Advertisement

Wireless Personal Communications

, Volume 82, Issue 2, pp 723–742 | Cite as

A New Meta-heuristic Algorithm for Maximizing Lifetime of Wireless Sensor Networks

  • Habib Mostafaei
  • Mohammad Shojafar
Article

Abstract

Monitoring a set of targets and extending network lifetime is a critical issue in wireless sensor networks (WSNs). Various coverage scheduling algorithms have been proposed in the literature for monitoring deployed targets in WSNs. These algorithms divide the sensor nodes into cover sets, and each cover set can monitor all targets. It is proven that finding the maximum number of disjointed cover sets is an NP-complete problem. In this paper we present a novel and efficient cover set algorithm based on Imperialist Competitive Algorithm (ICA). The proposed algorithm taking advantage of ICA determines the sensor nodes that must be selected in different cover sets. As the presented algorithm proceeds, the cover sets are generated to monitor all deployed targets. In order to evaluate the performance of the proposed algorithm, several simulations have been conducted and the obtained results show that the proposed approach outperforms similar algorithms in terms of extending the network lifetime. Also, our proposed algorithm has a coverage redundancy that is about 1–2 % close to the optimal value.

Keywords

Imperialist Competitive Algorithm (ICA) Sensor scheduling  Disjoint set cover Wireless sensor networks (WSNs) 

Notes

Acknowledgments

The authors would like to thank Dr. Jamshid Bagherzadeh from Urmia University for his assistance.

