b) and Fig. 6.6(b) respectively. The
routing from N5 to BS in the planned solution is via N4 whereas it routes though
N8 in the real deployments. Except N5 , the remaining nodes have the same next
hop as indicated by the iMOST solutions.
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Chapter 6. Real measurements and results analysis
(a) Original scanned floor plan of CEI-UPM
(b) Automatic
reconstructed result
(c) 3DS imported
models
(c) 3D view of the scenario with furniture
Figure 6.3: Indoor modelling by using iMOST: automatic 3D reconstruction+3DS
models.
The evaluated performance of the two candidate solutions are shown in Table
.6.3, topology 1 performs better than topology 2 in terms of cost, lifetime and data
drop rate.
Table 6.4 shows the measured data of topology 1 in details. The detected
neighbors and corresponding RSS values are shown (the first sub-row) for each
node (N1 -N17 ) in the scenario.
All the detected RSS values are computed by
averaging the fetched samples along different time, and they are compared with
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6.3. Indoor measurements
Figure 6.4: User requirement over the indoor test.
(a) iMOST solution
1
BS
2
8
11
12 13
14
15
16
3
9
4
5
6
7
10
17
(b) Real topology
Figure 6.5: Topology comparison 1: (a) One of the eight solutions generated by
iMOST for CEI-UPM. (b) The topology of real deployment.
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Chapter 6. Real measurements and results analysis
(a) iMOST solution
1
BS
2
3
4
5
6
7
18
8
11
12 13
14
15
16
9
10
17
(b) Real topology
Figure 6.6: Topology comparison 2: (a) Another solution generated by iMOST for
CEI-UPM. (b) The topology of the corresponding real deployment.
Table 6.3: Evaluated performance of the two candidates.
Topology solution
1
2
Objectives
DC DCost DL
1
0.83
0.432
1
0.825 0.428
D Pl
0.99
0.99
D Pd
0.99
0.95
the simulation results, by setting the ray-tracing engine with maximum reflection
depth depth = 0(the second sub-row) and depth = 3 (the third sub-row). The
number of traced rays grows as depth increases, however the results will be more
accurate as more multi-path effects are considered.
As can be seen, there are some errors in discovered neighbors of some nodes (e.
g.N2 and N14 ), and the errors are marked by red color. Assuming that N (u) is the
set of actual neighbors of a node u, and N (u) the set of neighbors known to u (i.e.
whose identifier is present in its neighborhood table). Neighborhood accuracy Accnb
is the average value of the accumulation for all the nodes in the WSN the proportion
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6.3. Indoor measurements
of actual neighbors of node that have been indeed detected. It is formulated as (6.1):
|N (u) N (u)|
u=1
|N (u)|
M
Accnb =
M
(6.1)
× 100%.
Accordingly, we have the accuracy of neighborhood Accnb = 88% in this case
study. The main reasons of this error are from the embedded HELLO protocol of the
ZigBee layer. As N2 cannot detect N7 and N8 while N7 and N8 is able to discover
N2 as their neighbor, the similar reason can be applied for the neighborhood of
N5,6,10,12,13,14,16 .
Fig.
6.7 demonstrates graphically RSS values of the measured data and
simulation results when depth = 0 and depth = 3, by aligning the actual neighbors
for each node (from N1 to N17 ). Table 6.4 shows the mean error (M E) and standard
deviation error (STD) for those two simulations. When depth = 0, M E0 = 4.29 dB
and ST D0 = 5.06 dB; If depth = 3, M E3 = 3.80 dB and ST D3 = 3.61 dB, which as
expected, performs better (11% reduction in M E and 28.6% reduction in ST D) than
the former case. Therefore WSN designer should make a trade-off decision between
the time consumption and accuracy, if the planning strategy can be realized without
time constraint, this work suggests users configure ray tracing engine with depth > 0
to improve the accuracy of topology estimation.
This scenario is tested on 22nd and 23rd November, 2013.
Sensors detect
environment and send data packet every one minute, the battery status is reported
from each node to BS every one minute as well. This traffic load mechanism aims to
speedup the battery consumption with a ratio of 120 times faster than the simulation
period (100 days). Therefore network longevity is predicted as 8.64 hours and we
should change battery since that moment. The curves of battery consumption shown
in Fig. 6.8 are the measured remaining energy of N1,8,14 varied along working time
of WSN. According to the test N1 has the lowest lifetime around 8.3 hours, which
obtains 96.1% of match with the predicted value the estimated result and indicates
a good performance of the proposed work on lifetime modelling in iMOST.
