Research On Routing Clustering Algorithm For Wireless Sensor Networks

Wireless sensor network(WSN)is mainly used in data information acquisition and data information processing.It is a combination of wireless communication,sensors and embedded technologies.Wireless sensor networks have important application value in many application fields.However,since wireless sensor networks are composed of many small and energy-limited sensor nodes,so designs a routing algorithm that effectively saves energy consumption and prolongs network lifetime is the key research direction of wireless sensor network.This thesis analyzes the specific workflow of LEACH algorithm and finds that LEACH algorithm is random in the cluster establishment stage.It does not consider the influencing factors such as residual node energy,node transmission distance and neighbor node density,resulting in improper selection of cluster heads and causing the problem of excessive nodes energy consumption.To this end,in order to slow down the network energy consumption rate and improve the survival time of wireless sensor network,this paper designs GABP-optimized routing clustering algorithm and routing clustering algorithm based on immune and BP.In the establishment stage of the cluster,first applying the genetic algorithm the characteristics of the global search optimal solution,and the residual energy of the nodes,the transmission distance of the nodes and the influence factor of the adjacent node density are introduced to select the cluster head nodes,and the shortest data forwarding distance is calculated to select a single hop or multi-hop communication.Then,in the data transmission phase,a three-layer BP neural network data fusion model is used to fuse the data information of the member nodes in the cluster,and the energy consumption of the cluster head nodes is reduced.Finally,the simulation experiment shows that the network survival time of the GABP-optimized routing clustering algorithm is 44.12% higher than that of the LEACH algorithm,and the nodes energy consumption is more balanced.In the further improved GABP optimized route clustering algorithm,the immune algorithm with stronger global search ability is introduced,and the nodes residual energy,transmission distance and neighbor nodes density are considered to elect the cluster head nodes,and BP neural network data fusion model is applied in the data fusion of the member nodes in the cluster in order to reduces the cluster head nodes to bear too much data information,and at the same time optimizes the path of data transmission of all cluster head nodes by using the immune algorithm.After verification by simulation experiments,the routing clustering algorithm based on immune and BP than the GABP-optimized routing clustering algorithmis is extended by 32.7% in the network life cycle and the energy consumption of the network is reduced by 67%.