Evolutionary Game-Based Secrecy Rate Adaptation in Wireless Sensor Networks

Abstract

Physical layer security, whose aim is to maximize the secrecy rate of a source while keeping eavesdroppers ignorant of data transmitted, is extremely suitable for Wireless Sensor Networks (WSNs). We therefore, by developing the classical wire-tap channel, construct an approach to compute the secrecy rate between a sensor node and its responsible cluster head in the clustered WSNs. A noncooperative secrecy rate game towards WSNs is formulated to solve contradictions between maximizing the secrecy rate of a sensor node and minimizing power consumed for data transmission. Using evolutionary game theory, we set up a selection dynamics upon which a power level can be adaptively selected by a sensor node. Thus, the objective of secrecy rate adaptation for maximizing the fitness of member sensor nodes is achieved. We also prove the game is stable; that is, there exist evolutionarily stable strategies (ESSs) that explain which strategies will be selected by a sensor node in the end. Moreover, a corresponding algorithm of secrecy rate adaptation is given. Numerical experiments show our proposed approach can adaptively adjust the secrecy rate of a sensor node, which provides a novel way to guarantee the confidentiality of WSNs.