Research And Improvement Of Centroid Positioning Algorithm And Routing Algorithm In WSN

IoT technology has the advantages of intelligence,openness and flexibility,and it is widely used in all aspects of production and life.It mainly acquires environmental information through sensor detection technology,and uploads environmental information to the cloud by using wireless communication technology,and realizes information sharing through cloud computing.In the Internet of Things technology,Wireless Sensor Networks(WSN)plays a vital role in the realization of its functions.The WSN node location and routing protocol is the key technology for sensor networks to accurately acquire and transmit data.The current positioning technology generally relies on GPS and other positioning systems and hardware devices to achieve accurate positioning,the cost is too high,and does not have universality.The WSN achieves positioning based on the connectivity and density of the nodes,and the cost is low,but the accuracy is poor.In large-scale WSN applications,routing protocols generally have problems such as excessive node energy consumption and death,and unstable network structure in sensor network data transmission.The paper aims at the problem of WSN node self-positioning and node energy loss:(1)A RSSI centroid localization algorithm based on hybrid group intelligent algorithm optimization is proposed.In this paper,the simulated annealing algorithm is introduced on the basis of the particle swarm optimization algorithm to solve the problem that the particle swarm algorithm is too early to fall into the local extremum point,and the convergence of the algorithm is improved.The method not only retains the advantages of few parameters,simple solution and strong global search ability of the particle swarm algorithm,but also combines the advantages of high operational efficiency of simulated annealing algorithm,which can effectively improve the positioning accuracy of RSSI centroid localization algorithm.In order to verify the effectiveness of the RSSI centroid localization algorithm based on hybrid cluster intelligence algorithm,the traditional centroid localization algorithm,the RSSI centroid localization algorithm and the RSSI centroid localization algorithm based on particle swarm optimization are used as the comparison method for simulation experiments.The experimental results show that the proposed algorithm not only has better performance and higher positioning accuracy,but also has the advantages of high algorithm stability and strong applicability.(2)An improved algorithm for LEACH routing clustering region based on cluster head optimization is proposed.The method selects the first round of cluster heads and theremaining rounds of cluster heads the distance from the node to the base station and the remaining energy of the nodes,and then it divides the uniform clustering area,sets the cluster head level,and forms a multi-level network structure according to the idea of dividing the clustered area of house structure in JC-LEACH,which solves the problem of the LEACH algorithm that nodes of energy consumption is too fast to die in the large-scale WSN application.In order to verify the effectiveness of the improved algorithm for LEACH routing clustering region based on cluster head optimization,the traditional LEACH algorithm and JC-LEACH algorithm are used as comparison methods for simulation experiments.The experimental results show that the algorithm can effectively reduce nodes energy consumption,increase node lifetime and extend network lifetime in large-scale WSN applications.