Incentive Compatible and Globally Efficient Position Based Routing for Selfish Reverse Multicast in Wireless Sensor Networks

Abstract

We consider the problem of all-to-one selfish routing in the absence of a payment scheme in wireless sensor networks, where a natural model for cost is the power required to forward, referring to the resulting game as a Locally Minimum Cost Forwarding (LMCF). Our objective is to characterize equilibria and their global costs in terms of stretch and diameter, in particular finding incentive compatible algorithms that are also close to globally optimal. We find that although social costs for equilibria of LMCF exhibit arbitrarily bad worst-case bounds and computational infeasibility of reaching optimal equilibria, there exist greedy and local incentive compatible heuristics achieving near-optimal global costs.

Keywords

• sensor networks
• incentive compatible topology control
• game theory
• price of stability
• price of anarchy
• heuristics for NP-hard problems
• location based routing
• local algorithms
• random Euclidean power graphs