Localization in WSN using maximum likelihood estimation with negative constraints based on particle swarm optimization

In this paper, we propose a maximum likelihood estimation approach with negative constraints to realize the localization of the unknown nodes in wireless sensor network. The main work can be divided into three parts: firstly, we measure the distance based on received signal strength from the nodes. Secondly, a series of positive and negative constrains are combined to build the modeling using the maximum likelihood estimation. Finally, particle swarm optimization is employed to find the optimal position. The simulation results show that the proposed approach outperforms some existing localization algorithm without negative constrains.