Game-theory-based lifetime maximization of multi-channel cooperative spectrum sensing in wireless sensor networks

Accurate and efficient detection of the radio-frequency spectrum is a challenging issue in wireless sensor networks (WSNs), which are used for multi-channel cooperative spectrum sensing (MCSS). Due to the limited battery power of sensors, lifetime maximization of a WSN is an important issue further sensing quality requirements. The issue is more complex if the low-cost sensors cannot sense more than one channel simultaneously, because they do not have high-speed Analogue-to-Digital-Convertors which need high-power batteries. This paper proposes a novel game-theoretic sensor selection algorithm for MCSS that extends the network lifetime assuming the quality of sensing and the limited ability of sensors. To this end, an optimization problem is formulated using the "max-min" method, in which the minimum remaining energy of sensors is maximized to keep energy balancing in the WSN. This paper proposes a coalition game to solve the problem, in which sensors act as game players and decide to make disjoint coalitions for MCSS. Each coalition senses one of the channels. Other nodes, that decide to sense none of the channels, turn off their sensing module to reserve energy. First, a novel utility function for the coalitions is proposed based on the remaining energy and consumption energy of sensors besides their detection quality. Then, an algorithm is designed to reach a Nash-Equilibrium (NE) coalition structure. The existence of at least one NE, converging toward one of the NEs, and the computational complexity of the proposed algorithm are discussed. Finally, simulations are presented to demonstrate the ability of the proposed algorithm, assuming the systems using IEEE802.15.4/Zigbee and IEEE802.11af.