WSN Mobile Node Localization Research Based On PSO And MCB

At present, node localization of WSN (Wireless Sensor Network) is mainly concentrated in the static node positioning; the positioning of the mobile node is relatively small. In view of the different application scenarios, the deployment of the network will be different. If add some mobile nodes into static wireless sensor network, we can effectively make up for static wireless sensor network unadaptable, unable to network dynamically, further expand the range of application and strain capacity of WSN, and make the wireless sensor network deploy more flexible. Therefore the localization of mobile nodes in wireless sensor network has important significance.The paper main research work can be summarized as follows:(1) The paper describers the basic concepts and theories of the localization of nodes in WSN, classifies the current mainstream localization algorithm, and compares and analyses the research status of the localization of mobile nodes. And then focuses on the advantages and disadvantages of the MCL (Monte Carlo Localization) algorithm and the MCB (Monte Carlo Localization Boxed) algorithm in the mobile node localization.(2) The paper proposes one kind of PSOMCB mobile node localization algorithm based on the idea of MCB. In order to solve the problem of low sampling efficiency and low positioning accuracy of MCL, PSOMCB first use of the movement model of the mobile node and MCB to predict the estimated location of the mobile node, and define the location correction function by measuring the distance between the mobile node and anchor node, after that make use of PSO (Particle Swarm Optimization) algorithm to optimize that function fleetly, and then find out the optimal position of the mobile nodes. The result of simulation experiments shows that PSOMCB algorithm is better than MCL algorithm and MCB algorithm in the positioning accuracy.(3) The paper proposes PSOMCB-SA hybrid mobile node localization algorithm based on the idea of SA (Simulated Annealing) algorithm. In order to further improve the global optimality and robustness of the PSOMCB algorithm, the mechanism of SA is introduced into the paper. PSOMCB-SA algorithm not only make use of the fast convergence ability of PSO algorithm, but also has the probabilistic jumping property of simulated annealing algorithm and can effectively avoid the algorithm fall into a locally optimal solution, and also it has a higher level of robustness. The experimental results show that PSOMCB-SA algorithm performance of localization is superior to PSOMCB both in the accuracy and the speed.