Geosensor Networks: Spatiotemporal
Queries for Environmental Monitoring
Matt Duckham
Department of Geomatics, The University of Melbourne, Australia
With acknowledgments to Patrick Laube, Jafar Sadeq, Ming Shi
(UMelbourne), Allison Kealy (UMelbourne) Mike Worboys (UMaine), Femke
Reitsma (UEdinburgh), Alex Klippel (PSU)
Applications of wireless sensor networks
Szewczyk, R., Osterweil, E., Polastre, J., Hamilton, M., Mainwaring, A., and Estrin, D., (2004).
Habitat monitoring with sensor networks. Communications of the ACM, 47(6): 34–40.
Wireless sensor networks (WSN)
• Wireless networks of
miniaturized sensorenabled computers
(nodes)
• Geosensor network (GSN)
emphasizes that the
sensor nodes are located
somewhere
• Requires new ways of
thinking about data
capture and processing
Challenges
• Physical/chemical (e.g., new sensor arrays)
• Hardware (e.g., low-power processors and
transceivers)
• Software (e.g., small footprint OS)
• Communications (e.g., efficient routing)
• Applications (e.g., water resources, fire
monitoring)
Spatial challenges
•
Localization
– Scalable, low-cost, low-power, accurate, and precise
positioning
•
Spatial policy
– Integration of information into decision-making process
•
Processing and spatial computing
1.
2.
3.
4.
Decentralized, distributed processing
Qualitative vs quantitative information
Dynamic phenomena and real-time information
Uncertainty and robustness
Decentralized, distributed computing
• GSN fundamental #1: In any WSN, power is the
overriding resource constraint that governs network
lifetime.
• Computation is cheaper than communication
(1Kbit comm≈3M CPU inst), therefore efficient
to trade communication for computation.
Decentralized, distributed computing
Decentralized, distributed computing
Decentralized, distributed computing
32
31
28
27
28
25
26
22
27
22,23,27
22,23,26,26,27
26
26
30
23
26
31
Decentralized, distributed computing
32
31
28
27
28
25
26
22
26
27
27
26
27
30
23
26
31
Qualitative information processing
• GSN fundamental #2: Using qualitative
information where possible in a GSN improves
robustness, energy efficiency.
• Qualitative (ordering and non-metric)
information:
– is imprecise and improves robustness to inaccuracy
– can always be created from quantitative information;
the converse is not true
– can correspond to salient boundaries
– requires fewer bits to transmit
Qualitative information processing
1: c>d>e
2: a>b>c
Example from Guibas, L. J. (2002). Sensing, tracking and reasoning with
relations. Signal Processing Magazine, IEEE, 19(2), 73-85.
Dynamic and real-time information
• GSN fundamental #3: Event- and process-oriented
information is central to understanding the dynamic
environments monitored by GSN.
• Moving beyond the snapshot metaphor
• Ask questions about “What is happening?” as
well as “What is the state of the world?”
Dynamic and real-time information
Merging
t1
Aggregation
t2
Splitting
t3
Disaggregation
Dynamic and real-time information
Uncertainty and robustness
• GSN fundamental #4: Data in a GSN is
inherently unreliable.
• Robustness and reasoning under uncertainty are
cross-cutting themes in GSN
• Hardware is low-cost, unreliable, poorly
calibrated
• Need to generate coarse-grained but reliable
decision support services from fine-grained but
unreliable data
App #1: Conservation contracts
• Conservation contracts increasingly used to
promote environmentally beneficial management
on private land by public bodies
• Use enhanced, spatially-aware GSN to help in
monitoring conservation contract compliance
– Overcome problems of localization
– Overcome problems of distributed, decentralized
spatial computing
– Combined data capture and processing
App #1: Conservation contracts
• Low-cost, fine-grained monitoring of
environmental change over extended periods
• Distributed: Spatial variation in environmental
phenomena
• Qualitative: Specified outcomes often qualitative
• Dynamic: Observation of events and processes
• Robust: Uncertainty exists at every level of the
application
App #2: Distributed in-network prediction
Slide courtesy Femke Reitsma
App #2: Distributed in-network prediction
App #3: Happy sheep!
Centralized
r
r
r
r
flock
flock
Distributed
r
Fundamental ideas in GSN
• “The opposite of GIS”
• “Blurring the distinction between spatial
information capture and processing”
• “Individual data items become almost
meaningless”
• “Ambient spatial intelligence”
DG/SUM’07 Workshop
COSIT’07: Spatial Information Theory
Related publications
•
•
•
•
•
•
Klippel, A. Worboys, M.F., Duckham, M. (2007) Identifying factors of geographic event
conceptualization. Accepted for International Journal of Geographical Information Science .
Worboys, M.F., Duckham, M. (2006) Monitoring qualitative spatiotemporal change for geosensor
networks. International Journal of Geographical Information Science v20 n10, 1087-1108.
Klippel, A., Worboys, M.F., Duckham, M. (2006) Geographic event conceptualization. Cognitive
Processing v7 nS1, S52-S54.
Worboys, M.F. and Duckham, M. (2006) Formalizing mobility in dynamic location-aware sensor
networks. In MDM '06 MLASN (Mobile Data Management 2006 Workshop on Mobile Location-Aware
Sensor Networks), IEEE, pp. 157
Duckham, M., Nittel, S. and Worboys, M. (2005). Monitoring Dynamic Spatial Fields Using
Responsive Geosensor Networks. In Shahabi, C. and Boucelma,O. (eds) ACM GIS 2005, ACM
Press, pp. 51-60.
Nittel, S., Duckham, M., Kulik, L. (2004) Information dissemination in mobile ad-hoc geosensor
networks. In Egenhofer, M.J., Freksa, C. and Miller, H.J. (eds) Lecture Notes in Computer Science 3234,
Springer, pp. 206-222.
•
Duckham, M. and Reitsma, F. (2007, in prep) Distributed environmental prediction and feedback
in robust geosensor networks.
•
Forthcoming special issue of ISPRS Journal of Photogrammetry and Remote Sensing on
Distributed Geoinformatics (eds Agouris, Croitoru, Duckham)
Forthcoming special issue of Computers, Environment, and Urban Systems on Distributed
and Mobile Spatial Computing (eds Duckham, Laube, Croitoru)
•