The Research On Location Algorithm Based On DV-Hop In WSN

Wireless sensor network can broaden the people’s ability to obtain information, it can obtain a lot of objective physical information. WSN has a broad application prospect. At present, it is one of the most influential technology. Node location technology is the key technology of WSN, as well as the premise of all other technologies. Detecting system can make the corresponding judgment based on the feedback information when it takes the specific location of the monitoring nodes.Node localization technology of WSN is divided into node localization based on distance and node localization without distance. Node localization based on distance has a high positioning accuracy, but these methods have a high hardware cost and a large consumption, they aren’t suitable for application in a real environment. Therefore, node localization without distance is widely used. The main works as follow:(1) After analyzing and simulating several common location algorithm, drawing out the form about performance of TOA, TDOA, AOA, RSSI, centroiding algorithm and DV-Hop algorithm, then improving the high-performanced DV-Hop location algorithm.(2) In the second stage of traditional DV-Hop localization algorithm, unknown node seem the first received average hop distance from the beacon node as its average hop distance, this way can bring a large error algorithm. In order to reduce the algorithm error, using the two improved methods. Method one:average hop distances of all the beacon nodes multiplied by the corresponding weights, then add them up, the value is the unknown node’s average hop distance; method two:nodes calculate average hop distance and error of average hop distance, then add them up, the value is the average hop distance of corresponding hops. Through simulation with MATLAB, the performance of the two kinds of improved algorithm are better than the original DV-Hop algorithm, method two has the better performance than the method one.(3) In the third stage of traditional DV-Hop localization algorithm, least squares method solves the nonlinear constraint problem can bring a large error algorithm. In order to reduce the algorithm error, with hybrid genetic algorithm combined with simple ideas instead of least square method. Genetic algorithm is very suitable for solving the global optimal solution in combination and optimization problem, it can accurately obtain the optimal solution, but the operation time is long and the local search ability is poor. The principle of saving the best individual is adapted to reduce the operation time, and combined with the simplex algorithm to improve the local search ability. Through simulation with MATLAB, after the improvement in the third stage, positioning error is lower and coverage is greater. The performance of the total improved algorithm is best.