References

  1. 1.
    Slijepcevic, S., & Potkonjak, M. (2001). Power efficient organization of wireless sensor networks. In Proceedings of the IEEE international conference on communications (ICC’01). Helsinki, Finland, pp. 472–476.Google Scholar
  2. 2.
    Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35, 619–632.CrossRefGoogle Scholar
  3. 3.
    Mostafaei, H., Meybodi, M. R., & Esnaashari, M. (2010). A learning automata based area coverage algorithm for wireless sensor networks. Journal of Electronic Science and Technology, 8(3), 200–205.Google Scholar
  4. 4.
    Mostafaei, H., Meybodi, M. R., & Esnaashari, M. (2010). EEMLA: Energy efficient monitoring of wireless sensor network with learning automata. In International Conference on Signal Acquisition and Processing, Bangalore, India, pp. 107–111. doi: 10.1109/ICSAP.2010.14.
  5. 5.
    Mostafaei, H., & Meybodi, M. (2014). An energy efficient barrier coverage algorithm for wireless sensor networks. Wireless Personal Communications, 77(3), 2099–2115. doi: 10.1007/s11277-014-1626-1.
  6. 6.
    Gu, Y., Zhao, B., Ji, Y. S., et al. (2011). Theoretical treatment of target coverage in wireless sensor networks. Journal of Computer Science and Technology, 26(1), 117–129.CrossRefzbMATHGoogle Scholar
  7. 7.
    Mostafaei, H. (2015). Stochastic barrier coverage in wireless sensor networks based on distributed learning automata. Computer Communications, 55, 51–61. doi: 10.1016/j.comcom.2014.10.003.
  8. 8.
    Esnaashari, M., & Meybodi, M. R. (2010). A learning automata based scheduling solution to the dynamic point coverage problem in wireless sensor networks. Computer Networks, 54(14), 2410–2438.CrossRefzbMATHGoogle Scholar
  9. 9.
    Cardei, M., & Du, D.-Z. (2005). Improving wireless sensor network lifetime through power aware organization. Wireless Networks, 11, 333–340.CrossRefGoogle Scholar
  10. 10.
    Mostafaei, H., & Meybodi, M. R. (2013). Maximizing lifetime of target coverage in wireless sensor networks using learning automata. Wireless Personal Communications, 71(2), 1461–1477. doi: 10.1007/s11277-012-0885-y.
  11. 11.
    Mostafaei, H., Esnaashari, M., & Meybodi, M. R. (2015). A coverage monitoring algorithm based on learning automata for wireless sensor networks. Applied Mathematics & Information Sciences, 9(3), 1–9. doi: 10.12785/amis/090326.
  12. 12.
    Zorbas, D., Glynos, D., Kotzanikolaou, P., & Douligeris, C. (2010). Solving coverage problems in wireless sensor networks using cover sets. Ad Hoc Networks, 8(4), 400–415. doi: 10.1016/j.adhoc.2009.10.003.CrossRefGoogle Scholar
  13. 13.
    Mohamadi, H., Ismail, A., Salleh, S., & Nodehi, A. (2013). Learning automata-based algorithms for solving the target coverage problem in directional sensor networks. Wireless Personal Communications, 73(3), 1309–1330. doi: 10.1007/s11277-013-1279-5.CrossRefGoogle Scholar
  14. 14.
    Fang, Z., & Wang, J. (2009). Hybrid approximation for minimum-cost target coverage in wireless sensor networks. Optimization Letters, 4, 371–381.CrossRefMathSciNetGoogle Scholar
  15. 15.
    He, J., Ji, S., Pan, Y., & Li, Y. (2011). Reliable and energy efficient target coverage for wireless sensor networks. Tsinghua Science and Technology, 16(5), 464–474.CrossRefGoogle Scholar
  16. 16.
    Gil, J.-M., & Han, Y.-H. (2011). A target coverage scheduling scheme based on genetic algorithms in directional sensor networks. Sensors, 11(2), 1888–1906. doi: 10.3390/s110201888.CrossRefMathSciNetGoogle Scholar
  17. 17.
    Ting, C.-K., & Liao, C.-C. (2010). A memetic algorithm for extending wireless sensor network lifetime. Information Sciences, 180(24), 4818–4833. doi: 10.1016/j.ins.2010.08.021.CrossRefGoogle Scholar
  18. 18.
    Zhao, Q., & Gurusamy, M. (2008). Connected K-target coverage problem in wireless sensor networks with different observation scenarios. Computer Networks, 52, 2205–2220. doi: 10.1016/j.comnet.2008.03.009.CrossRefzbMATHGoogle Scholar
  19. 19.
    Yen, Y. S., Hong, S., Chang, R. S., & Chao, H. C. (2007). An energy efficient and coverage guaranteed wireless sensor network. In IEEE WCNC 2007, pp. 2923–2928.Google Scholar
  20. 20.
    Gupta, H., Zhou, Z., Das, S. R., & Gu, Q. (2006). Connected sensor cover: Self-organization of sensor networks for efficient query execution. IEEE/ACM Transactions on Networking, 14(1), 55–67.CrossRefGoogle Scholar
  21. 21.
    Choi, W., & Das, S. K. (2006). Coverage-adaptive random sensor scheduling for application-aware data gathering in wireless sensor networks. Computer Communications, 29(17), 3467–3482. doi: 10.1016/j.comcom.2006.01.033.CrossRefGoogle Scholar
  22. 22.
    Boukerche, A., Fei, X., & Araujo, R. B. (2007). An optimal coverage-preserving scheme for wireless sensor networks based on local information exchange. Computer Communications, 30(14–15), 2708–2720. doi: 10.1016/j.comcom.2007.05.018.CrossRefGoogle Scholar
  23. 23.
    Tian, D., & Georganas, N. D. (2002). A coverage-preserving node scheduling scheme for large wireless sensor networks. Paper presented at the Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications, Atlanta, Georgia, USA.Google Scholar
  24. 24.
    Nan, G., Shi, G., Mao, Z., & Li, M. (2012). CDSWS: Coverage-guaranteed distributed sleep/wake scheduling for wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2012(1), 1–14. doi: 10.1186/1687-1499-2012-44.CrossRefGoogle Scholar
  25. 25.
    Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist Competitive Algorithm: An algorithm for optimization inspired by imperialistic competition. In IEEE congress on evolutionary computation, pp. 4661–4667.Google Scholar
  26. 26.
    Atashpaz-Gargari, E., & Hashemzadeh, F. (2008). Colonial competitive algorithm, a novel approach for PID controller design in MIMO distillation column process. International Journal of Intelligent Computing and Cybernetics, 1(3), 337–355.CrossRefMathSciNetGoogle Scholar
  27. 27.
    Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In IEEE congress on evolutionary computation, pp. 4661–4667.Google Scholar
  28. 28.
    Pooranian, Z., Shojafar, M., Javadi, B., & Abraham, A. (2014).Using imperialist competition algorithm for independent task scheduling in grid computing. Journal of Intelligent and Fuzzy Systems, 27, 187–199.Google Scholar
  29. 29.
    Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences, Hawaii, USA, pp. 1–10.Google Scholar
  30. 30.
    Sobeih, A., Hou, J. C., Lu-Chuan, K., Li, N., Honghai, Z., Wei-Peng, C., et al. (2006). J-Sim: a simulation and emulation environment for wireless sensor networks. IEEE Wireless Communications, 13(4), 104–119. doi: 10.1109/mwc.2006.1678171.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Computer Engineering, Urmia BranchIslamic Azad UniversityUrmiaIran
  2. 2.Department of Information Engineering, Electronics (DIET)Sapienza University of RomeRomeItaly

Personalised recommendations