Technische Universität Darmstadt
Dependable, Embedded Systems and
Software Group (DEEDS)
Hochschulstr. 10
64289 Darmstadt
Seminar: Dependable/Secure Mobile Computing
Topics Descriptions
OVERVIEW
Fault Tolerant Compression in WSN.............................................................................. 2
Comparison of Compression Techniques in WSN ......................................................... 2
Data-based Agreement for Coordination across Mobile Nodes ..................................... 2
Information Attributes in Wireless Senor Networks ...................................................... 3
Internet of Things and Wireless Sensor Networks Applications .................................... 3
Machine-to-Machine Networks ...................................................................................... 4
Optimal Tradeoffs in Wireless Sensor Networks ........................................................... 5
Sensor Cloud: Towards Sensor-Enabled Cloud Services ............................................... 6
Beyond Accuracy: What Information Quality Means to End User ................................ 6
Efficient Spatial Sampling in Wireless Sensor Networks............................................... 7
Body sensor networks for heathcare applications........................................................... 8
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Fault Tolerant Compression in WSN
Wireless Sensor Networks (WSN) by their nature are unreliable and fragile.
Therefore, there are various faults occurring in the WSN ranging from message
loss to node crashes. Any application designed for WSN has to take into account
these practical problems in consideration for the application to function
appropriately.
Various data compression schemes have been proposed for WSN. However,
only a few take into account the fragile nature of the network in consideration.
Most of the existing literature takes into consideration the message loss and
design the compression algorithm to tolerate the message loss. This seminar
targets to explore the literature for existing works that takes into account the
fragile nature of WSN while compressing data and provides the fault tolerance.
This seminar provides an opportunity to learn what it takes to turn a lab tested
design to run in real world scenario by considering compression in WSN as a
case study.
Comparison of Compression Techniques in WSN
Wireless Sensor Networks (WSN) have data redundancy due to redundant node
deployment or similar attribute distribution. Various schemes have been
proposed to reduce the amount of data like aggregation, thresholding, filtering
etc. Each technique has its pros and cons. A certain technique might perform
better in a certain scenario/application and another technique might perform
better in another.
The objective of this seminar to study the current literature and classify the
various compression techniques and identify the applications where they might
perform better than the other. We are also interested in finding a blend of various
techniques, if used together, could they improve upon the single technique used
otherwise?
Data-based Agreement for Coordination across Mobile Nodes
In order to implement cooperation between mobile entities, data-based
agreement can be exploited especially in case of predefined mobility paths of
assist nodes in WSNs. We refer by data-based agreement to database
transactions where mobile entities agree on a set of cooperative tasks that need
to be performed by these entities in an atomic way. Atomicity means that all
transaction participants agree on a set of tasks which will be performed by them
or no one of them is performing any task. The data about the agreed tasks and
their corresponding stakeholders are kept in databases as a proof for the
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obtained agreement. This proof may be of interest to the user, police, insurance
companies etc.
We focus in this seminar on database transactions executed between mobile
entities where network partitioning (due to either node or link failure/disruption) is
a dominant network failure to consider. For this purpose, different transaction
management approaches should be surveyed in this seminar.
Information Attributes in Wireless Senor Networks
In Wireless Sensor Networks (WSN) information processing is a vital fact. The
quality is the degree or grade of excellence, and Quality of Information (QoI) is
the quality experienced/perceived by the user concerning the received
information. To perceive this quality we need to understand information by its
characteristics/attributes. Hence, exploring, defining and understanding these
attributes is a key challenge and vital fact to deliver information with quality. In
the existing state of art there are some available attributes and some are still
missing.
The fact with QoI attributes lies in how one attribute differs from other. It is one of
the important aspects of QoI. It's based just not on the facts and analysis that
constitute information, but more on the context within which information is
accessed.
According to our knowledge there are still some missing attributes in WSNs for
QoI, these attributes play a vital role and useful in WSNs. The attributes
tunability, usability and affordability are similarly interwoven to the existing ones
in the literature and also used in other fields like database management, machine
learning and management studies. These attributes is applicable to WSNs and
also required, because of its sensible aspect in information processing.
Moreover, these attributes are very much relevant in saving resources like
energy and bandwidth.
Exploring the nature of these attributes and giving insight about tunability,
usability and affordability is a key challenge. However, the main concern here is
to compare these attributes with the existing ones and further explore them to
give a clear insight. Hence, this leads to propose a new set of attributes for
information in WSNs.
Internet of Things and Wireless Sensor Networks Applications
Wireless Sensor Networks (WSNs) seamlessly couples the physical environment
with the digital world. Sensor nodes are small, low power, low cost, and provide
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multiple functionalities. WSNs have sensing capability, processing power,
memory, communication bandwidth, battery power.
