Adaptive Energy-Efficient Clustering Mechanism for Underwater Wireless Sensor Networks Based on Multidimensional Game Theory

Abstract

Efficient and reliable clustering mechanisms play a crucial role in enhancing the energy management efficiency of underwater wireless sensor networks (UWSNs). In recent years, game theory has been widely applied in clustering mechanisms for its ability to provide theoretical support in optimizing strategies. However, existing game theory-based clustering mechanisms only analyze the current cooperation and competition relationships of nodes in a single dimension, which limits the efficient energy utilization of the network. To address these limitations, this article proposes an adaptive energy-efficient clustering mechanism for UWSNs based on multidimensional game theory (MDGTC). During the candidate cluster head (C-CH) nodes selection, MDGTC enhances the opportunity of the potential optimal CH node to act as C-CH again by establishing a multidimensional clustering game model. Subsequently, an adaptive CH competition mechanism is introduced to further optimize the CH selection strategy by considering the energy and energy consumption status of local nodes and global networks. In addition, by combining a hierarchical architecture and a hybrid CH rotation mechanism, the stability of the proposed model is ensured, leading to a more balanced energy consumption among network nodes. In conclusion, MDGTC offers an effective distributed energy management architecture for UWSNs. The simulation results show that the MDGTC can achieve efficient energy utilization and prolong the network lifetime significantly.