Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 2; p. 478
• Main Authors: Yan, Xiao, Huang, Cheng, Gan, Jianyuan, Wu, Xiaobei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2022; MDPI
• Subjects: Algorithms; Analysis; Cluster Analysis; Clustering; Computer Communication Networks; Electric power transmission; Energy consumption; Energy efficiency; Energy management systems; Energy use; Game Theory; idle listening time; Internet of Things; Listening; network lifetime; Optimization algorithms; penalty mechanism; Power supply; Sensors; Sleep; Wireless networks; wireless sensor network; Wireless sensor networks; Wireless Technology; penalty mechanism; idle listening time; network lifetime; game theory; wireless sensor network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22020478

Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime.