Research On Node Localization Algorithm In Wireless Sensor Networks Based On Improved Salp Swarm Algorithm

The development of modern network and communication technology bring great convenience to the society,connecting the equipment and places all over the world.Among them,the wireless sensor network technology can serve many objects,and the working environment's requirement is not high,which makes it a visible trend to integrate into the modern society on a large scale.For this new technology that changes people's daily life,academic circles have carried out a variety of in-depth research,in which the localization algorithm of wireless sensor network node occupies a very important position.In this paper,two typical localization algorithms are selected as the optimization objects,and the improved meta heuristic algorithm is used to improve the accuracy of localization.(1)Firstly,the wireless sensor network is summarized by consulting the literature.Secondly,the positioning technology is summarized,and the mathematical principles involved are introduced in detail.Finally,two kinds of localization algorithms about range-based and range-free are introduced.(2)In 2017,Deb laboratory proposed the salp swarm algorithm,which was inspired by a mollusk in the ocean,and designed this new optimization algorithm by simulating its behavior.Compared with most swarm intelligence algorithms,this algorithm is easy to code and implement,with simpler structure and fewer input parameters,but it also has some problems,such as lack of mutation mechanism,easy to fall into the premature trap and so on.In order to solve these problems,this paper proposes two improved algorithms.The first one is based on the loser-out-tournament and elite guide mutation algorithm(LGSSA).The algorithm does not use random population at the beginning,but uses cat chaotic map to produce,so that the initial solution in the search space can be as diverse as possible;At the same time,elite guide mutation and loser-out-tournament are used in the evolution,which improves the speed and accuracy of the algorithm and improves the stability.The second is the salp swarm algorithm which integrates fluid search(FSSA).Aiming at the problem of high randomness of leader's search in the optimization,fluid search strategy is adopted to make the swarm easier to evolve towards the correct goal.Through the experiments on benchmark function and engineering problems,the results of meta heuristic algorithm are obviously inferior to LGSSA and FSSA,which shows that the improved algorithm has advantages in convergence accuracy,optimization speed and stability.(3)DV-Hop localization algorithm and RSSI localization algorithm are two typical localization algorithms with different mechanisms,which have different application conditions and error sources.In order to reduce the error of localization algorithm,LGSSA-DV-HOP localization algorithm and FSSA-RSSI localization algorithm are proposed.The error of DV-HOP location algorithm is mainly caused by network connectivity,while LGSSA algorithm can reduce the sensitivity of least square method to error.Simulation results show that the improved algorithm can effectively improve the positioning accuracy of the node.The error of RSSI algorithm is mainly the error of RSSI ranging.Aiming at the problem that its positioning effect is greatly affected by ranging,FSSA algorithm is used for optimization,and the accuracy is higher than RSSI algorithm.PSO-RSSI algorithm,SSA-RSSI algorithm and FSSA-RSSI algorithm have the best positioning performance.