Wireless Networks

, Volume 15, Issue 5, pp 619–635 | Cite as

Rechargeable sensor activation under temporally correlated events

Article

Abstract

Wireless sensor networks are often deployed to detect “interesting events” that are bound to show some degree of temporal correlation across their occurrences. Typically, sensors are heavily constrained in terms of energy, and thus energy usage at the sensors must be optimized for efficient operation of the sensor system. A key optimization question in such systems is—how the sensor (assumed to be rechargeable) should be activated in time so that the number of interesting events detected is maximized under the typical slow rate of recharge of the sensor. In this article, we consider the activation question for a single sensor, and pose it in a stochastic decision framework. The recharge-discharge dynamics of a rechargeable sensor node, along with temporal correlations in the event occurrences makes the optimal sensor activation question very challenging. Under complete state observability, we outline a deterministic, memoryless policy that is provably optimal. For the more practical scenario, where the inactive sensor may not have complete information about the state of event occurrences in the system, we comment on the structure of the deterministic, history-dependent optimal policy. We then develop a simple, deterministic, memoryless activation policy based upon energy balance and show that this policy achieves near-optimal performance under certain realistic assumptions. Finally, we show that an aggressive activation policy, in which the sensor activates itself at every possible opportunity, performs optimally only if events are uncorrelated.

Keywords

Rechargeable sensors Temporal correlations Node activation Energy efficiency 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Neeraj Jaggi
    • 1
  • Koushik Kar
    • 1
  • Ananth Krishnamurthy
    • 2
  1. 1.Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic InstituteTroyUSA
  2. 2.Department of Decision Sciences and Engineering SystemsRensselaer Polytechnic InstituteTroyUSA

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