Optimization Research On Location Algorithm Of Wireless Sensor Networks Based On DV-Hop

Wireless sensor networks have a very broad application prospects in many fields,among which the node location technology is one of the key technologies in WSN.The DV-Hop positioning algorithm is an algorithm widely used in the technology.However,due to the uniformity of the node distribution,the positioning accuracy of the algorithm has a certain influence.Therefore,improving the accuracy of DV-Hop positioning algorithm becomes an important research topic.This thesis expounds the main problems in DV-Hop positioning algorithms,and focuses on the improvement measures to improve the accuracy of the DV-Hop positioning algorithm.(1)In order to reduce the node positioning error caused by minimum hops and average hop-size,improved DV-Hop positioning algorithm based on hop count refinement and correcting average is proposed.RSSI distance measuring technique is introduced to modify the minimum hops.The average hop-size is corrected by the weighted mean of the jump distance error and the estimated distance error.(2)In order to reduce the node positioning error caused by the least square method used for calculating the position of an unknown node,a new DV-Hop positioning algorithm based on Genetic PSO for Simultaneous Changes of Segmented Weights and Learning Factors is proposed.The genetic-improved PSO and the segmented weight are employed to replace the least square method.(3)Combining the two proposed algorithms,a new DV-Hop positioning algorithm based on RA-GAPSO optimization is proposed,and the RA-DV-Hop positioning algorithm is optimized using GAPSO algorithm.The three optimization algorithms proposed in the paper are simulated on the MATLAB platform from three aspects: the number of anchor nodes,the communication radius,and the total number of nodes.Simulation results show that the localization error of the improved algorithm is smaller than that of the classic DV-Hop positioning algorithm,and the DV-Hop positioning algorithm based on RA-GAPSO optimization has the smallest positioning error and the highest positioning accuracy.