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Model-based object tracking in wireless sensor networks

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Abstract

Tracking moving objects is one of the most common requirements in wireless sensor network applications. Most tracking schemes predict a target’s location based on a single object movement model and periodically activate nearby sensors to monitor the target. However, in most real-world situations, a target exhibits multiple movement patterns. Thus, multiple movement models are required to accurately describe the target’s movement. This paper proposes a tracking framework, called model-based object tracking system (MOTS), that allows a sensor network to adaptively apply the most suitable tracking mechanism to monitor the target under various circumstances. To fairly and accurately evaluate all tracking modules, this study further develops a monitoring-cost evaluator to evaluate the monitoring cost of the inactive tracking modules, and then designs three tracking module selection strategies, the Greedy Strategy, Min-Max Strategy, and Weighted Moving Average Strategy, to select the most effective tracking module to monitor the target in each period. A set of experiments is conducted to evaluate MOTS and compare it against existing tracking systems. The obtained results reveal that the cost efficiency of MOTS is considerably better than that of existing tracking systems.

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References

  1. Chen, J., Meng, X., Guo, Y., Grumbach, S., & Sun, H. (2006). Modeling and predicting future trajectories of moving objects in a constrained network. In Proceedings of the 7th international conference on mobile data management (MDM 06), (p. 156).

  2. Yao, Y., Tang, X., & Lim, E.-P. (2009). Localized monitoring of kNN queries in wireless sensor networks. The VLDB Journal, 18(1), 99–117.

    Article  Google Scholar 

  3. Tseng, Y.-C., Kuo, S.-P., Lee, H.-W., & Huang, C.-F. (2003). Location tracking in a wireless sensor network by mobile agents and its data fusion strategies. In Proceedings of the 2nd international conference on information processing in sensor networks (IPSN 03) (pp. 625–641).

  4. Chen, T.-S., Liao, W.-H., Huang, M.-D., & Tsai, H.-W. (2005). Dynamic object tracking in wireless sensor networks. In Proceedings of the 13th ieee international conference on networks (ICON 05) (Vol. 1, pp. 475–480).

  5. Xu, Y., & Lee, W.-C. (2007). Compressing moving object trajectory in wireless sensor networks. International Journal of Distributed Sensor Networks, 3(2), 151–174.

    Google Scholar 

  6. Cerpa, A., Elson, J., Estrin, D., Girod, L., Hamilton, M., & Zhao, J. (2001). Habitat monitoring: Application driver for wireless communications technology. In Proceedings of the 1st ACM SIGCOMM workshop data communications (pp. 20–41).

  7. Goel, S., & Imielinski, T. (2001). Prediction-based monitoring in sensor networks: Taking lessons from mpeg. ACM Computer Communication, 31(5), 82–98.

    Article  Google Scholar 

  8. Peng, W.-C., Ko, Y.-Z., & Lee, W.-C. (2006). On mining moving patterns for object tracking sensor networks. In Proceedings of the 7th international conference on mobile data management (MDM 06) (pp. 41–44).

  9. Xu, Y., & Lee, W.-C. (2003). On localized prediction for power efficient object tracking in sensor networks. In Proceedings of the 23rd international conference on distributed computing systems (pp. 434–439).

  10. Xu, Y., Winter, J., & Lee, W.-C. (2004). Prediction-based strategies for energy saving in object tracking sensor networks. In Proceedings of IEEE international conference on mobile data management (MDM 04) (pp. 346–357).

  11. Zhang, W., & Cao, G. (2004). DCTC: Dynamic convoy tree-based collaboration for target tracking in sensor networks. IEEE Transactions on Wireless Communications, 3(5), 1689–1701.

    Article  Google Scholar 

  12. Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing (WCMC), 2(5), 483–502.