Fig. 6.9 compares packet delivery status in a period of 30 minutes for topology 1.
N7 has the highest packet loss rate (20%) because it needs the maximum number of
hops to reach BS compared with other nodes. The average packet latency is around
3.5 s therefore Dpl ≈ 0.97 which is slightly less (3%) than the estimated result.
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Chapter 6. Real measurements and results analysis
BS(-73.14),1(-69.40),2(-67.37),3(-63.72),4(-66.81),5(-72.12),6(-75.28),7(-78.03),8(-62.21),10(-60.28)
BS(-83.58),1(-68.95),2(-66.33),3(-64.00),4(-66.76),5(-72.06),6(-75.27),7(-78.01),8(-62.74),10(-60.25)
BS(-56.00),1(-72.00),2(-64.00),3(-66.00),5(-64.00),6(-72.50),7(-69.00),8(-69.00),9(-56.00)
BS(-75.80),1(-72.00),2(-54.25),3(-67.37),4(-63.82),5(-69.43),6(-72.25),7(-74.75),8(-68.07),9(-60.28)
BS(-68.89),1(-72.03),2(-69.95),3(-66.96),4(-63.85),5(-68.99),6(-72.15),7(-74.77),8(-68.72),9(-59.58)
12(-59.00),13(-67.00),14(-64.00),15(-71.50),16(-73.00),17(-79.00)
BS(-75.34),12(-54.25),13(-65.99),14(-68.13),15(-73.25),16(-75.59),17(-77.67)
BS(-74.90),12(-54.27),13(-67.00),14(-64.76),15(-74.42),16(-75.60),17(-78.90)
11(-59.00),13(-60.00),15(-81.00),16(-75.00),17(-81.00)
BS(-73.75),11(-54.25),13(-62.41),14(-65.24),15(-71.26),16(-73.81),17(-76.05)
BS(-73.30),11(-54.27),13(-62.32),14(-62.95),15(-71.71),16(-74.72),17(-77.28)
11(-64.00),12(-62.00),14(-56.00),15(-69.00),16(-65.00),17(-69.00)
BS(-72.62),11(-65.99),12(-62.41),14(-56.87),15(-66.37),16(-69.85),17(-72.68)
BS(-73.12),11(-66.24),12(-62.61).14(-55.71),15(-66.10),16(-69.49),17(-73.04)
11(-64.00),13(-56.00),15(-54.00),17(-72.00)
BS(-69.82),11(-68.13),12(-65.24),13(-56.87),15(-63.19),16(-67.38),17(-70.60)
BS(-69.54),11(-66.30),12(-65.30),13(-56.61),15(-59.55),16(-67.81),17(-69.74)
BS(-76.00),11(-75.00),12(-81.00),13(-69.00),14(-54.00),16(-58.00),17(-67.00)
BS(-67.94),11(-74.00),12(-72.01),13(-67.12),14(-63.94),16(-57.78),17(-64.55)
BS(-67.83),11(-77.01),12(-71.71),13(-69.27),14(-61.73),16(-57.67),17(-64.48)
11(-73.00),12(-75.00),13(-64.00),15(-56.00),17(-55.00)
BS(-67.03),11(-76.33),12(-74.56),13(-70.60),14(-68.13),15(-57.78),17(-57.78)
BS(-66.94),11(-76.14),12(-74.72),13(-69.99),14(-63.97),15(-57.10),17(-57.52)
BS(-70.00),11(-77.00),12(-80.00),13(-71.00),14(-72.00),15(-66.00),16(-55.00)
BS(-69.31),11(-78.42),12(-76.80),13(-73.43),14(-71.35),15(-64.55),16(-57.78)
BS(-70.32),11(-78.89),12(-77.29),13(-74.32),14(-72.24),15(-64.46),16(-58.17)
BS(-69.00),2(-55.00),3(-65.00),4(-70.50),5(-78.00),6(-79.00),7(-84.00),8(-63.00),9(-65.00),10(-83.00)
