Wireless Sensor Network Based System for Fire Endangered Areas
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Identification of Low-Level Point Radiation Sources Using a Sensor Network
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Model-based monitoring for early warning flood detection
Proceedings of the 6th ACM conference on Embedded network sensor systems
Concentration of Measure for the Analysis of Randomized Algorithms
Concentration of Measure for the Analysis of Randomized Algorithms
Rapid detection of rare geospatial events: earthquake warning applications
Proceedings of the 5th ACM international conference on Distributed event-based system
A wireless mesh sensing network for early warning
Journal of Network and Computer Applications
Handbook on Securing Cyber-Physical Critical Infrastructure
Handbook on Securing Cyber-Physical Critical Infrastructure
A fresh perspective: learning to sparsify for detection in massive noisy sensor networks
Proceedings of the 12th international conference on Information processing in sensor networks
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A geospatial system is one in which the state space includes one, two or three-dimensional space and time. A geospatial event is one in which an event impacts points in space over time. Examples of geospatial events include floods, tsunamis, earthquakes, and emission of toxic plumes. This paper discusses aspects of the theory of geospatial distributed event based systems (GDEBS). The paper describes algorithms for rapid detection of geospatial events which can be used on Cloud computing architectures, in which many servers collaborate to detect events by analyzing data streams from large numbers of sensors. Sensor noise and timing errors may result in false detection or missed detection as well as incorrect identification of event attributes such as the location of the event source. The paper presents mathematical analyses and simulations dealing with rapid event detection for geospatial events of varying speeds in the presence of substantial sensor noise and timing error. The paper also describes some of the algorithmic and machine-learning techniques for improving event detection in the Cloud with large numbers of noisy sensors. Experience with GDEBS using a seismic network is described.