Actor Node Mobile Localization And Path Optimization Algorithm In Wireless Sensor And Actor Network

On the basis of Wireless Sensor Network(WSN), Wireless Sensor and Actor Network(WSAN) is developing rapidly, which not only can receive data that sensor nodes collect information from the external environment, also can take action and operate events timely after the monitoring data has been analyzed. WSAN improves the feedback ability of awareness events that has a wide practical prospects and theoretical prospects. In WSAN, in order to get events positioning, localization remains a key technology of the network. In this paper, we summarize the current node localization algorithms. Using the mobility of Actors in WSAN, Actors act mobile anchor nodes for location. Actors need to select the virtual beacon nodes of low energy consumption and positioning accuracy in the process of mobile location.Therefore, we discuss and study the issue that how do we select virtual beacon in path planning. For the selected virtual beacon nodes, we plan an optimal path of movement.This paper analyses architecture of WSAN, characteristic and main problem, introduces various localization algorithms of WSAN, mainly summaries the current path planning method. First, in order to solve the repeated traversal problem of DFS algorithm, a dynamic path planning algorithm based on optimized DFS is proposed. This algorithm uses to select virtual beacon node location. The algorithm uses the principle of tabu search and node weigh to limit virtual beacon nodes was selected and solves the problem of node repeated traversal. Extensive simulation results confirm that the optimized DFS algorithm reduces the number of virtual beacons under the same communication radius and location proportion can reach more than 60%. Optimized DFS algorithm achieves the purpose of energy conservation and improves the network lifetime. Second, we introduce ant colony algorithm and mainly describe the basic principle and algorithm of ant colony algorithm model, we analyze the problems of the ant colony algorithm and propose the algorithm called a dynamic path planning algorithm based on optimized ant colony optimization. The algorithm uses the hops and node received signal strength to limit the path chosen and finally outputs the best of path. The algorithm solves the slow speed of convergence and the problem of easy to fall into optimal solution. Simulation experiments show that the path length of the optimal algorithm is less than the path length of the traditional algorithm. To some extent, simulation experiments show the effectiveness of the improved algorithm.