Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Habitat monitoring with sensor networks
Communications of the ACM - Wireless sensor networks
Symbolic dynamic analysis of complex systems for anomaly detection
Signal Processing
Versatile low power media access for wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Tracking multiple targets with self-organizing distributed ground sensors
Journal of Parallel and Distributed Computing
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
Sensor Network Operations
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Design of a wireless sensor network platform for detecting rare, random, and ephemeral events
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Mixed-State Auto-Models and Motion Texture Modeling
Journal of Mathematical Imaging and Vision
Wireless sensor networks for structural health monitoring
Proceedings of the 4th international conference on Embedded networked sensor systems
SyncWUF: An Ultra Low-Power MAC Protocol for Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Information and Self-Organization: A Macroscopic Approach to Complex Systems (Springer Series in Synergetics)
Distributed network control for mobile multi-modal wireless sensor networks
Journal of Parallel and Distributed Computing
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This article presents an algorithm for adaptive sensor activity scheduling (A-SAS) in distributed sensor networks to enable detection and dynamic footprint tracking of spatial-temporal events. The sensor network is modeled as a Markov random field on a graph, where concepts of Statistical Mechanics are employed to stochastically activate the sensor nodes. Using an Ising-like formulation, the sleep and wake modes of a sensor node are modeled as spins with ferromagnetic neighborhood interactions; and clique potentials are defined to characterize the node behavior. Individual sensor nodes are designed to make local probabilistic decisions based on the most recently sensed parameters and the expected behavior of their neighbors. These local decisions evolve to globally meaningful ensemble behaviors of the sensor network to adaptively organize for event detection and tracking. The proposed algorithm naturally leads to a distributed implementation without the need for a centralized control. The A-SAS algorithm has been validated for resource-aware target tracking on a simulated sensor field of 600 nodes.