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A Practical Target Tracking Technique in Sensor Network Using Clustering Algorithm

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DOI: 10.4236/wsn.2012.411038    3,281 Downloads   5,026 Views   Citations

ABSTRACT

Sensor network basically has many intrinsic limitations such as energy consumption, sensor coverage and connectivity, and sensor processing capability. Tracking a moving target in clusters of sensor network online with less complexity algorithm and computational burden is our ultimate goal. Particle filtering (PF) technique, augmenting handoff and K-means classification of measurement data, is proposed to tackle the tracking mission in a sensor network. The handoff decision, an alternative to multi-hop transmission, is implemented for switching between clusters of sensor nodes through received signal strength indication (RSSI) measurements. The measurements being used in particle filter processing are RSSI and time of arrival (TOA). While non-line-of-sight (NLOS) is the dominant bias in tracking estimation/accuracy, it can be easily resolved simply by incorporating K-means classification method in PF processing without any priori identification of LOS/NLOS. Simulation using clusters of sensor nodes in a sensor network is conducted. The dependency of tracking performance with computational cost versus number of particles used in PF processing is also investigated.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

L. Wang and C. Wu, "A Practical Target Tracking Technique in Sensor Network Using Clustering Algorithm," Wireless Sensor Network, Vol. 4 No. 11, 2012, pp. 264-272. doi: 10.4236/wsn.2012.411038.

References

[1] P. M. Djuric, M. Vemula and M. F. Bugallo, “Tracking with Particle Filtering in Tertiary Wireless Sensor Networks,” IEEE International Conference on Acoustics, Speech and Signal Processing, 18-23 March 2005, pp. 757-760. doi:10.1109/ICASSP.2005.1416119
[2] S. Maheswararajah and S. Halgamuge, “Mobile Sensor Management for Target Tracking,” 2nd International Symposium on Wireless Pervasive Computing, San Juan, 5-7 February 2007, pp. 506-510. doi:10.1109/ISWPC.2007. 342656
[3] J. Wu, H. Xiong, J. Chen and W. Zhou, “A Generalization of Proximity Functions for K-means,” 7th IEEE International Conference on Data Mining, Omaha, 28-31 October 2007, pp. 361-370.doi:10.1109/ICDM.2007.59
[4] J. F. Liao and B. S. Chen, “Robust Mobile Location Estimator with NLOS Mitigation Using Interacting Multiple Model Algorithm,” IEEE Transactions on Wireless Communications, Vol. 5, No. 11, 2006, pp. 3002-3006. doi:10.1109/TWC.2006.04747
[5] S. S. Moghaddam, V. T. Vakilim and A. Falahati, “New Handoff Initiation Algorithm (Optimum Combination of Hysteresis & Threshold Based Methods),” 52nd IEEE VTS-Fall VTC of Vehicular Technology Conference, Vol. 4, 2000, pp. 1567-1574. doi:10.1109/AMS.2009.11
[6] C.-D. Wann, “Kalman Filtering for NLOS Mitigation and Target Tracking in Indoor Wireless Environment,” In: V. Kordic, Ed., Kalman Filter, Intech Publication, 2010, pp. 16-33.
[7] L. Yi, S. G. Razul, Z. Lin and C.-M. See, “Target Tracking in Mixed LOS/NLOS Environments Based on Individual TOA Measurement Detection,” IEEE Sensor Array and Multichannel Signal Processing Workshop, Jerusalem, 4-7 October 2010, pp.153-156. doi:10.1109/SAM.2010.5606720
[8] W. Kei and L. Wu, “Mobile Location with NLOS Identification and Mitigation Based on Modified Kalman Filtering,” Sensors, Vol. 11, No. 2, 2011, pp. 1641-1656. doi:10.3390/s110201641
[9] S. Venkatesh and R. M. Buehrer, “NLOS Mitigation Using Linear Programming in Ultrawideband Location-Aware Networks,” IEEE Transactions on Vehicular Technology, Vol. 56, No. 5, 2007, pp. 3182-3198. doi:10.1109/TVT.2007.900397
[10] J. F. Liao and B. S. Chen, “Adaptive Mobile Location Estimator with NLOS Mitigation Using Fuzzy Inference Scheme,” 8th International Symposium on Communications, Kaohsiung, Taiwan, 2005.
[11] B. S. Gukhool and S. Cherkaoui, “Handoff in IEEE 802.11p-Based Vehicular Networks,” IFIP International Conference on Wireless and Optical Communications Networks, Cairo, 28-30 April 2009, pp. 1-5.
[12] J. F. Liao and B. S. Chen, “Robust Mobile Location Estimator with NLOS Mitigation Using Interacting Multipke Model Algorithm,” IEEE Transactions on Wireless Communications, Vol. 5, No. 11, 2006, pp. 3002-3006. doi:10.1109/TWC.2006.04747
[13] C. C. Tseng, K. H. Chi, M. D. Hsieh and H. H. Chang, “Location-Based Fast Handoff for 802.11 Networks,” IEEE Communications Letters, Vol. 9, No. 4, 2005, pp. 304-306. doi:10.1109/LCOMM.2005.04010
[14] S. Arulampalam, S. Maskell, N. Gordon and T. Clapp, “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking,” IEEE Transactions on Signal Processing, Vol. 50, No. 2, 2002, pp. 174-188. doi:10.1109/78.978374
[15] Z. Yang and X. Wang, “Joint Mobility Tracking and Handoff in Cellular Networks via Sequential Monte Carlo Filtering,” IEEE Transactions on Signal Processing, Vol. 51, No. 1, 2003, pp. 269-281. doi:10.1109/TSP.2002.806580
[16] R. Prakash and V. Veeravalli, “Adaptive Hard Handoff Algorithm,” IEEE Journal on Selected Areas in Communications, Vol. 18, 2000, pp. 2456-2464. doi:10.1109/49. 895049
[17] G. Welch and G. Bishop, “An Introduction to the Kalman Filter,” Technical Report, University of North Carolina, New Caledonia, 2002.

  
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