Localization Algorithm For WSN Based On Sparse Anchors

WSN (Wireless Sensor Networks) has become the integrate of information, intelligence and networking, and an important research direction. WSN achieves obtaining, processing, transmission of information, intelligent of system control, and provide convenience for exchange of information between people or between human and machines. WSN has been applied in many fields such as military defense, environmental monitoring, transportation dredging and medical monitoring.Node localization is the base and core of WSN, and has attracted wide attention. Node localization technique is aimed to determine the position of the monitoring events and objectives, or the sensor nodes that obtain information. For environmental monitoring, it is necessary for the location of environmental information; for emergency incidents, it is important for the location of the fire, earthquake prediction, and the scope of the enemy chariot activities and so on. Therefore, the monitoring information of no location information is meaningless, which means that the sensor nodes have to locate themselves before determining the location information of the event or target.In this thesis, it is studied about the RSSI range-based localization algorithm for sparse-anchors network. The main work is as follows:For positioning errors and sparse anchors, using the minimized-stress search algorithm can avoid the error when adjacent anchor distance equation of the AHLos algorithm is ill, and decrease positioning error greatly; using the collaborative algorithm has an excellent positioning effect on low density of anchor, specifically on marginal sensor nodes. Through MATLAB, the simulation results verified validity and accuracy of the algorithm, and the localization error of the algorithm is smaller than AHLos algorithm and improves the localization success rate.To the sparse anchors situation, for the error accumulation of trilateration and multilateration algorithms, the Particle Swarm Optimization (PSO) algorithm is introduced. The paper deeply studies the enhanced localization algorithms based on PSO, the one core of which is to improves localization success ratios by using the location data of remote anchors which is provided by the closest neighbor nodes of an unknown node to calculate the locations of unknown nodes with insufficient anchor nodes; the PSO algorithm is employed to increase localization accuracy and the DV-distance approach is applied to further boost up the success ratios of localization. Through MATLAB, the simulation results verified validity and accuracy of the algorithm, and the algorithm has smaller error and improves the localization success rate.