Sensor Scheduling Algorithm Target Tracking-Oriented

DOI: 10.4236/wsn.2011.38030   PDF   HTML     4,962 Downloads   9,130 Views   Citations


Target tracking is a challenging problem for wireless sensor networks because sensor nodes carry limited power recourses. Thus, scheduling of sensor nodes must focus on power conservation. It is possible to extend the lifetime of a network by dynamic clustering and duty cycling. Sensor Scheduling Algorithm Target Tracking-oriented is proposed in this paper. When the target occurs in the sensing filed, cluster and duty cycling algorithm is executed to scheduling sensor node to perform taking task. With the target moving, only one cluster is active, the other is in sleep state, which is efficient for conserving sensor nodes’ limited power. Using dynamic cluster and duty cycling technology can allocate efficiently sensor nodes’ limited energy and perform tasks coordinately.

Share and Cite:

D. Yan and J. Wang, "Sensor Scheduling Algorithm Target Tracking-Oriented," Wireless Sensor Network, Vol. 3 No. 8, 2011, pp. 295-299. doi: 10.4236/wsn.2011.38030.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. Kahn, R. H. Katz and K. Pester, “Next Century Challenges: Mobile Networking for Smart Dust,” ACM MOBICOM Conference, 1999.
[2] Feng Zhao, Jie Liu, Juan Liu, Leonidasl Guibas, James Reich, “Collaborative Signal and Information Processing: An Information-Directed Approach,” Proceedings of the IEEE, 2003. doi:10.1109/JPROC.2003.814921
[3] Raghunathan, V.; Schurgers, C.; Sung Park; Srivastava, M.B.; “Energy-aware wireless microsensor networks,” Signal Processing Magazine, IEEE Vol. 19, Issue: 2 2002 , pp: 40-50. doi:10.1109/79.985679
[4] Blough D M, Santi P. “Investigating upper bounds on network lifetime extension for cell-based energy conservation techniques in stationary ad hoc network,” Proceeding of 8th Annual Int’l Conf on Mobile Computing and Networking. Atlanta, GA, USA: ACM Press, 2002, pp. 183-192.
[5] Xu Y, Heidemann J, EstrinD. “Geography-informed energy conservation for ad hoc routing,” Proceedings ACM Mobicom, 2001, pp.70-84.
[6] Jianyong Lin; Wendong Xiao; Lewis, F.L.; Lihua Xie; “Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks,” IEEE Transactions on Instrumentation and Measurement, Volume: 58, Issue: 6 2009, pp: 1886-1896. doi:10.1109/TIM.2008.2005822
[7] D. Estrin, “Wireless sensor networks tutorial part IV: sensor network protocols,” In Proc. Mobicom, USA, pp:23–28, 2002.
[8] Ratnasingham Tharmarasa, Thiagalingam Kirubarajan, Marcel L. Hernandez, “Large-Scale Optimal Sensor Array Management for Multitarget Tracking,” IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol. 37, NO. 5, 2007 pp: 803-814. doi:10.1109/TSMCC.2007.901003
[9] Cardei M, Thai MT, Li Y S, et al. “Energy-efficient target coverage in wireless sensor networks,” Proceedings of IEEE INFOCOM, 2005, pp:1976-1984. doi: 10.1109/INFCOM.2005.1498475
[10] I.F. Akyildiz, W. Su*, Y. Sankarasubramaniam, E. Cayirci, “Wireless sensor networks: a survey,” Computer Networks, 2002, 38(4), pp. 393-422.
[11] Giuseppe Anastasi, Marco Conti, Mario Di Francesco, Andrea Passarella, “Energy conservation in wireless sensor networks: A survey ,” Ad Hoc Networks, 2009.
[12] Wang Z, Zhang J., “Energy Efficiency of Two Virtual Infrastructures for MNAETs,” IPCCCC 2006, pp. 547-552. doi 10.1109/PCCC.2005.1460632
[13] Dongmei Yan, JinKuan Wang, Li Liu, Bin Wang, Peng Xu, “Topology Control Algorithm Target Tracking-ori- ented,” The 5th International Conference on Wireless Communications, Networking and Mobile Computing, 2009, Vol. 4. doi 10.1109/WICOM.2009.5304199
[14] Karayiannis, N.B., “MECA: maximum entropy clustering algorithm,” Proceedings of the Third IEEE Conference on Fuzzy Systems, IEEE World Congress on Computational Intelligence, Vol. 1, 1994, pp. 630-635 doi 10.1109/FUZZY.1994.343658
[15] Xue Wang, Sheng Wang, Aiguo Jiang., “A Novel Framework for Clusterbased Sensor Fusion,” IMACS Multiconference on Computational Engineering in Systems Applications, Vol. 2, 2006, pp. 2033-2038. doi 10.1109/CESA.2006.313648
[16] Greg Welch,Gary Bishop, “An Introduction to the Kalman Filter,” TR 95-041, Department of Computer Science University of North Carolina at Chapel Hill.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.