Energy-efficient clustering algorithm based on game theory for wireless sensor networks

Clustering is a popular method to achieve energy efficiency and scalable performance in large-scale sensor networks. Many clustering algorithms were proposed to use energy efficiently, extend network life span, and improve data transfer. Clustered routing for selfish sensors is a recently proposed algorithm based on game theory. In clustered routing for selfish sensors, the sensor nodes campaign to be cluster heads in order to achieve equilibrium probability. However, this algorithm needs global information for the computation of probability and disregard the uneven energy dissipation from different nodes that serve as cluster heads, thereby causing some nodes to die quickly. Therefore, an energy-efficient clustering algorithm based on game theory is proposed in this study. In the cluster head selection phase, each node competes as potential cluster head by joining a localized clustering game, and a potential cluster head is selected to be a real cluster head through a properly designed probability method. Simulation results show that the life span of wireless sensor networks extended by our algorithm becomes longer than those extended by low-energy adaptive clustering hierarchy and clustered routing for selfish sensors when proper parameters are used.