Research On Node Location Algorithm Based On Distance Vector

With the rapid development of wireless sensor network technology,its application in people’s life service is becoming increasingly prominent.Wireless sensor technology can be used in a variety of fields,such as medicine,science,the military,weather forecasting and commercial trade.At present,node positioning technology is one of the more popular research contents in sensor networks,and the algorithm of Distance Vector-Hop(DV-Hop)has the advantages of wide application range,low energy consumption,simple algorithm and easy improvement,so it has been widely concerned.This paper introduces the hop adjustment factor and the normalized weighting coefficient to correct the minimum hop error and the average hop distance error existing in the traditional DV-Hop algorithm.At the same time,considering that the sensor nodes will have different network environments when deployed,this paper first establishes the beacon node information list,and then uses the improved K-means clustering method to cluster the sensor nodes in different regions,and modifies the beacon node selection strategy,Then the error between sensor nodes is corrected by calculating the hop adjustment factor and the normalized weighting coefficient.The simulation results under the overall network structure and complex network structure show that the algorithm can achieve 83.43% and 80.27% positioning accuracy,which is 24.73% higher than the original positioning algorithm,laying a foundation for the following optimization intelligent algorithm.Aiming at the error of traditional DV-Hop algorithm in calculating the coordinates of unknown nodes,this paper introduces tabu search hybrid differential evolution algorithm to optimize the original algorithm.Differential evolution algorithm has the characteristics of high optimization efficiency and strong robustness,but in practical applications,it often produces the problem of local optimal solution.Therefore,it is necessary to introduce tabu algorithm to further improve the differential evolution algorithm.Tabu algorithm can avoid the repeated search process in local search,iteratively optimize the set fitness function,and finally output the coordinates of the nodes to be measured when the algorithm meets the iterative requirements.The simulation is carried out in a variety of different scene areas.The experimental results show that when the number of beacon nodes,the communication radius of nodes,the range of monitoring area,and the total number of nodes are changed respectively,compared with the classical DV-Hop algorithm,the DV-Hop algorithm based on differential evolution algorithm,the DV-Hop algorithm based on improved particle swarm optimization algorithm,and the DV-Hop algorithm based on improved firefly swarm optimization algorithm,The average positioning error of the improved DV-Hop algorithm has been reduced by 35.47%,19.53%,18.17% and 9.84% respectively,which has been significantly improved.