Cluster based localization algorithm in wireless networks

Given the locations of a small number of reference anchor nodes and the distances between neighbour nodes, various localization algorithms for wireless networks have been proposed. In this paper, we carry out a comparative evaluation of three different cluster based localization algorithms. The three different algorithms are based on the use of extended Kalman filter (EKF), semi-definite programming (SDP) and multi-dimensional scaling (MDS). Their cluster based variants are the decentralized EKF (DEKF), cluster based SDP (CSDP) and cluster based MDS (CMDS), respectively. The algorithms are evaluated in both static and low mobility environments. Simulation results show that DEKF performs as well as EKF in both static and low mobility environments, and they outperform CSDP and CMDS. DEKF requires less anchor nodes, smaller cluster, while achieving more accurate location estimation.