Research on WSN Intelligent Routing Algorithm Based on Bayesian Learning and particle swarm optimization

Abstract

Background: Wireless sensor networks have the characteristics of strong scalability, easy maintenance, and self-organization, but the energy of nodes is limited and it is difficult to replace the energy supply module. The survival time of the network has always been the key to restricting the development of wireless sensor networks. Objective: Aiming at the problems of short network lifetime and low coverage, a multi-objective optimization routing algorithm has been proposed, focusing on how to balance the communication energy consumption of each node in the network and improve the coverage area of the remaining nodes. Method: Firstly, the node region was divided into several fan ring subregions. Then, the particle swarm optimization algorithm was used to find the fan angles and radii of each fan ring subregion. Next, Bayesian learning was used to select the appropriate cluster head. Results: The simulation results showed the convergence speed of the proposed algorithm to be improved, solving the problems of cluster head election and node routing planning, improving the utilization of node energy, and verifying the effectiveness. Conclusion: The particle swarm optimization algorithm and Bayesian learning have been introduced to cluster network nodes, and a multi-objective fitness function compatible with the energy consumption and coverage of network nodes has been designed. By optimizing the selection method of convergence nodes, the network communication cost of each node can be effectively balanced, and the speed of network coverage area reduction can be effectively reduced in the later period of node communication.