Localization Optimization Algorithm Without Ranging Based On WSNs

The wireless sensor network is a distributed communication transmission network in which nodes cooperate to transmit information to perceive the environment.The main function of the sensor node is to collect data information in the surrounding environment.Usually,if the location of the sensor node is unknown,the monitored data will be meaningless.Due to the high cost of installing GPS module for each sensor node,many localization algorithms unique to wireless sensor networks are proposed.According to whether additional hardware or equipment is needed to assist positioning,localization can be divided into range-based and range-free technologies.Distance vector-Hop algorithm(DV-Hop)has become one of the widely concerned range-free localization algorithms because it only depends on the connectivity of the whole network,does not require additional ranging hardware and has high robustness.However,due to its low accuracy and poor stability,improving the performance of DV-Hop localization algorithm has become a hot research topic for scholars.The existing improved DV-Hop algorithm is mainly reflected in three aspects: hop count,average hop distance and position calculation.This paper proposes two improved DV-Hop algorithms for different scenarios.(1)DV-Hop localization algorithm based on adaptive differential particle swarm optimization(ADPDV-Hop)is proposed for the network topology with regular region,small scale and high positioning accuracy.First,the algorithm uses RSSI technology to refine the hop count in one hop range.Then,the hop distance credibility is used to give different weights to the path of the node to be tested the target anchor node to reduce the error caused by the broken line distance instead of the line distance.According to different effects of different anchor nodes on the unknown nodes,different weights are given to anchor nodes to reduce the error caused by the average hop distance.Finally,the adaptive differential particle swarm optimization algorithm is used to estimate the coordinates of unknown nodes.This improved algorithm is suitable for the network topology with rule area and small scale.The simulation results show that the positioning accuracy of the improved algorithm is significantly improved compared with the tradition DV-Hop and other improved algorithms.(2)DV-Hop localization algorithm based on dynamic average hop distance and RANSAC optimization(DRDV-Hop)is proposed for irregular and large-scale network topology.First,the algorithm uses hamming distance to judge path similarity,and selects the anchor node-anchor node path that is most similar to the unknown node-anchor node path to find the average hop distance of the unknown node.Then the RANSAC algorithm is used to determine and select reliable anchor nodes based on the threshold to estimate the unknown node coordinates.Finally,the coordinates of the unknown nodes obtained by each sample are compared,and the final coordinates of the unknown nodes with the least error are selected.Since the unknown node is located by the reliable anchor node,the average hop distance of the unknown node is also obtained by the path of the most similar anchor node pair,the improved algorithm greatly reduces the dependence of node location on the network topology.The simulation results show that under the topology of O-type,C-type and X-type network,the positioning accuracy of unknown nodes is higher than that of traditional DV-Hop and other improved algorithms.Figure[27] table[8] reference[62]...