BS(-68.98),2(-54.25),3(-64.55),4(-68.82),5(-76.35),6(-78.43),7(-80.68),8(-65.32),9(-69.40),10(-72.00)
BS(-68.85),2(-54.27),3(-64.51),4(-70.05),5(-86.31),6(-88.52),7(-90.52),8(-65.68),9(-69.31),10(-82.08)
BS(-74.00),1(-55.00),3(-48.00),4(-65.67),5(-79.00),6(-73.00),9(-65.00),10(-70.00)
BS(-71.10),1(-54.25),3(-60.28),4(-65.88),5(-74.58),6(-76.82),7(-79.21),8(-64.57),9(-67.37),10(-69.99)
BS(-72.85),1(-55.67),3(-59.62),4(-66.11),5(-85.83),6(-86.68),7(-89.21),8(-64.74),9(-67.42),10(-69.95)
BS(-72.00),1(-63.50),2(-62.00),4(-61.75),5(-75.00),6(-69.50),7(-78.25),8(-71.00),9(-63.50),10(-69.00)
BS(-74.12),1(-64.55),2(-60.28),4(-57.78),5(-71.35),6(-74.02),7(-76.80),8(-67.99),9(-63.72),10(-67.37)
BS(-74.94),1(-64.01),2(-60.23),4(-57.84),5(-81.80),6(-84.00),7(-76.8),8(-88.05),9(-53.00),10(-68.40)
BS(-70.00),1(-74.00),2(-69.00),3(-61.00),5(-66.00),6(-74.00),7(-77.00),8(-73.00),9(-63.00),10(-64.50)
BS(-76.27),1(-68.82),2(-65.88),3(-57.78),5(-68.13),6(-71.35),7(-74.56),8(-69.24),9(-66.81),10(-63.82)
BS(-74.89),1(-70.05),2(-66.23),3(-57.84),5(-68.22),6(-71.15),7(-75.07),8(-69.15),9(-65.56),10(-63.96)
BS(-78.50),1(-86.00),3(-76.00),4(-62.25),7(-55.00),8(-81.00),9(-72.33),10(-73.00)
BS(-80.98),1(-76.35),2(-74.58),3(-71.35),4(-68.13),6(-57.78),7(-65.99),8(-76.26),9(-72.12),10(-69.43)
BS(-80.32),1(-86.63),2(-84.33),3(-72.17),4(-68.22),6(-57.71),7(-60.45),8(-76.52),9(-72.06),10(-69.66)
BS(-72.00),1(-83.00),3(-72.00),4(-74.30),5(-55.00),7(-45.00),9(-74.00),10(-70.00)
BS(-81.85),1(-78.43),2(-76.82),3(-74.02),4(-71.35),5(-57.78),7(-60.58),8(-77.55),9(-75.28),10(-72.25)
BS(-79.33),1(-78.52),2(-77.71),3(-74.49),4(-71.15),5(-57.71),7(-56.86),8(-77.31),9(-75.27),10(-72.14)
BS(-43.00),1(-91.00),2(-87.60),3(-80.33),4(-75.00),5(-54.25),6(-44.00),8(-79.50),9(-76.00),10(-71.00)
BS(-84.42),1(-80.68),2(-79.21),3(-76.80),4(-74.56),5(-65.99),6(-60.58),8(-79.89),9(-78.03),10(-74.75)
BS(-54.30),1(-80.33),2(-80.87),3(-76.80),4(-75.05),5(-60.45),6(-56.85),8(-79.69),9(-76.39),10(-74.77)
BS(-68.00),1(-60.00),2(-65.00),3(-73.00),4(-77.00),5(-76.00),6(-71.00),7(-76.50),9(-52.00),10(-76.00)
BS(-69.23),1(-65.32),2(-64.57),3(-67.99),4(-69.24),5(-76.26),6(-77.55),7(-79.89),9(-62.21),10(-68.07)
BS(-69.04),1(-65.68),2(-63.70),3(-68.37),4(-69.10),5(-76.22),6(-77.84),7(-79.69),9(-62.37),10(-68.09)
BS(-83.00),1(-67.50),2(-56.00),3(-58.00),4(-66.00),5(-75.00),6(-75.00),7(-76.00),8(-65.50),10(-64.50)
Neighbor ID and Average RSSs (dBm)
88%
Accnb
ME3 = 3.80
STD3 = 3.61
ME0 = 4.29
STD0 = 5.06
RSS ME, STD
Table 6.4: Neighborhood table and RSS comparisons between real measurement and simulation results: indoor scenario.
