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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Energy-Efficient Adaptive Dynamic Sensor Scheduling for Target Monitoring in Wireless Sensor Networks

  • Zhang, Jian (College of Information Science and Engineering, Northeastern University) ;
  • Wu, Cheng-Dong (College of Information Science and Engineering, Northeastern University) ;
  • Zhang, Yun-Zhou (College of Information Science and Engineering, Northeastern University) ;
  • Ji, Peng (College of Information Science and Engineering, Northeastern University)
  • Received : 2011.01.16
  • Accepted : 2011.06.10
  • Published : 2011.12.31
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Abstract

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption.

Keywords

References

  1. Z. Merhi, M. Elgamel, and M.Bayoumi, "A Lightweight Collaborative Fault Tolerant Target Localization System for Wireless Sensor Networks," IEEE Trans. Mobile Comput., vol. 8, no. 12, Dec. 2009, pp. 1690-1704. https://doi.org/10.1109/TMC.2009.81
  2. Lasse Klingbeil and T. Wark, "A Wireless Sensor Network for Real-time Indoor Localization and Motion Monitoring," Int. Conf. Info. Process. Sensor Netw., 2008, pp. 39-50.
  3. T. Wark et al., "The Design and Evaluation of a Mobile Sensor/Actuator Network for Autonomous Animal Control," IPSN, 2007, pp. 206-215.
  4. F. Akyildiz et al., "Wireless Sensor Networks: A Survey," Comput. Netw., vol. 38, no. 4, Mar. 2002, pp. 393-442. https://doi.org/10.1016/S1389-1286(01)00302-4
  5. S. Gezici et al. "Localization via Ultra-Wideband Radios: A Look at Positioning Aspects for Future Sensor Networks," IEEE Signal Process. Mag., vol. 22, no. 4, 2005, pp. 70-84.
  6. M. Rudafshani and S. Datta, "Localization in Wireless Sensor Networks," Info. Process. Sensor Netw., 2007, pp. 51-60.
  7. M.S. Brandstein, J.E. Adcock, and H.F. Silverman, "A Closed-Form Location Estimator for Use with Room Environment Microphone Arrays," IEEE Trans. Speech Audio Process., vol. 5, no. 1, Jan. 1997, pp. 45-50. https://doi.org/10.1109/89.554268
  8. N. Patwari et al., "Relative Location Estimation in Wireless Sensor Networks," IEEE Trans. Signal Process., vol. 51, no. 8, Aug. 2003. pp. 2137-2148. https://doi.org/10.1109/TSP.2003.814469
  9. T. He et al., "Range-Free Localization Schemes for Large Scale Sensor Networks," Proc. ACM Mobicom, San Diego, CA, 2003, pp. 81-95.
  10. B. Xiao, H. Chen, and S. Zhou, "Distributed Localization Using a Moving Beacon in Wireless Sensor Networks," IEEE Trans. Parallel Distributed Syst., vol. 19, no. 5, May 2008, pp. 587-600. https://doi.org/10.1109/TPDS.2007.70773
  11. X. Sheng and Y. Hu, "Maximum Likelihood Multiple-Source Localization Using Acoustic Energy Measurements with Wireless Sensor Networks," IEEE Trans. Signal Process., vol. 53, no. 1, Jan. 2005, pp. 44-53 . https://doi.org/10.1109/TSP.2004.838930
  12. R. Niu and P.K. Varshney, "Target Location Estimation in Sensor Networks with Quantized Data," IEEE Trans. Signal Process., vol. 54, no. 12, Dec. 2006, pp. 4519-4528. https://doi.org/10.1109/TSP.2006.882082
  13. W. Chen, J.C. Hou, and L. Sha, "Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks," IEEE Trans. Mobile Comput., vol. 3, no. 3, Aug. 2004, pp. 258-271. https://doi.org/10.1109/TMC.2004.22
  14. R. Tharmarasa, T. Kirubarajan, and M.L. Hernandez, "Large-Scale Optimal Sensor Array Management for Multitarget Tracking," IEEE Trans. Syst. Man, Cybern., Part C: Appl. Rev., vol. 37, no. 5, 2007, pp. 803-814.
  15. L. Zuo, R. Niu, and P.K. Varshney, "A Sensor Selection Approach for Target Tracking in Sensor Networks with Quantized Measurements," 16th Int. IEEE Symp. Personal, Indoor, Mobile Radio Commun., 2005, pp. 2521-2524.
  16. J. Jafaryahya et al., "Sensor Selection for Target Tracking in Binary Sensor Networks Using Particle Filter," IEEE Digital Object Identifier, 2010, pp. 1-5.
  17. J. Lin et al., "Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks," IEEE Trans. Instrum. Meas., vol. 58, no. 6, 2009, pp. 1886-1896. https://doi.org/10.1109/TIM.2008.2005822
  18. X.-H. Kuang, R. Feng, and H.-H. Shao, "A Lightweight Target- Tracking Scheme Using Wireless Sensor Network," Meas. Sci. Technol., vol. 19, no. 2, 2008, 025104. https://doi.org/10.1088/0957-0233/19/2/025104
  19. M. Zoghi and M.H. Kahaei, "Adaptive Sensor Selection in Wireless Sensor Networks for Target Tracking," IET Signal Process., vol. 4, no. 5, Oct. 2010, pp. 530-536. https://doi.org/10.1049/iet-spr.2008.0205
  20. B.P. Michael, Random Process in Linear Systems, Englewood Cliffs, NJ: Prentice-Hall, 2002.
  21. J. Miguez et al., "Particle Filtering for Systems with Unknown Noise Probability Distributions," IEEE Workshop Statistical Signal Process., 2003, pp. 522-525.
  22. W.R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-Efficient Communication Protocol for Wireless Microsensor Networks," Proc. 33rd Ann. Hawaii Int. Conf. Syst. Sci., 2000, pp. 3005-3014.

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