Using Misbehavior to Analyze Strategic versus Aggregate Energy Minimization in Wireless Sensor Networks

We present a novel formulation of the problem of energy misbehavior and develop an analytical framework for quantifying its impact on other nodes. Specifically, we formulate two versions of the power control problem for wireless sensor networks with latency constraints arising from duty cycle allocations. In the first version, strategic power optimization, nodes are modeled as rational agents in a power game, who strategically adjust their powers to minimize their own energy. In the other version, joint power optimization, sensor nodes adjust their transmission powers to minimize the aggregate energy expenditure. Our analysis of these models yields insights into the different energy outcomes of strategic versus joint power optimization. We show that while joint power optimization fits the accepted paradigm of cooperation among sensor nodes (for example large number of sensor nodes cooperating for a task such as target tracking), it comes with both advantages and disadvantages when energy misbehavior is taken into account. One advantage is that it can (sometimes) be energy-dominant, i.e. the optimal energy cost for each node under joint energy minimization is lower than its strategically optimal energy cost. We then develop a model for characterizing energy misbehavior and show that joint optimization is disadvantageous because it is impossible to prevent misbehavior under any channel quality and load constraints, whereas strategic optimization is more resilient. We prove that it is impossible for a node to unilaterally and un-detectably follow a different energy optimization strategy than the other nodes and hence the only threat to the network is misbehavior through false advertisement. We then provide sufficient conditions under which misbehavior through false advertisement can be prevented under a strategic optimization regime. Our analytical results reveal optimal strategies for attacking nodes in an enemy network through energy depletion and help develop effective defense mechanisms for protecting our own wireless network against energy attacks by an intelligent adversary. This work was supported in part by AFRL and NSF under ITR0312632 and IIS-0329738. The views expressed in this articles are those of the authors and do not necessarily reflect the official policy or position of the United States Air Force, the U.S. Department of Defense or the U.S. Government. R. Kannan is with the Department of Computer Science, Louisiana State University, Baton Rouge LA 70803. S. Wei is with the Department of Electrical Engineering, Louisiana State University, Baton Rouge LA 70803. V. Chakravarthy is with Air Force Research Laboratory (AFRL/EW), WPAFB, Dayton, OH