Optimization and Sequence Search Based Localization in Wireless Sensor Networks

Localization is critical for various applications of Wireless Sensor Networks. This paper presents a 3D localization algorithm for high accuracy localization of a wireless sensor network, which consists of three parts: pre-localization, refinement and sequence search. It is clear that the localization accuracy partly depends on the localization sequence of the unknown nodes, which has not been investigated so far. The proposed novel algorithm aims to address this problem by searching a localization sequence corresponding to a high localization accuracy and a robust algorithm. The simulation results show that the proposed algorithm can get rid of the flip ambiguity and is more robust than several existing algorithms in terms of the localization accuracy

Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page

HEA-Loc: A robust localization algorithm for sensor networks of diversified topologies

In recent years, localization in a variety of Wireless Sensor Networks (WSNs) is a compelling but elusive goal. Several algorithms that use different methodologies have been proposed to achieve this goal. The performances of these algorithms depend on several factors, such as the sensor node placement, anchor deployment or network topology. In this paper, we propose a robust localization algorithm called Hybrid Efficient and Accurate Localization (HEA-Loc). HEA-Loc combines two techniques, Extended Kalman Filter (EKF) and Proximity-Distance Map (PDM) to improve localization accuracy. It is distributed in nature and works well in various scenarios as it is less susceptible to anchors deployment and the network topology. Furthermore, HEA-Loc has strong robustness and it can work well even the measurement errors are large. Simulation results show that HEA-Loc outperforms existing algorithms in both computational complexity and communication overhead. ©2010 IEEE.published_or_final_versionThe IEEE Wireless Communications and Networking Conference (WCNC 2010), Sydney, NSW., 18-21 April 2010. In Proceedings of WCNC, 2010, p. 1-

Distributed Cooperative Localization in Wireless Sensor Networks without NLOS Identification

In this paper, a 2-stage robust distributed algorithm is proposed for cooperative sensor network localization using time of arrival (TOA) data without identification of non-line of sight (NLOS) links. In the first stage, to overcome the effect of outliers, a convex relaxation of the Huber loss function is applied so that by using iterative optimization techniques, good estimates of the true sensor locations can be obtained. In the second stage, the original (non-relaxed) Huber cost function is further optimized to obtain refined location estimates based on those obtained in the first stage. In both stages, a simple gradient descent technique is used to carry out the optimization. Through simulations and real data analysis, it is shown that the proposed convex relaxation generally achieves a lower root mean squared error (RMSE) compared to other convex relaxation techniques in the literature. Also by doing the second stage, the position estimates are improved and we can achieve an RMSE close to that of the other distributed algorithms which know \textit{a priori} which links are in NLOS.Comment: Accepted in WPNC 201