A Research Of Acclimatization Localization Algorithm In WSN

Self-localization of sensor nodes is one of the main technique of WSN. Exact localization is the precondition of the data which gathered.by all the nodes in the WSN. Self-localization of sensor node is to get the position information of node by using information of some already known nodes. Only if the each node accomplish self-localization, the position of the affair which surveyed by the WSN can be ascertained, it can make the data gathered more effect.By the way of combining RSSI method and Centroid-Weighted algorithm, the dissertation proposes a new localization algorithm. The localization process is divided into two parts:Distance-measured and computing. In distance-measured phase, the thesis constitute the model of the pathway-ullage around each anchor node by the relationship between the signal strength and distance among the anchor nodes, and the result is the pathway-ullage model for each specifically anchor node. Then each anchor node send it's self-information conclude the result to all the unknown nodes. After receiving this information, each unknown node compare the RSSI of it with the original RSSI, then account the distance between itself and each anchor node by the pathway-ullage model of the anchor node. In localization phase, the thesis divide all the anchor nodes into several groups, constitute Centroid-Weighted model between the unknown node and each group of anchor nodes, then the coordinate of the unknown node could be accounted by the distance of the unknown node and each anchor of the group. Afterward, accounting these groups of coordinate with Centroid-Weighted algorithm, The algotithm can obtain the accurate coordinate of the unknown node. The excellence of this algorithm is it can constitute different models with different anchor nodes, this can ensure a majority of unknown nodes ascertain their positions in the area with different parts, buildup the acclimatization of the algorithm.Finally, the thesis evaluate the performance of the proposed algorithm from simulation experiments, and compare it with other similar localization algorithm. It is demonstrated that the proposed algorithm has high accuracy.