Comparative study of RSS-based collaborative localization methods in sensor networks

Abstract

Reliable localization is an essential building block of sensor networks. Many techniques have taken advantage of the received signal strength (RSS) measurement for location estimation in wireless sensor networks, since no special hardware implementation is required to measure RSS in almost all kinds of wireless systems. In this paper, two such techniques, MDS method and MLE that are recently proposed for collaborative location estimation, are studied in detail. From the theoretical formulation of the RSS-based location estimation problem, it is seen that MLE is more appropriate than MDS method. However, from simulation studies of both algorithms, which are iterative in nature, it is found that MLE is more sensitive to initial estimate than MDS method. Therefore, in this paper we propose to integrate these two techniques in series so that an estimate is first obtained using MDS method by taking advantage of its better convergence property, then MLE is employed to fine-tune the solution of MDS method to remove modeling errors that are inherent in MDS method. Through extensive simulations it is demonstrated that the new integrated method, named MDS-MLE, consistently outperforms both MDS method and MLE in various simulation scenarios. In this paper, we also address many important issues in collaborative localization, including effects of sensor node density, reference node density, and different deployment strategies of reference nodes.

Localization, tracking, and imaging of targets in wireless sensor networks: An invited review

“…However, it is worth noting that the arrangement of the reference nodes plays a key role and it can positively/negatively affects the performance of the resulting detection accuracy whatever the algorithm at hand. As expected, the localization performances increase when the number of reference nodes grows [Latsoudas and Sidiropoulos, 2007;Li, 2007;Koneru et al, 2006]. Such an issue could represent a limiting factor for potential deployments in real-world applications.…”

“…[24] As for collaborative localization in WSNs, iterative approaches based on the maximum likelihood estimator (MLE) [Li, 2007;Koneru et al, 2006;Patwari et al, 2003] or on the multidimensional scaling (MDS) method [Latsoudas and Sidiropoulos, 2007;Li, 2007;Koneru et al, 2006;Costa et al, 2006;Shang et al, 2004] have shown satisfactory tracking capabilities also in complex scenarios.…”

“…[25] The MDS method performs the detection of the O targets, equipped with active devices as well as the M reference nodes, by minimizing the following function [Koneru et al, 2006]…”

“…Although the formulation in (7) has been shown being more appropriate than MDS [Li, 2007] in fitting the statistical model of the data, the MDS solution turned out to be less sensitive to the initial estimate than the MLE solution [Koneru et al, 2006] with enhanced convergence properties. In order to exploit the positive features of both methods, a hybrid algorithm called MDS-MLE has been also formulated by Li [2007].…”

RSS-Based Localization in Obstructed Environment with Unknown Path Loss Exponent

“…In the case of unknown PLE, both PLE and target location can be regarded as the parameters to be estimated. Most research works consider that the more reference nodes in the network are deployed, the more accurate estimation result gets [10], [11]. However, when there is obstruction between transmitter and receiver, the signal power can drop significantly.…”