WCGDV-Hop Localization Algorithm For Wireless Sensor Networks

Wireless sensor network (WSN) is composed of a large number of low-cost microsensor nodes which are deployed in monitoring areas, forming a multiple hopsself-organizing network system by wireless communication mode, and WSNs are used toperceive, collect and process perception object information in monitoring areascollaboratively, then sent those information to an observer. For those information, the nodeposition is a very important part of monitoring information, and the related positioninformation is nonnegligible to monitoring activities of wireless sensor network. Inwireless sensor network, the monitoring activities without position information aremeaningless. Therefore, whether to acquire the node position of sensory information hasbecome a key issue for research on wireless sensor network.According to measure actual distance between nodes or not, wireless sensor networklocalization algorithms can be divided into range-based localization algorithm andrange-free localization algorithm. Range-based localization algorithm’s location accuracyis higher, but need additional equipment to measure the distance between nodes, at theexpense of the network cost and energy consumption. Range-free localization algorithmneedn’t additional equipment to realize localization, possessing the advantages of low costand low energy consumption and the disadvantage of lower location accuracy, but thelocation accuracy will still be able to meet the demand of the application of wireless sensornetwork. Therefore, range-free localization algorithm is currently the focus of attentionand DV-Hop localization algorithm as the classic range-free localization algorithm hasattracted the research interest of many scholars.The paper carried on intensive reseach to the location principle of DV-Hoplocalization algorithm and thorough analysis to the error source of DV-Hop localizationalgorithm, and put forword that the influence factors of DV-Hop localization algorithmerror magnitude included external objective factors and internal subjective factors.External objective factors determined by the network setup and deployment situation wasinevitable, and internal subjective factors determined by the principle of localizationalgorithm could be changed to reduce the error by improving the algorithm. Therefore,starting from the internal subjective factors influencing DV-Hop localization algorithmerror magnitude: average every hop distance, hop distance and coordinate calculationmethod, for the purpose of reducing the node location error, increasing the algorithm location accuracy and improving the DV-Hop localization algorithm, the paper put forwarda genetic optimization DV-Hop localization algorithm based on error distance weightedand hop algorithm choiced, namely WCGDV-Hop localization algorithm.Based on DV-Hop localization algorithm, WCGDV-Hop localization algorithm hadmade three improved points:Improved point one: for the unknown nodes’ average every hop distance calculationmethod of DV-Hop localization algorithm, the paper put forword an average hop distancecalculation method based on error and distance weighted. Unknown node accorded to theaverage every hop distance error of the first received nearest three anchor nodes and thehop distance between them to the unknown node to calculate the anchor nodes’ weights,and then through to the average every hop distance of the three anchor nodes donenormalized weighted processing to calculate its own average every hop distance.Improved point two: for the hop distance calculation method between unknown nodesto all abchor nodes of DV-Hop localization algorithm, the paper put forword a selectivehop distance calculation method based on position judgement. First according to all anchornodes’ position unknown node judged all anchor nodes as the nearest anchor node or theother anchor nodes, and then according to the different types of anchor nodes unknownnode choiced the different algorithm to calculate the hop distances between all anchornodes to the unknown node.Improved point three: for the unknown nodes coordinate calculation method ofDV-Hop localization algorithm, the paper put forword an unknown nodes coordinatecalculation method based on improved genetic algorithm optimization. First unknownnode calculated its coordinate by using the maximum likelihood estimation method, andthen used the improved genetic algorithm for the unknown node coordinate to carry on theiterating genetic operation. Each generation genetic operation included crossover, mutationand selection. After each generation operation the algorithm choosed individual to heredityto the next generation according to the fitness function value of individuals, and thencontinued the new generation genetic operation. When reaching the terminate algebra thealgorithm stoped genetic operation and output the optimal solution.Using MATLAB7.10.0to compare the simulation results of WCGDV-Hoplocalization algorithm and DV-Hop localization algorithm, the simulation results show thatWCGDV-Hop localization algorithm has better average location error and locationaccuracy than DV-Hop localization algorithm, and reduces the node average location errorand increases the algorithm location accuracy.