Node ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
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6.3. Indoor measurements
-30
Real measurement
Simulation result depth=0
Simulation result depth=3
-40
RSS (dBm)
-50
-60
-70
-80
-90
-100
0
20
40
60
80
Measured value index aligned in the table
100
120
140
Figure 6.7: RSS comparison between real measurement and simulation result with
depth = 0 and depth = 3.
100
node 1
node 8
node 14
90
80
Remaining energy (%)
70
60
50
40
30
20
10
0
0
1
2
3
4
5
Working time (hours)
6
7
8
9
Figure 6.8: Remaining energy of N1 , N8 and N14 along the working time of WSN.
The packet drop rate is calculated as the proportion between lost data packets and
total number of packets that have been sent in the WSN. According to the observed
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Chapter 6. Real measurements and results analysis
packet sequence number for each node, there is 10% of packet loss in this case, thus
the desirability metric DPd = 90%. Compared with the estimated result (99%), the
error is approximately 9%.
25
Number of packets
Arrived packets
Sent packets
20
15
10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Node ID
Figure 6.9: Comparing the number of arrived packet and the number of sent packet
for each node in the deployment.
The three nodes: N1,8,14 are the bottlenecks of this deployment. Although N1
has less children than N8 and same as N14 , the accumulated dropped packets hoping
through N1 are less than that via N8 and N14 . As a result, N1 should forward more
data packets to BS and a faster exhaustion on the battery energy occurs, which also
reveals why (see Fig. 6.8) the lifetime of N1 is shorter than other nodes.
The three nodes N1,8,14 are the bottleneck of the deployment, although N1 has
less children than N8 and same as N14 , the accumulated packet loss rate hoping
through N1 is less than that via N8 and N14 . As a result, N1 should forward more
154
6.4. Outdoor measurements
data packets to BS and a faster exhaustion on the battery energy occurs, which also
reveals why (see Fig. 6.8) the lifetime of N1 is shorter than other nodes.
Sensed data are gathered at the coordinator and the environment variation is
recorded for each sensor. Fig. 6.10 shows the variation of temperature, humidity
and light in the room where N4 is placed, and the observation time is from 16 : 00
to 21 : 00 on 22nd Nov, 2013. The average temperature is 29.25◦ C, humidity is
47.13 without significant changes along time. While the light level suddenly reduces
from 90.35% (3700) to 0% (0) at 20:30, it indicates that the light is turned off and
people may have left room after this moment.
50
Measured level
40
30
20
10
0
16
Temperature
Humidity
Light (x0.01)
17
18
19
20
Time of the day (from 16hr~21hr)
21
Figure 6.10: The sensed data of N4 .
6.4
Outdoor measurements
The second test is realized to monitor the outdoor environment at the parking lot of
UPM. The bird’s view of the environment is taken from Google Maps, as shown in
Fig. 6.11(a), the automatic image understanding algorithm, using Madrid training
database, provides the recognition result shown in Fig. 6.11(b) which achieves 93.3%
155
Chapter 6. Real measurements and results analysis
of accuracy compared with the ground truth. It is expected to assigned uniform
(a) Original image
(b) Image recognition result
(c) 3D view of the reconstructed result by adding terrain information from Google Earth
Figure 6.11:
Outdoor modelling
reconstruction+terrain information.
by
using
iMOST:
automatic
3D
height to the building, while in this case we register the terrain information of this
area is available, and therefore it was used to enhance the performance. The 3D
view of the reconstructed model is shown in Fig. 6.11(c). This 3D environment
modelling took 6.83 minutes to reconstruct a region of 233.36 m × 297 m × 73.67 m.
156
6.4. Outdoor measurements
Table 6.5: Evaluated performance of the selected candidate.
DC
1
Objectives
DCost DL
D Pl
0.91
0.155 0.99
D Pd
0.98
User configuration on sensing area for this test is As = 99 and TX power for
all the nodes are set as 4 dBm which is the maximum communication ability of the
antenna. Nodes are set with RX sensitivity as −98 dBm, Rsense = 8 m. iMOST
generates topology (Fig. 6.12(a)) accordingly, and the candidate, which with the
lowest cost and competitive desirability values of other metrics, is selected in this
case. The predicted performance is shown in Table. 6.5. By placing 9 nodes in
target locations, the real topology is shown in Fig. 6.12(b). N1 ∼ N4 are, as
estimated, directly connected with BS, N7 routes via N4 rather than through N6 in
the solution.
