Location Technology Based On Hybrid Particle Swarm Constrained Optimization For WSN

Wireless Sensor Network (WSN) as a newly developed multidisciplinary technology, has a wide range of application in environment monitoring, military, healthcare and so on. In practical application, WSN usually spread in either hostile environment or dangerous area. Regardless of which area is, the network nodes are randomly dropped, and their location can hardly be found beforehand. Therefore, in most wireless sensor network, the information has scarcely any practical value, if just obtain the information without knowing where it come from. As a result, in the practical application, it is of great importance to choose an appropriate location technology to successfully complete the nodes localization in WSN.According to different localization mechanism, the current location technology can be divided into range-based location technology and range-free location technology. Through the analysis of various typical location technology, the range-based location technology and the DV-Hop range-free location technology consist of obtaining the distance between the nodes and localization calculation. The least square method is generally selected, which is as much as possible using the location information of anchor node of location system in the practical application. Through the location calculation principle of the least square method, it is known that the location accuracy of the least-square method is rather sensitive to measurement error between the unknown node and a random anchor node. Even under the condition that the measurement error between the rest of anchor node and the unknown node is very precise, if the measurement error between the unknown node and the anchor node is relatively large, the accuracy of the location technology is also low.In order to overcome the deficiency of the least square, this paper transforms the problem of location technology using the least square method into a constrained optimization problem of measurement distance and measurement error.In order to solve the problem above, after a thorough understanding about the optimal performance of Particle Swarm Optimization (PSO) and Simulated Annealing (SA) and according to the feature of faster of PSO and the feature of SA, the paper put forward the hybrid Particle Swarm Optimization (SA-PSO), which is using retention factor a to create new individual optimal extremum and global optimal extremum and introducing the Metroplis selection of SA to accept the new created solution at a certain probability as the current individual optimal extremum and global optimal extremum. Simulation experiments show that the hybrid Particle Swarm optimization has better optimization performance and faster convergence speed.Since intelligent algorithm itself does not have the ability to deal with constraint conditions, this paper selected a feasibility principle with less parameters to solve the problem of intelligent algorithm dealing with constrained optimization. The method use retention factor a to keep the information of individual optimal extremum and global optimal extremum,1-α to keep the individual optimal infeasible solution and the global optimal infeasible solution to overcome the deficiency of feasibility principle of the optimum in the boundary of constraint conditions, to reduce probability of PSO running into local optimum extremum by means of the method. The results indicate that the hybrid PSO can find out better optimum.Finally, the SA-PSO algorithm is applied to the node localization in WSN, which is based on a constrained optimization problem of measuring distance and measurement error. Experiments show that the location technology based on hybrid Particle Swarm Constrained Optimization algorithm help to curb the interference of the ranging error superposition, improves the accuracy of positioning technology.