Cluster-based Three-dimensional Localization Algorithm for Large Scale Wireless Sensor Networks

Abstract

In wireless sensor networks, three-dimensional localization is important for applications. It becomes a challenge with the scale of network getting large. This paper proposes a three-dimensional localization algorithm for large scale WSN on the basis of cluster. Focusing on the MDS-based localization, it adopts the cluster structure and the global coordinate system to represent the whole network logically, and reduces the influence of range measurement errors through decreasing the probability of multi-hop. With the combination of variable power of nodes and the triangle principle, the range measurement errors can be corrected. Through the comparison of three different computations in the algorithm of simulations, correction effects are presented. To address the proposed algorithm, CBLALS, more convincible, the comparison of CBLALS and DV-Distance (3D) is taken. The result shows that the positioning accuracy of CBLALS is much better than the one of DV-Distance (3D). With the increasing of the range measurement error, the positioning error of CBLALS varies gently, and could be controlled within 55% while the range measurement error is 30%.