Table 6.6 shows the measured data of the deployed topology in details. All the
detected RSS values are computed by averaging the fetched samples along different
time, and they are compared with the simulation results with depth = 3 (the second
sub-row). N5 and N8 can not discover each other in the real deployment while
the simulated result shows connections between them, and therefore Accnb = 98%.
Fig. 6.13 demonstrates graphically RSS values of the measured data and simulation
results when depth = 3, by aligning the actual neighbors for each node (from N1 to
N9 ). Table 6.6 indicates that in this case M E3 = 2.45 dB and ST D3 = 2.45 dB.
As the outdoor environment is an open area without significant obstacles between
nodes, this performance is slightly better than indoor performance. Both indoor
and outdoor neighborhood results indicate that the proposed modelling methods on
radio propagation and link establishment are practical and reliable to be applied in
real deployments.
This scenario is tested on 23rd November, 2013. Sensing period and battery
period are one minute. The estimated network longevity is predicted as 1.86 hours
with the maximum transmission power. The curves of battery consumptions shown
in Fig. 6.14 are the measured remaining energy of N4,5,2 varying along working time
of WSN. According to the test, N4 has the lowest lifetime (1.95 hours), which is
4.8% higher than the estimated result. N5 is in charge of forwarding packets only
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Chapter 6. Real measurements and results analysis
BS(-61.00),2(-61.50),3(-67.00),4(-75.00),5(-70.00),6(-79.00),7(-78.00),8(-80.00)
BS(-65.65),2(-62.26),3(-68.25),4(-71.40),5(-74.47),6(-76.41),7(-77.99),8(-81.13)
BS(-77.00),1(-62.30),3(-58.00),4(-68.00),5(-76.00),6(-74.00),7(-76.00),8(-75.00),9(-86.00)
BS(-72.45),1(-62.25),3(-62.19),4(-67.69),5(-75.17),6(-74.53),7(-76.29),8(-78.85),9(-88.02)
BS(-57.00),1(-68.50),2(-59.00),4(-62.65),5(-66.00),6(-71.00),7(-75.00),8(-73.00),9(-86.00)
BS(-68.11),1(-68.24),2(-62.20),4(-61.15),5(-68.86),6(-72.11),7(-74.51),8(-72.31),9(-80.15)
BS(-79.00),1(-74.00),2(-67.00),3(-63.00),5(-70.00),6(-71.00),7(-70.00),8(-77.00),9(-85.00)
BS(-71.18),1(-71.44),2(-67.69),3(-61.17),5(-64.31),6(-69.24),7(-72.45),8(-84.86),9(-85.83)
BS(-73.00),1(-75.50),2(-73.00),3(-67.00),4(-68.00),6(-63.00),7(-69.00),9(-83.00)
BS(-74.42),1(-74.57),2(-72.19),3(-68.87),4(-64.31),6(-61.96),7(-68.21),8(-69.44),9(-83.56)
BS(-77.00),1(-81.00),2(-74.00),3(-69.00),4(-70.33),5(-65.00),7(-60.00),8(-64.00),9(-78.00)
BS(-76.27),1(-76.39),2(-74.51),3(-72.11),4(-69.24),5(-61.99),7(-62.44),8(-66.05),9(-79.89)
BS(-79.00),1(-76.00),2(-72.00),3(-76.00),4(-67.00),5(-69.00),6(-59.00),8(-63.00),9(-63.00)
BS(-77.81),1(-77.96),2(-76.42),3(-74.55),4(-72.46),5(-68.21),6(-62.45),8(-62.08),9(-68.10)
1(-80.00),2(-76.00),3(-78.00),4(-86.00),6(-65.00),7(-61.00),9(-61.00)
1(-80.95),2(-88.60),3(-73.21),4(-84.86),5(-69.44),6(-65.95),7(-62.09),9(-62.07)
2(-85.00),3(-79.00),4(-85.00),5(-82.00),6(-77.00),7(-67.00),8(-61.00)
2(-88.21),3(-80.23),4(-85.83),5(-83.56),6(-69.72),7(-68.09),8(-62.08)
Neighbor ID and Average RSSs (dBm)
99%
Accnb
STD3 = 2.45
ME3 = 2.45
RSS ME, STD
Table 6.6: Neighborhood table and RSS comparisons between real measurement and simulation results: outdoor scenario.