There are common available applications in WSNs such as applications in
military, health care, tracking etc. With the evolving internet of things, will the use
of WSNs with other embedded devices give a broader perspective of applications
that can be targeted? It’s just not only using embedded devices but also making
use of the internet clouds.
Using a mobile phone as an already existing embedded device in daily life and
the use of deployed WSNs in the environment and the internet can lead to
applications for emerging marketing strategies such as advertising. For example:
on a nice morning in the weekend during your shopping, the WSNs deployed in
the central area of the city can propose some directions and alert offers directly
to your mobile phones.
The primary goal:
•
•
A brief survey of existing WSNs applications.
Will the use of Internet of Things give the perspective of broader
application?
The secondary goal:
•
Proposing the different application that can emerge using WSNs and other
embedded devices.
Machine-to-Machine Networks
In the near future, there will be many more embedded devices than there are
mobile phones. When these devices are connected to the Internet, many novel
kinds of ubiquitous service will be enabled. It has been estimated that in 2010,
the number of communicating devices will be a thousand times greater than the
number of mobile phones, which is already more than one billion. When
connecting devices such as various machines, actuators and sensors to the
Internet, novel types of service are enabled. Previously, such devices
communicated with services using technology such as SMS. The applications
were vendor or domain-specific closed systems, for which achieving
interoperability with other vendor/domain systems was challenging.
M2M services refer to the services resulting from collection, transmission and
processing of information, and establish an interactive system with the remote
devices that are ultimately integrated within a managed M2M software system.
M2M systems will provide essential business possibilities and advantages for
companies, especially when information systems controlling their core processes
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are utilizing the real-time information produced by an M2M system. In
consequence, a company can increase the quality of its services, reduce costs
and increase customer satisfaction.
Currently, no universally applicable M2M service infrastructure exists that would
allow interoperation between devices and their enabled applications in wired and
wireless systems, regardless of the supplier. Information technology applications
usually operate as separate M2M solutions that are unaware of each other. As a
result, a number of business opportunities remain unexploited as the services
provided by the devices cannot be placed on the Internet.
The primary goal:
•
•
•
Investigate and explore the M2M area to understand the domain.
Identify requirements those are important for a general M2M system.
Explore technologies and protocols relevant for the different parts of an
M2M system.
The secondary goal:
•
Discuss possible approaches to the overall system architecture
Optimal Tradeoffs in Wireless Sensor Networks
In wireless sensor networks (WSNs) delivering the information with the required
Quality of information (QoI) to the user/sink is the main concern. To satisfy the
user required QoI, we should carefully design the information attributes such as
the accuracy of the samples to represent the real phenomena, the timeliness and
reliability of the data/information1 transport from the sources towards the sink. We
note that these attributes may be orthogonal to each other.
The intrinsic properties of WSN such as their energy constraints, and limited
availability of resources, constitute an unfavorable environment for end-to-end
timeliness guarantees. Many existing solutions are based on a timeliness notion
borrowed from real-time systems, which can only express strict end-to-end
deadlines. However, it is practically infeasible to impose these timeliness
requirements in WSN without overestimating the network capacity. On the other
hand, it is just infeasible to attain better timeliness without considering accuracy
of the samples and reliability of the data reaching the sink from the sources.
The primary goal:
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We refer to data by basic monitored facts/chunks (e.g., sensor readings/samples) and to information by the
collated and interpreted data systematized by purposeful acumen and processing required for an application
(e.g., average temperature in a region)
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Is it feasible to have an optimal tradeoff between accuracy, reliability and
timeliness to obtain the desired QoI?
The secondary goal:
How to achieve user required QoI by manipulating the sampling accuracy and
the chosen data transport reliability with timeliness?
Sensor Cloud: Towards Sensor-Enabled Cloud Services
Wireless Sensor Networks (WSNs) seamlessly couples the physical environment
with the digital world. Sensor nodes are small, low power, low cost, and provide
multiple functionalities. WSNs have sensing capability, processing power,
memory, communication bandwidth, battery power. In aggregate, sensor nodes
have substantial data acquisition and processing capability. On the other hand,
cloud computing refers to both the applications delivered as services over the
internet and the hardware and systems software in the data centers that provide
those services. Cloud computing is a way to increase capacity or add capabilities
on the fly without investing in new infrastructure, training new personnel, or
licensing new software.
The Sensor Cloud is an infrastructure that allows truly pervasive computation
using sensors as interface between physical and cyber worlds, the data-compute
clusters as the cyber backbone and the internet as the communication medium.
Sensor cloud integrates large-scale sensor networks with sensing applications
and cloud computing infrastructures. Sensor cloud collects and processes data
from various sensor networks and enables large-scale data sharing and
collaborations among users and applications on the cloud. Sensor cloud delivers
cloud services via sensor-rich mobile devices.
