Intelligent-searching Location Algorithm Research Of Wireless Sensor Network

Wireless sensor network is a comprehensive network system that integrates monitoring,control,and wireless communication,it is supported by multiple technologies.Among them,node localization technology is one of the key technologies of wireless sensor network,accurately obtaining the location information of the nodes is of great significance for the entire network information collection.At present,the research of location algorithm based on swarm intelligence algorithm has become a hot research topic.The introduction of swarm intelligence algorithm in the positioning algorithm can effectively reduce the node position calculation error and improve the positioning accuracy.According to the characteristics of different swarm intelligence algorithms,the thesis makes corresponding improvements to the positioning algorithm to be suitable for different positioning scenes.The main research contents and research results of this thesis are as follows:(1)Firstly,corresponding node positioning models are established for two different positioning scenes,the node localization problem is converted into a function optimization problem,then use the optimization and efficient swarm intelligence algorithm to solve the function optimization problem,complete the determination of unknown node location.(2)Secondly,in order to solve the problem of low localization accuracy of the centroid algorithm in the sparse node distribution area,this thesis proposes an improved bat algorithm.The improved algorithm reduces the initial search area of bats,optimizes the random vector ?,introduces an inertial weighting factor into the position update formula,then applies the improved bat algorithm to the centroid algorithm positioning model.The simulation results show that the algorithm can effectively improve the positioning accuracy of nodes,and is more suitable for positioning scenes with less noise interference and sparse node distribution.(3)Finally,in order to solve the problem of unsatisfactory localization performance in large noise interference scenes,this thesis proposes an improved chicken swarm optimization.The improved algorithm optimizes the individual selection method of flocks based on the distance between flocks,adds a random walk strategy to the position update formula of the hen,introduces the idea of net energy gain in the position update formula of the chicken,then applies the improved chicken swarm optimization to the DV-Hop algorithm localization model.The simulation results show that the algorithm can effectively improve the positioning accuracy of nodes and is more suitable for positioning in scenes with large noise interference.