This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Campos-Nañez, E. Articles by Li, C. Search for Related Content

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

Department of Engineering Management and Systems Engineering, George Washington University, Washington, DC 20052
Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia 22904
Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia 22904
ecamposn{at}gwu.edu
agarcia{at}virginia.edu
cl2ha{at}virginia.edu

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).

Subject classifications: sensor networks; game theory; distributed algorithms; power management.
History: Received March 2006; revision received August 2006; accepted November 2006.