We propose a low-cost and low-power-consumption localization scheme for ZigBee-based wireless sensor networks (WSNs). Our design is based on the link quality indicator (LQI)—a standard feature of the ZigBee protocol—for ranging and the ratiometric vector iteration (RVI)—a light-weight distributed algorithm—modified to work with LQI measurements. To improve performance and quality of this system, we propose three main ideas: a cooperative approach, a coefficient delta ( ) to regulate the speed of convergence of the algorithm, and finally the filtering process with the extended Kalman filter. The results of experiment simulations show acceptable localization performance and illustrate the accuracy of this method. 1. Introduction Many advances in wireless communication technologies and electronic systems miniaturisation have contributed to the implementation of wireless sensor networks (WSNs). These networks have recently received a lot of attention due to a wide range of potential applications especially in the field of localization. In fact, localization is the process of estimating the location of each sensor node in wireless sensor network. Localization can be applied to various domains such as environmental monitoring, human and object tracking, and human interfacing. Thus, the wide variety of techniques of localization is characteristic of many applications and services offered. A specific service matches with a location system, that is to say a set of methods, algorithms, standards, and own strategies. The aim of this work is to provide a first characterization of a ZigBee-based wireless sensor network (WSN) for localization of people and objects in confined spaces. The main aspects for the success of this localization application are low cost, due to the potentially high number of nodes required for covering a huge surface; efficient energy management, in order to provide the system with a reasonable operation time; independence from additional hardware which can raise costs and reduce mobility and robustness of mobile devices. Several approaches based on the communication between sensors nodes have been proposed and developed over the years to achieve localization in WSNs [1–3]. Most of these techniques rely on wireless technologies, for example, WLAN, RFID, ZigBee, and UWB, and several signal metrics have been investigated like basic node to node distance, angle, or numbers of hops [4, 5]. There are many different parameters that have been used as indicator of distance between nodes, for example, angle of arrival (AOA) [6], time of arrival (TOA)
References
[1]
G. Mao, B. Fidan, and B. D. O. Anderson, “Wireless sensor networklocalization techniques,” Computer Networks, vol. 51, no. 10, pp. 2529–2553, 2007.
[2]
X. Wang, O. Bischoff, R. Laur, and S. Paul, “Localization in wireless ad-hoc sensor networks using multilateration with RSSI for logistic applications,” Procedia Chemistry, vol. 1, no. 1, pp. 461–464, 2009.
[3]
P. Bahl and V. N. Padmanabhan, “RADAR: an in-building RF-based user location and t racking system,” in Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'00), vol. 2, pp. 775–784, Tel Aviv, Israel, March 2000.
[4]
G. Mao, B. Fidan, and B. D. O. Anderson, “Wireless sensor network localization techniques,” Computer Networks, vol. 51, no. 10, pp. 2529–2553, 2007.
[5]
P. Stefanut, Application des algorithmes de haute résolution à la localisation de mobiles en milieu confiné [Rapport de thèse], 2010.
[6]
R. Peng and M. L. Sichitiu, “Angle of arrival localization for wireless sensor networks,” in Proceedings of the 3rd Annual IEEE Communications Society on Sensor and Ad hoc Communications and Networks (Secon'06), pp. 374–382, Reston, Va, USA, September 2006.
[7]
I. Güven？ and C. C. Chong, “A survey on TOA based wireless localization and NLOS mitigation techniques,” IEEE Communications Surveys and Tutorials, vol. 11, no. 3, pp. 107–124, 2009.
[8]
R. I. Reza, Data fusion for improved TOA/TDOA position determination in wireless systems [M.S. thesis].
[9]
G. Mao, B. D. O. Anderson, and B. Fidan, “Path loss exponent estimation for wireless sensor network localization,” Computer Networks, vol. 51, no. 10, pp. 2467–2483, 2007.
[10]
A. Roxin, J. Gaber, M. Wack, and A. Nait-Sidi-Moh, “Survey of wireless geolocation techniques,” in IEEE Globecom Workshops, pp. 1–9, 2007.
[11]
J. Lee, K. Cho, S. Lee, T. Kwon, and Y. Choi, “Distributed and energy-efficient target localization and tracking in wireless sensor networks,” Computer Communications, vol. 29, no. 13-14, pp. 2494–2505, 2006.
[12]
C. Gomez, A. Boix, and J. Paradells, “Impact of LQI-based routing metrics on the performance of a one-to-one routing protocol for IEEE 802.15.4 multihop networks,” Eurasip Journal on Wireless Communications and Networking, vol. 2010, Article ID 205407, 20 pages, 2010.
[13]
“Localization and the extended Kalman filter,” CSCI-4190 Introduction to Robotic Algorithms, 2006.
[14]
Jennic 2008, JN5139.
[15]
A. Shareef and Y. Zhu, Localisation Using Extended Kalman Filter in WSN, Electrical and Computer Engineering University of Maine United States of America.
[16]
J. . Hartikainen, A. Solin, and S. S？rkk？, Optimal Filtering with Kalman Filters and Smoothers a Manual for the Matlab Toolbox EKF/UKF, Aalto University School of Science.
[17]
R. C. Johnson, A Brief Summarization of the Kalman Filter.
