Research On Localization Algorithm Based On Path Planning Of Mobile Node

In most application fields of wireless sensor networks, it is essential to obtain the location information of the nodes due to location information is the premise and basis for most applications. Therefore, studies on location technology have a far-reaching significance for the development of sensor networks and even the whole of the human.Since the traditional location technology for wireless sensor network mainly concerned on the static network, thus the nodes can’t change their position once deployed. However, the wild-environment is more complex, the layout costs of the nodes, the interference of the obstructions and the restriction of energy consumption would seriously affect the location performance of whole networks. For this situation, a location algorithm based on the mobile beacon with optimal path has been presented. Thus, this paper will design reasonable mobile path which not only can make as many as possible nodes to realize localization, but also can greatly reduce network construction costs and the impact of obstacles, accordingly, improve the location efficiency of the nodes. These major works are:Firstly, based on analysis of localization algorithm for wireless sensor network, combined with several performance evaluations of localization technology, this essay focused on analysis and comparison of the performance of typical Range-free localization algorithms.Secondly, for the deficiencies of the traditional location technology for wireless sensor network, this paper proposed a method of using mobile beacon to locating. In general, the moving path of mobile beacon will have a direct effect on the location performance of networks. For this reason, based on analysis of several typical path planning methods, this article proposed a new path planning method which based on improved ant colony algorithm. It has outstanding advantages in path planning as an optimization algorithm. The algorithm choose the right path through perceiving the size of the pheromone concentration, and then by continuous iteration to obtain the optimal solution. However, ant colony algorithm also has some flaws, such as slow convergence, easily trapped into local optimal solution and so on. Therefore, we proposed an improved ant colony algorithm based on the angles selection strategy. On the one hand, by setting the threshold of pheromone concentration can avoid search stagnation; On the other hand, by limiting the search angle of ants to avoid search the worst path, thereby improving the convergence speed of algorithm; In addition, by joining the angle regulating factor can continuously renew the probability of path selection, which was conducive to choosing the optimal path and avoiding fall into local optimal solution.Thirdly, we proposed a DV-Hop algorithm based on mobile nodes, which using the improved ant colony algorithm for path planning. The algorithm use a mobile beacon to move in the network according to a predetermined path, then stopped at a fixed time and broadcast its location information that create several virtual beacons. Unknown nodes can obtain information from virtual beacon nodes, which includes hops and the average distance. Subsequently, they utilizes these information to realize self-localization. In order to obtain more accurate localization, we correct the initial position through Taylor series expansion method.Finally, we simulated node localization algorithm based on improved ant colony algorithm, node localization algorithm based on traditional ant colony algorithm, node localization algorithm based on the Gaussian model and traditional DV-Hop algorithm in MATLAB simulation environment. The simulation results show that whether in terms of location accuracy or coverage rates and energy consumption, the first three kinds of the mobile localization algorithm is superior to the traditional DV-Hop algorithm. While compared with the other two mobile localization algorithm, the localization algorithm based on improved ant colony algorithm not only improves the location accuracy and coverage rates, but also reduces energy consumption and improves the overall location performance of networks.