| It has become one of the most important technologies for wireless sensor networks in the world because of its strong network scalability,easy to maintain and self-organization nowadays.However,the energy of nodes in wireless sensor networks is limited and it is difficult to replace batteries.The life cycle of the network always has been the key to restricting the development of wireless sensor networks,and in the later stage of network communication,the node coverage rate will drop rapidly due to the death of some nodes.Therefore,how to improve the energy utilization rate of wireless sensor nodes and slow down the rapid decline of network coverage has become one of the hot topics in recent years.To solve the problems of short network survival time and low coverage rate in the wireless sensor network,this paper proposes a multi-objective optimal routing algorithm based on improved particle swarm algorithm.The main work of this paper is shown as follows:(1)Clustering is an important method to extend the life cycle of wireless sensor networks,and its clustering method directly affects the clustering quality of the network.In view of the uneven distribution of cluster heads in the traditional LEACH algorithm and the large difference in the number of each cluster member node,a new clustering method based on region segmentation is proposed.Firstly,the node region is divided into several sector subregions,each subregion is a cluster,the member nodes in the subregion compete for the cluster leader.Secondly the particle swarm algorithm is used to find the angle and radius of each subregion in the current situation,thereby determining the number of clusters and the number of clusters of each cluster.This clustering method can effectively solve the problems of uneven clustering and insufficient dispersion of cluster heads,and then solving the problems of balancing network load and extending network life cycle.(2)To solve the problems of traditional particle swarm algorithm that are easy to fall into local optima and poor global search ability,a new particle swarm optimization algorithm EBG-PSO(Energy Balance and Energy Center of Gravity Based on Particle Swarm Optimization)is proposed that combines adaptive weight coefficients and dynamic constants.The EBG-PSO algorithm is introduced into the research of sensor networks,which effectively solves the problems of cluster head election and path planning during node data transmission.The simulation results show that the convergence speed of the EBG-PSO algorithm has been improved.And the introduction of new algorithms,it can be better to solve the problems of cluster head election and node routing planning,the utilization of node energy has been improved,which verifies the effectiveness of the EBG-PSO algorithm.(3)Aiming at the problems of short node life and poor network coverage in the later stage of communication in wireless sensor networks,a multi-objective optimization of mobile convergent nodes based on improved particle swarm algorithm is proposed for research.The region segmentation algorithm is introduced to cluster network nodes,and a multi-objective fitness function is designed to compatible with network node energy consumption and coverage.The improved particle swarm algorithm is used to obtain the optimal fitness value.Simulation results show that the network communication cost of each node can be effectively balanced by optimizing the moving path of the sink node,and in the later stage of the node communication,the speed of the reduction of the network coverage area is also reduced.(4)The traditional clustering algorithm and the typical improved algorithm are analyzed and compared,and MATLAB software is used to simulate and analyze the proposed algorithm and other improved algorithms mentioned in the article.From the aspects of network survival time,node energy consumption balance,later network coverage,discrete type between cluster heads,etc.The analysis is carried out to verify the effectiveness of the algorithm proposed in this paper. |
PDF Full Text Request