Towards Self–optimizing Sensor Networks: Game–Theoretic Second–Order CA–Based Approach

We propose a second–order Cellular Automata (CA)–based approach to solve a problem of lifetime optimization in Wireless Sensor Networks (WSN). A WSN graph created for a given deployment of WSN in monitored area is considered as a multiagent system, where agents take part in a spatial Prisoner’s Dilemma game. We propose a local, agent–player oriented criterion which incorporates issues of area coverage and sensors energy spending. Agents act in such a way to maximize their profits what results in achieving by them a solution corresponding to Nash equilibrium. We show that the system is self–optimizing, i.e. is able to optimize a global criterion not known for players, related to a Nash equilibrium, which provides a balance between requested coverage and spending energy, and results in expanding WSN lifetime. The proposed approach is validated by a number of experimental results.