Game Theory–Based Effective Clustering in Heterogeneous Underwater Acoustic Sensor Network

A game‐based energy‐efficient cluster head selection method is proposed for the cluster head selection problem in 3D underwater networks. Underwater networks are different from terrestrial networks. To simplify the network structure, first, the 3D underwater network is regarded as a large cube and is divided into several small cubes for cluster head selection. This method can effectively balance the energy consumption of the network. Simultaneously, in the process of selecting cluster heads, the problem of selecting sensor node cluster heads is treated as a game problem, and cluster heads are selected based on the node utility function. Due to sensor nodes being all individually rational, an incentive mechanism is introduced to avoid adopting non‐cooperative strategies and induce sensor nodes to make decisions that are more beneficial to the overall system, thereby satisfying utility maximization in multiple Nash equilibria. After a large number of simulations, the outcomes indicate that the algorithm presented in this study outperforms alternative approaches in terms of both network lifetime and energy utilization efficiency.