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Tracking the events in the coverage of wireless sensor networks based on artificial neural-networks algorithms

  • Research Article
  • Published:
Frontiers of Electrical and Electronic Engineering in China

Abstract

Sensor deployment is an important problem in mobile wireless sensor networks. This paper presents a distributed self-spreading deployment algorithm (SOMDA) for mobile sensors based on artificial neural-networks self-organizing maps algorithm. During the deployment, the nodes compete to track the event and cooperate to form an ordered topology. After going through the algorithm, the statistical distribution of the nodes approaches that of the events in the interest area. The performance of the algorithm is evaluated by the covered percentage of region/events, the detecting ability and the energy equalization of the networks. The simulation results indicate that SOMDA outperforms uniform and random deployment with lossless coverage, enhancive detecting ability and significant energy equalization.

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References

  1. Meguerdichian S., Koushanfar F., Potkonjak M. et al., Coverage problems in wireless Ad-hoc sensor networks, IEEE INFOCOM, 2001, 3: 1380–1387

    Google Scholar 

  2. Dhillon S. S., Chakrabarty K., Sensor placement for effective coverage and surveillance in distributed sensor networks, IEEE WCNC 2003, 2003, 3: 1609–1614

    Google Scholar 

  3. Lin Zhi-ting, Qu Yu-gui, Zhai Yu-jia, 3-dimensional sensor model in the sensor network, Chinese Journal of Electronics, APR 2006, 15(2): 324–328 (in Chinese)

    Google Scholar 

  4. Lin Zhi-ting, Qu Yu-gui, Zhai Yu-jia, Placement algorithm for the wireless sensor network, Chinese Journal of Electronics, 2006, 15(1): 179–182 (in Chinese)

    Google Scholar 

  5. Heo N., Varshney P. K., A distributed self spreading algorithm for mobile wireless sensor networks, IEEE WCNC, 2003, 3: 1597–1602

    Article  Google Scholar 

  6. Quintao F. P., Nakamura F. G., Mateus G. R., Evolutionary algorithm for the dynamic coverage problem applied to wireless sensor networks design, the 2005 IEEE Congress on Evolutionary Computation, 2005, 2: 1589–1596

    Google Scholar 

  7. Kohonen T., The self-organizing map, Proceedings of the Institute of Electrical and Electronics Engineers, 1990, 78: 1464–1480

    Google Scholar 

  8. Elaine C., Kristof V. L., Martin S., Self-organization in Adhoc sensor networks: an empirical study, Proceedings of the Eighth International Conference on Artificial Life, 2002: 260–263

  9. Kulakov A., Davcev D., Distributed data processing in wireless sensor networks based on artificial neural-networks algorithms, Proceedings of 10th ISCC, 2005, 353–358

  10. Kulakov A., Davcev D., Trajkovski G., Application of wavelet neural-networks in wireless sensor networks, SNPD/SAWN 2005: 262–267

  11. Kulakov A., Davcev D., Tracking of unusual events in wireless sensor networks based on artificial neural-networks algorithms, ITCC 2005, 2005, 2: 534–539

    Google Scholar 

  12. Simon H., Neural networks: a comprehensive foundation (second edition), Prentice-Hall, Inc, New Jersey: 1999

    MATH  Google Scholar 

  13. Kaplan E. D., Understanding GPS: principles and applications, Artech House, Inc, America: 1996

    Google Scholar 

  14. Wang X., Xing G., Zhang Y. et al., Integrated coverage and connectivity configuration in wireless sensor networks, SenSys’ 03: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, ACM Press (2003): 28–39

  15. George F. L., Artificial intelligence: structures and strategies for complex problem solving (fifth edition), Pearson Education, Inc, Iowa: 2005

    Google Scholar 

  16. Oliver I. M., Smith D. J., Holland J. R. C., A Study of permutation crossover operators on the traveling salesman problem, Proceedings of the Second International Conference on Genetic Algorithms, Hillsdale, NJ: Erlbaum & Assoc, 1987: 224–230

    Google Scholar 

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Correspondence to Zhao Bao-hua.

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Bai, Rg., Qu, Yg., Lin, Zt. et al. Tracking the events in the coverage of wireless sensor networks based on artificial neural-networks algorithms. Front. Electr. Electron. Eng. China 1, 445–450 (2006). https://doi.org/10.1007/s11460-006-0085-1

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  • DOI: https://doi.org/10.1007/s11460-006-0085-1

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