Node ID
1
2
3
4
5
6
7
8
9
158
6.4. Outdoor measurements
for N6 , therefore within the same time it consumes less energy than N4 . While N2
9
8
7
6
4
5
3
2
1
(a) iMOST solution with the lowest cost
9
8
7
6
5
4
3
2
1
(b) Real topology
Figure 6.12: Topology comparison: (a) One of the eight solutions generated by
iMOST for outdoor environment. (b) The topology of real deployment.
159
Chapter 6. Real measurements and results analysis
-30
Real measurement
Simulation result depth=3
-40
RSS (dBm)
-50
-60
-70
-80
-90
-100
0
10
20
30
40
50
Measured value index aligned in the table
60
70
80
Figure 6.13: RSS comparison between real measurement and simulation result with
depth = 3.
100
90
80
Remaining energy (%)
70
60
50
40
30
20
10
0
0
node 4
node 5
node 2
0.1
0.2
0.3
0.4
0.5
Working time (hours)
0.6
0.7
0.8
0.9
1
Figure 6.14: Remaining energy of N4 , N5 and N2 along the working time of WSN.
sends packets only for itself, compared with N4 and N5 , it has the longest battery
life.
160
6.5. Conclusion
38
Arrived packets
Sent packets
Number of packets
36
34
32
30
28
26
1
2
3
4
5
6
Node ID
7
8
9
Figure 6.15: Comparing the number of arrived packet and the number of sent packet
for each node in the deployment.
Fig. 6.15 compares packet delivery status in a period of 0.5 hour for the topology.
DPd = 99% which is better than the estimated result of 98%. N9 and N8 , which
perform similarly as N7 in the indoor measurement, have the highest packet loss
rate (10%), because they also need more number of hops to reach BS compared
with other nodes. The average packet latency is around 2.6 s, therefore Dpl ≈ 0.97
which is slightly less (2%) than the estimated result.
6.5
Conclusion
The whole methodology introduced through this thesis is comprehensively validated
in this chapter, by launching real WSN deployments.
161
Chapter 6. Real measurements and results analysis
3D environment models are constructed automatically for both indoor and
outdoor scenarios. Real deployments for ambient monitoring are realized for target
regions. Experiments are carefully designed by programming the sensor nodes coping
with the application requirements, and the placements of nodes strictly follow the
planned topologies generated by iMOST.
The experimental data are categorized and compared in terms of environment
reconstruction accuracy, routing table accuracy, neighborhood table accuracy, RSS
accuracy, lifetime and packet delivery status. Both applications show the potentials
of the proposed algorithm and the developed planning tool to fulfill user requirements
with optimized and practical performance.
162
Chapter 7
Conclusions and future works
7.1
Conclusions
In this thesis, a series of novel methodologies have been proposed, in terms of
environment modelling, metric modelling, network evaluation and multi-objective
optimized planning, for efficiently planning reliable WSNs. A planning tool iMOST
is developed by integrating those proposed methodologies to assist WSN designers
conveniently constructing and evaluating network topology for outdoor and indoor
environments.
The proposed automatic 3D outdoor and indoor environment modelling
methodology liberates WSN designers and wireless communication engineers from
the traditional time consuming and costly approaches, in which environment is
usually purchased from professional GIS companies or manually reconstructing
from field measurements. With the best average accuracy of 76.1% for outdoor
image understanding within one hour and 97% accuracy for indoor recognition in
less than one minute, this method is capable to recognize and segment objects
from images pixel wisely with sufficient accuracy. The 3D vectorization procedure
eliminates redundant information thus objects are stored succinctly to reduce
memory occupations.
Moreover we also prove its flexibility to be applied for
different outdoor environments by training customized environment databases and
the robustness for indoor reconstruction. To our best knowledge, this is the first
time that image understanding algorithm being applied to automatically reconstruct
environment database for signal propagation and network planning purpose. This
novel approach allows reconstructing large scale 3D map in a very short time,
accurately and for free.
Based on the practical and accurate 3D modelling, the ray tracing engine is
developed to tracing the radio and sensing signal path. The experimental results
Chapter 7. Conclusions and future works
proved the improvement on computation efficiency (in average 335%) by using
the kd-tree space division algorithm and modified polar sweep algorithm. The
radio propagation model is proposed for ray tracing engine, which emphasizes not
only the materials of obstacles but also their locations along the signal path and
the performance is evaluated through comparison with both indoor and outdoor
measurement data, and the mean error is less than 2.2 dB in outdoor test and
less than 2.42 dB for indoor scenarios, which outperforms than the compared
state-of-the-art works. The sensing signal of sensor nodes, which are sensitive to
the obstacles, is benefit from the ray-tracing algorithm via obstacles detection.
The performance of this modelling method is robust and accurate compared with
conventional methods and experimental results imply that this methodology is
suitable for both outdoor urban scenes and indoor environments, moreover it can be
applied to GSM communication and ZigBee protocol by varying frequency parameter
in the radio propagation model.
An automatic 3D multi-objective optimization WSN planning algorithm is
proposed in this work. More comprehensive metrics (connectivity, coverage, cost,
lifetime, packet latency and packet drop rate) are modeled practically compared
with other works, especially 3D ray tracing method are used to model the radio
link and sensing signal which are sensitive to the obstruction of obstacles; routing
of network is constructed by using AODV protocol; the network longevity, packet
delay and packet drop rate are obtained via simulating practical events in WSNet
simulator, which to the best of our knowledge, is the first time that network simulator
is involved in a planning algorithm. Moreover the multi-objective optimization
methodology is developed to cater the characteristics of WSNs. The individual
length is changeable so that the cost can be optimized, meanwhile crossovers
and mutations are designed to eliminate invalid modifications to improve the
computation efficiency. The capability of providing multiple optimized solutions
simultaneously allows users making their own decisions, and the results are more
comprehensive optimized compared with other state-of-the-art algorithms.
iMOST is developed by integrating the introduced novel algorithms, to assist
WSN designers efficiently planning reliable WSNs for different configurations.
iMOST features with convenient operation with user-friend vision system allow users
configuring the network properties freely; It supports the efficient and automatic
164
7.2. Future works
3D database reconstruction algorithm and fast 3D objects design for both indoor
and outdoor environments; The multiple multi-objective optimized 3D deployment
solutions in the 3D space and the corresponding evaluated performance are visually
presented to users; and the NPM of iMOST is available online as well as the source
codes of the other two rebuilt advanced heuristics. Therefore WSN designers will be
benefit from this tool on efficiently constructing environment database, practically
and efficiently planning reliable WSNs for both outdoor and indoor applications,
efficiently and accurately estimate the performance of a WSN. With the open source
codes, they are also able to compare their algorithms with ours to make contributions
to this academic field.
The whole methodology introduced through this thesis is comprehensively
validated, by launching real WSN deployments for both indoor and outdoor
environment. Experiments are carefully designed by programming the sensor nodes
coping with the application requirements, and the placements of nodes strictly
follow the planned topologies generated by iMOST. The environment reconstruction
accuracy, routing table accuracy, neighborhood table accuracy, RSS accuracy,
lifetime and packet delivery status are computed and analyzed through comparisons.
The results indicate that the proposed methodologies and the developed planning
tool iMOST are able to assist WSN designers efficiently planning reliable and
optimized WSN topology for both indoor and outdoor scenarios.
7.2
Future works
In the future, this work can be continued from different aspects: First of all, the
image understanding result for indoor environment can be improved. By observing
different types of line segments with different thickness, the materials of internal
walls can be distinguished and different dielectric parameters can automatically
assigned instead of using uniform parameter by the current work. More training
database should be constructed for different kinds of environments. Besides the
choices of some parameters in outdoor image understanding are arbitrary, which
leads to uncertainty in the performance and hence deep insight study over those
parameters will contribute to the work.
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Chapter 7. Conclusions and future works
Secondly, computer science knowledge shall be used in the future to optimize
the computation method for large scale network planning and large scenario
demonstration. When scenario becomes larger, more points are involved in the
computation which will increase time spent on searching proper placements. One
proposal to this problem is the partition computation: each detected region is
extracted and only the areas with high deployment possibility are concerned. By
doing so, the planning algorithm does not have to traverse the entire environment
database and the speed is improved.
There are several issues that we have not yet explored in this work, including
planning mobile sensor networks, the study of human movements on the network
performance and the interference from other wireless signals over the deployed
environment and the antenna radiation pattern towards the radio propagation
modelling. Therefore more efforts should be done to make contribution to this
academic field and bring more benefits to WSN designers.
7.3
Publications based on this work
This section shows the complete list of publications resulting from this thesis:
Refereed journal papers
1. Danping He, Guixuan Liang, Jorge Portilla, Teresa Riesgo, A Novel Method
for Radio Propagation Simulation Based on Automatic 3D Environment
Reconstruction. Radioengineering. 21 - 1, pp. 985 - 992. 12/2012. ISSN
1210-2512. (Invited paper)
2. Danping He, Gabriel Mujica, Guixuan Liang, Jorge Portilla, Teresa Riesgo,
Radio Propagation Modeling and Real Test of ZigBee Based Indoor Wireless
Sensor Networks.
