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A Game-Theoretic Approach to Efficient Power Management in Sensor Networks

Author

Listed:
  • Enrique Campos-Nañez

    (Department of Engineering Management and Systems Engineering, George Washington University, Washington, DC 20052)

  • Alfredo Garcia

    (Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia 22904)

  • Chenyang Li

    (Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia 22904)

Abstract

Wireless sensor networks pose numerous fundamental coordination problems. For example, in a number of application domains including homeland security, environmental monitoring, and surveillance for military operations, a network's ability to efficiently manage power consumption is extremely critical because direct user intervention after initial deployment is severely limited. In these settings, limited battery life gives rise to the basic coordination problem of maintaining coverage while maximizing the network's lifetime. In this paper, we propose a distributed scheme for efficient power management in sensor networks that is guaranteed to identify suboptimal topologies in an online fashion. Our scheme is based on a general (game-theoretic) mathematical structure that induces a natural mapping between the informational layer and the physical layer. We provide sufficient conditions for the convergence of the algorithm to a pure Nash equilibrium and characterize the performance of the algorithm in terms of coverage. We also present encouraging performance results on a MicaZ testbed as well as on large-scale topologies (obtained via simulation).

Suggested Citation

  • Enrique Campos-Nañez & Alfredo Garcia & Chenyang Li, 2008. "A Game-Theoretic Approach to Efficient Power Management in Sensor Networks," Operations Research, INFORMS, vol. 56(3), pages 552-561, June.
  • Handle: RePEc:inm:oropre:v:56:y:2008:i:3:p:552-561
    DOI: 10.1287/opre.1070.0435
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    References listed on IDEAS

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    1. Theodore J. Lambert & Marina A. Epelman & Robert L. Smith, 2005. "A Fictitious Play Approach to Large-Scale Optimization," Operations Research, INFORMS, vol. 53(3), pages 477-489, June.
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    Cited by:

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    2. Wu, Tao & Zhang, Ning & Gui, Lin & Wu, Wenjie, 2018. "Sustainable endogenous growth model of multiple regions: Reconciling OR and economic perspectives," European Journal of Operational Research, Elsevier, vol. 269(1), pages 218-226.
    3. Tiangui Lv & Hualin Xie & Hua Lu & Xinmin Zhang & Lei Yang, 2019. "A Game Theory-Based Approach for Exploring Water Resource Exploitation Behavior in the Poyang Lake Basin, China," Sustainability, MDPI, vol. 11(22), pages 1-14, November.
    4. Zhao, Rui & Zhou, Xiao & Han, Jiaojie & Liu, Chengliang, 2016. "For the sustainable performance of the carbon reduction labeling policies under an evolutionary game simulation," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 262-274.
    5. TürkogullarI, Yavuz B. & Aras, Necati & AltInel, I. Kuban & Ersoy, Cem, 2010. "A column generation based heuristic for sensor placement, activity scheduling and data routing in wireless sensor networks," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1014-1026, December.
    6. Jason R. Marden & Adam Wierman, 2013. "Distributed Welfare Games," Operations Research, INFORMS, vol. 61(1), pages 155-168, February.

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