Research On Cluster Routing Algorithm Of Wireless Sensor Networks Based On Game Theory

The rapid development of artificial intelligence and wireless communication technology has promoted the construction of the Internet of Things(Io T).In the background of the 5G era,the communication field will gradually achieve the goal of connecting everything,the wireless sensor networks(WSNs),an important part of the Io T,are widely used in various fields such as environmental monitoring and intelligent transportation due to their advantages of low power consumption,flexibility,self-organization,and low cost.As the energy consumption of the WSN data transmission process is relatively high and sensor node resources are limited,designing energy-efficient routing algorithms to extend the network lifetime is currently one of the key research directions of WSN.Among them,the cluster routing algorithm has significant advantages in network scalability and energy efficiency.Therefore,based on the problems of cluster head node election and selfish node in the cluster routing algorithm of the WSN,this paper proposes a new cluster routing algorithm based on game theory by analyzing the principle of existing cluster routing algorithms.The main research works and obtained results can be summarized as follows:Firstly,in view of the current problems such as unreasonable election of cluster head nodes and high energy consumption in the existing cluster routing algorithms,the energy saving clustering algorithm for wireless sensor networks based on game theory(ECWG)is proposed by designing a two-level cluster head node election mechanism to improve the rationality of cluster head node election.At the stage of candidate cluster head election,the game theory method is introduced to treat nodes and neighbor nodes as game participants and comprehensively consider the number of neighbor nodes and energy parameters to construct a profit function.The nodes choose the corresponding game strategy to obtain the equilibrium probability of becoming the cluster head node,and decide whether to become a candidate cluster head node according to the probability.Energy and density factors are introduced into the equilibrium probability to make candidate cluster head node election more reasonable and effective.In the actual cluster head election stage,the optimal number of cluster heads in the current network is calculated,and the candidate cluster head nodes complete the election of cluster head nodes by comparing the parameters of the real cluster head candidates.Simulation experimental results show that the proposed algorithm can make the election of cluster head nodes more reasonable,effectively balance the energy between nodes,and improve the network lifetime.Secondly,in order to further promote node cooperation and communication,and ensure the communication quality of the WSN cluster routing algorithm,the selfish node incentive mechanism based on evolutionary game theory is proposed on the basis of the ECWG algorithm.Firstly,based on the comprehensive trust factor of nodes,the base station judges whether the nodes participating in forwarding are selfish nodes.Then,using the evolutionary game theory,a model of evolutionary game is constructed by treating rational cluster head nodes and selfish nodes as game participants,analyzing the data forwarding process between nodes,and enhancing the practical significance of the model.At the same time,combining the incentive mechanism design profit function,all participants make decisions based on node trust,energy,and other factors,and constantly evolve until the profit is balanced.Finally,it is proved that this mechanism makes nodes continuously adjust their strategies during the game,choose the strategy that maximizes the profit,and avoid selfish behavior.Simulation experiments show that the proposed incentive mechanism improves the packet delivery rate of the WSN,encourages selfish nodes to actively participate in the forwarding of data packets forwarding which further balances the energy consumption of nodes,and makes data transmission more efficient.