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Adaptive Sensor Activity Scheduling in Distributed Sensor Networks: A Statistical Mechanics Approach

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  • Abhishek Srivastav
  • Asok Ray
  • Shashi Phoha

Abstract

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.

Suggested Citation

  • Abhishek Srivastav & Asok Ray & Shashi Phoha, 2009. "Adaptive Sensor Activity Scheduling in Distributed Sensor Networks: A Statistical Mechanics Approach," International Journal of Distributed Sensor Networks, , vol. 5(3), pages 242-261, May.
    • Handle: RePEc:sae:intdis:v:5:y:2009:i:3:p:242-261
    DOI: 10.1080/15501320802473250
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    References listed on IDEAS

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    1. Greco, Antonella & Carbone, Vincenzo & Sorriso-Valvo, Luca, 2007. "Non-Poisson intermittent events in price formation in a Ising spin model of market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 480-486.
    2. Shashi Phoha & John Koch & Eric Grele & Christopher Griffin & Bharat Madan, 2005. "Space-time Coordinated Distributed Sensing Algorithms for Resource Efficient Narrowband Target Localization and Tracking," International Journal of Distributed Sensor Networks, , vol. 1(1), pages 81-99, February.
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