Target Localization Using Sensor Location Knowledge in Wireless Sensor Networks

Incorporating error correcting coding techniques into target localization provides an immediate advantage that existing decoding algorithms can be used to determine which area the target is most likely located in. The important knowledge of exact sensor positions is, however, ignored in these decoding algorithms. This letter revisits the problem and shows that based on the weighted average of sensor positions with binary weightings from local decisions, a newly proposed decoding criterion can achieve a much better accuracy in target localization than the soft- and hard-decision rules particularly when a certain number of sensors are under Byzantine attacks.

[1]  Theodore S. Rappaport,et al.  Wireless Communications: Principles and Practice (2nd Edition) by , 2012 .

[2]  Yu Hen Hu,et al.  Energy-Based Collaborative Source Localization Using Acoustic Microsensor Array , 2003, EURASIP J. Adv. Signal Process..

[3]  Biao Chen,et al.  Fusion of censored decisions in wireless sensor networks , 2005, IEEE Transactions on Wireless Communications.

[4]  Jianping An,et al.  Constrained Total Least-Squares Location Algorithm Using Time-Difference-of-Arrival Measurements , 2010, IEEE Transactions on Vehicular Technology.

[5]  Pramod K. Varshney,et al.  Channel aware target localization in wireless sensor networks , 2007, 2007 10th International Conference on Information Fusion.

[6]  Yunghsiang Sam Han,et al.  Local Threshold Design for Target Localization Using Error Correcting Codes in Wireless Sensor Networks in the Presence of Byzantine Attacks , 2017, IEEE Transactions on Information Forensics and Security.

[7]  Pramod K. Varshney,et al.  Target Localization in Wireless Sensor Networks Using Error Correcting Codes , 2013, IEEE Trans. Inf. Theory.

[8]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[9]  Yücel Altunbasak,et al.  Parallel distributed detection for wireless sensor networks: performance analysis and design , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[10]  Zhi Ding,et al.  Wireless source localization based on time of arrival measurement , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Pramod K. Varshney,et al.  Distributed Inference with Byzantine Data: State-of-the-Art Review on Data Falsification Attacks , 2013, IEEE Signal Processing Magazine.

[12]  Yu Hen Hu,et al.  Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks , 2005, IEEE Transactions on Signal Processing.