Localization Based on Probabilistic Multilateration Approach for Mobile Wireless Sensor Networks

Abstract

Localization is one of the main problems in Mobile Wireless Sensor Networks, since it provides the location of an event occurrence. This paper presents a performance evaluation of the localization algorithms: Multilateration Algorithm, Weighted Multilateration Algorithm and Probabilistic Multilateration Algorithm (PMA). In addition, we propose an Improved Probabilistic Multilateration Algorithm that decreases the localization error of the interest node by using an approach that computes iteratively the position of a node of interest until it reaches the solution that minimizes the localization error. The proposed approach regards the noisy environment by its impact on a correlation matrix that involves the variance of the separation distance between the node of interest and the respective reference nodes (RNs). Furthermore, we also introduce a constant parameter called damping factor; which enhances the convergence of the localization algorithm providing the solution that minimizes the localization error. In this study, we evaluate localization algorithms in a single-hop and multi-hop scenarios considering a distribution with solid geometry of the RNs and randomly distributed RNs in both scenarios. The results we obtained show that our proposed algorithm Improved PMA presents a better performance according to the Normalized Root Mean Squared Error varying the number of reference nodes and noise proportion.

Weighted Multilateration Algorithm With a Correcting Factor for Mobile Wireless Sensor Networks

“…This proposal improves the accuracy of the NOI localization compared to that of the DV-Hop [33] and SMC [38] methods. In [40] the improved Probabilistic Multilateration Algorithm (PMA) achieves better normalized RMSE than the localization algorithms analyzed when the number of the RNs and the proportion of noise vary for different configuration topologies of the RNs in a single-hop and multi-hop network scenario. The improved PMA [40] has better normalized RMSE performance, since it computes the NOI's localization based on a correlation matrix that considers the noisy environment.…”

“…In [40] the improved Probabilistic Multilateration Algorithm (PMA) achieves better normalized RMSE than the localization algorithms analyzed when the number of the RNs and the proportion of noise vary for different configuration topologies of the RNs in a single-hop and multi-hop network scenario. The improved PMA [40] has better normalized RMSE performance, since it computes the NOI's localization based on a correlation matrix that considers the noisy environment. Besides, this method considers a constant parameter called the damping factor, which improves the convergence in the estimation of the NOI's position, providing the solution that minimizes the localization error.…”