The evolution game analysis of clustering for asymmetrical multi-factors in WSNs

This paper considers the impact of the distance from cluster heads (CHs) to the sink, and uses evolution game-theoretic model to analyze the communication energy optimization. We present the area division scheme of sensors so as to achieve a desirable communication energy optimization. By analyzing the evolution stable strategy (ESS) of territory game model, we propose a clustering algorithm based on territory game (TGC algorithm) to define the area limits. TGC algorithm mitigates the unbalanced energy consumption caused by the asymmetrical distance from CHs to the sink. By analyzing the ESS of the war of attrition game, we propose a clustering algorithm based on the war of energy attrition (WEAC algorithm). WEAC algorithm selects CHs from low energy sensors only considering individual remaining energy rather than the distance from their CHs to the sink. Simulations are given to validate the proposed TGC and WEAC algorithms. The results show the proposed algorithms achieve desirable network performances.

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► Analyze two models of evolution game for energy efficient clustering scheme.
► Propose two algorithms to obtain evolution stable strategies for clustering.
► Present a performance evaluation study for the proposed solutions.