A High Density WSN Cluster Positioning Approach

Abstract

Node self-positioning is one of the supporting technologies for wireless sensor network applications. In this paper, a clustering localization algorithm is proposed for large-scale high-density wireless sensor networks. Firstly, the potential of the node is defined as the basis for the election of the cluster head. The distance between the nodes in the network is calculated indirectly by the relationship between the received signal strength and the communication radius. The topology information in each cluster is saved by the cluster head, and the linear programming method is used in the cluster head to implement the cluster internal relative positioning. Then, from the sink node, the inter-cluster location fusion is gradually implemented, and finally the absolute positioning of the whole network is realized. Compared with the centralized convex programming algorithm, the proposed algorithm has low computational complexity, small traffic, high positioning accuracy, and does not need to know the signal attenuation factor in the environment in advance, and there is anti-noise ability.