Research On Node Localization Algorithms In Anisotropic Wireless Sensor Networks

Wireless Sensor Networks can be used in so many applications that it is named as one of the technologies that can strongly affect mankind lifestyle. In many applications, it is very important to know the positions of the nodes that have detected the interested events while these nodes do not know their positions in advance. So, node localization becomes one of the most important problems that must be solved in wireless sensor networks.The existing node localization algorithms in wireless sensor networks are almost suited to isotropic networks. With respect to this situation, QDV-Hop algorithm, which can be used in anisotropic networks, is proposed in this paper. First of all, the research background and meanings of node localization algorithms are introduced, and next the basic theory of node localization and several typical localization algorithms. Then the processes of centroid algorithm and DV-Hop algorithm are introduced in detail. Based on the two algorithms, some improvements are proposed. By being improved, DV-Hop algorithm can estimate the average hop distance in anisotropic networks reasonably and centroid algorithm can raise the localization accuracy. The process of average hop distance estimation makes DV-Hop algorithm can be only used in isotropic networks. To make DV-Hop algorithm suited to anisotropic networks, the following two improvements are proposed. On one hand, when calculating the average hop distance, an anchor only uses the information of its nearest three other anchors. On the other hand, an unknown node uses different average hop distances when calculating the distances it is to different anchors. To raise the localization accuracy, weighted centroid algorithm is proposed. When localizing a node using weighted centroid algorithm, different weights are given to different anchors according to the distances between the unknown node and these anchors. In weighted centroid algorithm proposed in this paper, the unknown node only uses the information of its nearest three anchors. By doing this, the localization accuracy can be raised while the computing decreases. At last, improved DV-Hop algorithm and weighted centroid algorithm are combined together into QDV-Hop algorithm, which estimates the distances between nodes using the distance estimation process of improved DV-Hop algorithm and localizes the unknown nodes using the position estimation process of weighted centroid algorithm, and some simulations are made in Matlab environment. The simulation results show that QDV-Hop algorithm can be used in C-shape networks, which are anisotropic networks, and has good accuracy when the anchors are distributed in regular grid form.