The key challenge is to provide an overview and basic concepts of sensor cloud.
Define the potential applications and how to integrate WSNs and clouds.
Beyond Accuracy: What Information Quality Means to End User
The concept of "fitness for use" is now widely adopted in Wireless Sensor
Networks (WSNs) and the quality literature. It emphasizes the importance of
taking an end user viewpoint of quality because ultimately it is the end user who
will judge whether or not a information is fit for use. We can define "information
quality" as information that is fit for use by end user. In addition, we define
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"information quality dimension" as a set of information quality attributes that
represent a single aspect or construct of information quality.
In the information systems literature, information quality and user satisfaction are
two major dimensions for evaluating the success of information systems. These
two dimensions generally include some information quality attributes, such as
accuracy, timeliness, precision, reliability, currency, completeness. Three
approaches are used in the literature to study information quality: (1) an intuitive,
(2) a theoretical, and (3) an empirical approach.
To improve information quality, we need to understand what information quality
means to information end user (those who use information). The purpose of this
research, therefore, is to develop a framework that captures the aspects of
information quality that are important to information end user.
Preliminary Conceptual Framework
• The information must be accessible to the end user.
• The end user must be able to interpret the information.
• The information must be relevant to the end user.
• The end user must find the information accurate.
Efficient Spatial Sampling in Wireless Sensor Networks
Wireless Sensor Network (WSN) is a rapidly emerging field that has many
applications in science and technology. A WSN is an adhoc self-organizing
wireless network of battery-powered Sensor Nodes (SN) or motes. Each mote is
equipped with a sensing device, limited processing capabilities and short-range
radio communication mechanism. WSN collects distributed data in a
collaborative fashion and reports them to a high-performance dedicated node
called the sink. Simplistic and low-cost design of SNs allows relatively
inexpensive large-scale distribution of motes. This attribute makes WSNs ideal
for surveillance and monitoring applications. The main challenge in WSN is to
efficiently transmit a large amount of data through a limited communication
channel.
Recent developments in SN technology made very small and energy-efficient
motes possible. New achievements in WSN pursue high-resolution macroscopic
views of a physical phenomenon in a large operational environment. This
introduces tremendous challenges towards devising sampling, routing and data
fusion techniques. The main purpose of all these works is to provide efficient
data collection methods for WSN. While there are many works so far concerning
networking issues of WSN, there is also a large body of literature related to
efficient spatial sampling techniques. The latter usually tries to exploit
correlations between data recorded by neighboring motes to transmit nonredundant data items through the network.
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Efficient spatial sampling methods in WSN lie in a very fundamental layer and
may determine the higher level concepts such as data transport and network
routing mechanisms. In simple words, it states this fundamental question: before
contriving efficient routing and data transmission methods, first see what data is
worth transmitting. Among several distributed sampling frameworks, “distributed
source coding”, “in-network compression” and recently “compressive sampling”
are the most authoritative ones. From the viewpoint of sampling theory, a WSN is
modeled as a 2-dimensional distributed signal processing problem and thus has
tight relations with basic mathematical topics. Compressive Sampling (CS) which
is a sub-Nyquist sampling theory is based on topics in statistics, linear algebra
and convex optimization as well. These fundamental subjects may result in
significantly efficient protocols for data collection in WSN that can open new
avenues towards extremely efficient WSNs.
Body sensor networks for heathcare applications
Electronic health (e-Health) and mobile health (m-Health) are increasingly
considered to be a key driver for the progress of health systems. They are not
less relevant than the development of new medicaments or treatment processes.
However, there is still only little work for supporting rescue in mass casualties
scenarios, though these scenarios are worldwide one of the most cover
demanding health disciplines.
Usually, first responders estimate the medical needs in mass casualties
scenarios from subjective observations gathered through uncoordinated
emergency calls from non-experts in the incident location. Accordingly, they
command specific teams to move to the location. At arrival the teams make local
measurements, based on which they rank the priorities of patients, and give local
treatments or decide to transport them to a specific hospital. Nevertheless the
advances in the measurement of vital signs, still the human estimation may be
error prone and not-in-time since usually the ratio of first responders to casualties
can reach one to hundreds or even thousands in some cases. Recently, an
approach based on plain patient to patient communication without relying on the
existence of first responders nor a communication infrastructure has been
proposed. This allows for the first time to classify, rank and schedule casualties
without experts in the loop. The casualties, the responders and the administration
gains are very compelling.
In this seminar, the student should survey and compare the use of body sensor
networks in emergency rescue scenarios while focussing on spontaneously (ad
hoc) interconnected body sensor networks.
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