    Article  Google Scholar 

  13. Musolesi, M., Hailes, S., & Mascolo, C. (2004). An ad hoc mobility model founded on social network theory. In MSWiM ’04: Proceedings of the 7th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems (pp. 20–24), New York, NY, USA.

  14. Chen, C.-C., & Chung, Y.-C. (2005). Tracking irregularly moving objects based on alert-enabling sensor model in sensor networks. In Proceedings of the 11th international conference on parallel and distributed systems (ICPADS 05) (Vol. 1, pp. 571– 577).

  15. Ghadaki, H., & Dizaji, R. (2006). Target track classification for airport surveillance radar (ASR). In Proceedings of IEEE conference on radar.

  16. Zaidi, Z. R., & Mark, B. L. (2005). Real-time mobility tracking algorithms for cellular networks based on Kalman filtering. IEEE Transactions on Mobile Computing, 4(2), 195–208.

    Article  Google Scholar 

  17. Yang, L., Feng, C., Rozenblit, J. W., & Qiao, H. (2006). Adaptive tracking in distributed wireless sensor networks. In Proceedings of the 13th Annual IEEE International Symposium and Workshop on Engineering of Computer Based Systems (ECBS 06), pp. 103–111.

  18. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

    Article  Google Scholar 

  19. Zhong, L. C., Shah, R., Guo, C., & Rabaey, J. (2001). An ultra-low power and distributed access protocol for broadband wireless sensor networks. In IEEE Broadband Wireless Summit, Las Vegas, NV.

  20. Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L. S., & Rubenstein, D. (2002). Energy-efficient computing for wildlife tracking: Design tradeoffs and early experiences with zebranet. ACM SIGARCH Computer Architecture News, 30(5), 96–107.

    Article  Google Scholar 

  21. Zou, Y., & Chakrabarty, K. (2003). Target localization based on energy considerations in distributed sensor networks. Ad Hoc Networks, 1(2–3):261–272. (Sensor network protocols and applications).

    Google Scholar 

  22. Xu, J., Tang, X., & Lee, W.-C. (2005). EASE: An energy-efficient in-network storage scheme for object tracking in sensor networks. In In Proceedings of IEEE sensor and ad hoc communications and networks (SECON 05) (pp. 396–405).

  23. Liu, T., Bahl, P., & Chlamtac, I. (1998). Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. IEEE Journal on Selected Areas in Communications, 16(6), 922–936.

    Article  Google Scholar 

  24. Hu, L., & Evans, D. (2004). Localization for mobile sensor networks. In Proceedings of the 10th annual international conference on mobile computing and networking (MobiComm 04) (pp. 45–57).

  25. Raghunathan, V., Schurgers, C., Park, S., & Srivastava, M. B. (2002). Energy-aware wireless microsensor networks. IEEE Signal Processing Magazine, 19(2), 40–50.

    Article  Google Scholar 

  26. Kaiser, W. J., & Pottie, G. J. (2000). Wireless integrated network sensors. Communications of the ACM, 43(5), 51–58.

    Article  Google Scholar 

  27. Ross, S. M. (1987). Introduction to probability and statistics for engineers and scientists. New York: Wiley.

    MATH  Google Scholar 

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Acknowledgments

The authors would like to thank anonymous reviewers for their valuable comments on improving the quality and presentation of the paper. This work was supported by the National Science Council of Taiwan (R.O.C.) under Grants NSC 99-2221-E-218-035 and NSC 98-2221-E-218-036.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Southern Taiwan University, Tainan, Taiwan, R.O.C

    Chao-Chun Chen

  2. Industrial Wireless Division, Moxa Inc., Taipei, Taiwan, R.O.C

    Chien-Han Liao

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  1. Chao-Chun Chen
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  2. Chien-Han Liao
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Correspondence to Chao-Chun Chen.

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Chen, CC., Liao, CH. Model-based object tracking in wireless sensor networks. Wireless Netw 17, 549–565 (2011). https://doi.org/10.1007/s11276-010-0296-5

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