Submitted to JSA. (Invited paper by Journal of
Systems Architecture: Embedded Software Design (JSA))
3. Danping He, Gabriel Mujica, Jorge Portilla, Teresa Riesgo, Modelling
and planning reliable wireless sensor networks based on multi-objective
optimization genetic algorithm with changeable length. Submitted to Journal
of Heuristics.
166
7.3. Publications based on this work
Refereed conference papers
1. Danping He, Nathalie Mitton, David Simplot-Ryl, An Energy Efficient
Adaptive HELLO Algorithm for Mobile Ad Hoc Networks. In Proceedings
of the 16th ACM international conference on Modeling, analysis & simulation
of wireless and mobile systems (MSWiM ’13), pp. 65 - 72, 2013.
2. Danping He, Gabriel Mujica, Guixuan Liang, Jorge Portilla, Teresa Riesgo,
Radio Propagation Modeling and Measurements for ZigBee Based Indoor
Wireless Sensor Networks. In Proceedings of the Jornadas de Computacin
empotrada, pp. 98 - 103, 2013. Best paper award, invited to publish
on JSA
3. Danping He, Jorge Portilla, Teresa Riesgo, A 3D Multi-objective
Optimization Planning Algorithm for Wireless Sensor Networks. Published
in IECON, 2013.
4. Danping He, Guixuan Liang, Jorge Portilla, Teresa Riesgo, A Novel Method
for Radio Propagation Simulation Based on Automatic 3D Environment
Reconstruction. In Proc. 6th European Conf. Antennas and Propagation
(EUCAP), pp. 1445 - 1449, 2012. Invited to publish at special issue
of Radioengineering Journal
5. Danping He, Gabriel Mujica, Jorge Portilla, Teresa Riesgo, Simulation Tool
and Case Study for Planning Wireless Sensor Network. In Proceedings of
Annual Conference of the IEEE Industrial Electronics Society (IECON), pp.
6028 - 6032, 2012.
6. Guixuan Liang, Danping He, Jorge Portilla, Teresa Riesgo, A Hardware In
The Loop Design Methodology For FPGA System and Its Application To
Complex Functions. In Proceedings of VLSI, Design, Automation and Test
(VLSI-DAT ), pp. 1 - 6, 2012.
7. Guixuan Liang, Danping He, Jorge Portilla, Teresa Riesgo, Functional
Validation of MB-OFDM System Using HW-in the loop. In Proceedings of
Conference on Design of Circuits and Integrated Systems (DCIS), pp. 131 136, 2012
167
Chapter 7. Conclusions and future works
8. Guixuan Liang, Danping He, Eduardo de la Torre, Teresa Riesgo,
Low-power, High-speed FFT Processor for MB-OFDMUWB Application.
Microtechnologies for the New Millennium 2011 (SPIE), 2011.
7.4
Implementation of this work
This work is implemented in the Working Package 5 of European project
WSN-DPCM, which is funded by the ARTEMIS Joint Undertaking (the European
technology platform representing the field of advanced research and technology for
embedded intelligence and systems), national authorities and European partner
companies with a total amount of 3.4 million euros. It is officially launched in
October 2011 and lasts for 36 months [142].
With the cooperation of several
technical universities and companies from Spain, Italy, Lithuania and Greece,
the project targets to address the WSN deployment, testing, and maintenance
challenging issues by developing an integrated platform for smart environments that
will comprise a middleware for heterogeneous wireless technologies as well as an
integrated engineering tool for quick system development, a planning tool and a
commissioning & maintenance tool for expert and non-expert users. The Working
Package 5 (WP5) of the WSN-DPCM project aims at developing the WSN visual
modeling, simulation and deployment tool for the optimal target WSN deployment
scheme definition. In the proposal, the planning tool should have rich graphical
end-user interface with high level of automation, supporting various proposals such
as models of WSN motes, network topology, communication models, environmental
conditions and deployment environments.
With the assistance of the planning
tool, WSN developers are expected to be facilitated during the WSN distributed
application design for investigating alternative deployment schemes in a transparent
and guided way, thus saving efforts and time.
168
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