Performances of Localization Algorithms in a Prototype WSN System

Abstract:

The proliferation of wireless sensor networks (WSNs) has fostered the demand of context aware applications, among which localization plays a significant role. Localization in WSN is facing the challenges of (1) error and noise; (2) dynamic environments; (3) data packet loss. However, most presented localization algorithms are verified just by simulation but practical systems. To validate the localization algorithms realistically, a prototype WSN system equipped with a group of sensor nodes is deployed in this paper. A trilateration based optimization approaches and the Extended Kalman Filter (EKF) with position-velocity (PV) model are proposed and tested in the system comparing with the traditional trilateration. The experiment result indicates that the two proposed localization algorithms have much better accuracy than the traditional trilateration and, in a certain aspect, EKF with PV model is the most suitable algorithm among the three algorithms for localization in the prototype system.

• Periodical: Advanced Materials Research (Volumes 457-458)
• Pages: 723-727
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.457-458.723
• Online since: January 2012
• Authors: Jing Zhi Ye, Ling Zhao, Wen Feng Luo
• Keywords: Extended Kalman Flter (EKF), Localization Algorithm, Prototype System, Trilateration, Wireless Sensor Network (WSN)

References

[1] Y. C. Yu and Y. L. Yi, A new received signal strength based location estimation scheme for wireless sensor network, Consumer Electronics, vol. 55, Oct 2009, pp.1295-1299, doi: 10. 1109/ TCE. 2009. 5277991.

DOI: 10.1109/tce.2009.5277991

[2] L. Mailaender, Comparing Geo-Location Bounds for TOA, TDOA, and Round-Trip Toa, Proc. IEEE Symp. Personal, Indoor and Mobile Radio Communications (PIMRC 07), IEEE Press, Sept. 2007, pp.1-5, doi: 10. 1109/PIMRC. 2007. 4393993.

DOI: 10.1109/pimrc.2007.4393993

[3] L. B. Xie, H. W. Feng, Y. Wang and Z. C. Ji, Analysis and optimal control of networked control systems with data packet loss, Proc. IEEE Symp. Intelligent Control and Automation (WCICA 08), IEEE Press, June 2008, pp.7204-7207.

DOI: 10.1109/wcica.2008.4594037

[4] W. F. Luo and Z. H. Luo, The Design and Implementation of a Target Tracking Test Ded Based on WSN, Proc. IEEE Symp. Computer, Mechatronics, Control and Electronic Engineering (CMCE 2010), IEEE Press, Oct. 2010, pp.312-315.

DOI: 10.1109/cmce.2010.5610499

[5] Z. Chaczk, R. Klempous, J. Nikodem and M. Nikodem, Methods of sensors localization in wireless sensor networks, Proc. IEEE Symp. Engineering of Computer-Based Systems (ECBS 07), IEEE Press, March 2007, pp.145-152, doi: 10. 1109/ ECBS. 2007. 48.

DOI: 10.1109/ecbs.2007.48

[6] D. Dardari and A. Conti, A sub-optimal hierarchical maximum likelihood algorithm for collaborative localization in ad-hoc network,. Proc. IEEE Symp. Sensor and Ad Hoc Communications and Networks (SAHCN 04), IEEE Press, Oct. 2004, pp.145-152.

DOI: 10.1109/sahcn.2004.1381944

[7] C. C. Pi, A non-line-of-sight error mitigation algorithm in location estimation, Proc. IEEE Symp. Wireless Communications and Networking Conference (WCNC 99), IEEE Press, Sep. 1999, Vol. 1, p.316–320, doi: 10. 1109/WCNC. 1999. 797838.

DOI: 10.1109/wcnc.1999.797838

[8] J. Wang, Q. H. Liang, K. Liang and S. G. Wei, A new extended Kalman filter based carrier tracking loop, Proc. IEEE Symp. Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2009, IEEE Press, Oct. 2009, pp.1181-1184.

DOI: 10.1109/mape.2009.5355852

[9] Y. Liu and Z. D. Sun, EKF-Based Adaptive Sensor Scheduling for Target Tracking, Proc. IEEE Symp. Information Science and Engineering (ISISE 2008), IEEE Press, Dec. 2008, pp.171-174, doi: 10. 1109/ISISE. 2008. 286.

DOI: 10.1109/isise.2008.286