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Solving Target Coverage Problem Using Cover Sets in Wireless Sensor Networks Based on Learning Automata

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Abstract

Recent years have witnessed a significant increase in employing wireless sensor networks (WSNs) for a variety of applications. Monitoring a set of discrete targets and, at the same time, extending the network lifetime is a critical issue in WSNs. One method to solve this problem is designing an efficient scheduling algorithm that is able to organize sensor nodes into several cover sets in such a way that each cover set could monitor all the targets. This study presents three learning automata-based scheduling algorithms to solve the problem. Moreover, several pruning rules are devised to avoid the selection of redundant sensors and manage critical sensors for extending the network lifetime. To evaluate the performance of proposed algorithms, we conducted several experiments, and the obtained results indicated that Algorithm 3 was more successful in terms of extending the network lifetime.

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References

  1. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

    Article  Google Scholar 

  2. Ghosh, A., & Das, S. K. (2008). Coverage and connectivity issues in wireless sensor networks: A survey. Pervasive and Mobile Computing, 4(3), 303–334.

    Article  MathSciNet  Google Scholar 

  3. Wang, B. (2011). Coverage problems in sensor networks: A survey. ACM Computing Surveys, 43(4), 1–53.

    Article  MATH  Google Scholar 

  4. Ting, C.-K., & Liao, C.-C. (2010). A memetic algorithm for extending wireless sensor network lifetime. Information Sciences, 180(24), 4818–4833.

    Article  Google Scholar 

  5. 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.

    Google Scholar 

  6. 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.

    Article  Google Scholar 

  7. Slijepcevic, S., & Potkonjak, M. (2001). Power Efficient Organization of Wireless Sensor Networks. In Paper presented at the ICC Helsinki, Finland.

  8. Cardei, M., & Du, D.-Z. (2005). Improving wireless sensor network lifetime through power aware organization. Wireless Networks, 11(3), 333–340.

    Article  Google Scholar 

  9. Lai, C. C., Ting, C. K., & Ko, R. S. (2007). An effective genetic algorithm to improve wireless sensor network lifetime for large-scale surveillance applications. In Evolutionary computation (pp. 3531–3538).

  10. Cardei, M., Thai, M. T., Li, Y., & Wu, W. (2005). Energy-efficient target coverage in wireless sensor networks. In Paper presented at the IEEE INFOCOM 2005.

  11. Cardei, M., Wu, J., Lu, M., & Pervaiz, M. O. (2005). Maximum network lifetime in wireless sensor networks with adjustable sensing ranges. In Proceedings of wireless and mobile computing, networking and communications.

  12. 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.

    Article  Google Scholar 

  13. Wang, J., Niu, C., & Shen, R. (2009). Priority-based target coverage in directional sensor networks using a genetic algorithm. Computers & Mathematics with Applications, 57(11–12), 1915–1922.

    Article  MATH  MathSciNet  Google Scholar 

  14. Zorbas, D., & Douligeris, C. (2011). Connected coverage in WSNs based on critical targets. Computer Networks, 55(6), 1412–1425.

    Article  Google Scholar 

  15. Najim, K., & Poznyak, A. S. (1994). Learning automata: theory and applications. Oxford: Pergamon.

    Google Scholar 

  16. Nicopolitidis, P., Papadimitriou, G. I., Pomportsis, A. S., Sarigiannidis, P., & Obaidat, M. S. (2011). Adaptive wireless networks using learning automata. Wireless Communications, IEEE, 18(2), 75–81.

    Article  Google Scholar 

  17. Torkestani, J. A., & Meybodi, M. R. (2011). Learning automata-based algorithms for solving stochastic minimum spanning tree problem. Applied Soft Computing, 11(6), 4064–4077.

    Article  Google Scholar 

  18. Torkestani, J. A., & Meybodi, M. R. (2011). Weighted Steiner connected dominating set and its application to multicast routing in wireless MANETs. Wireless Personal Communications, 60(2), 145–169.

    Article  Google Scholar 

  19. Torkestani, J. (2013). LAAP: A Learning Automata-based Adaptive Polling Scheme for Clustered Wireless Ad-Hoc Networks. Wireless Personal Communications, 69(2), 841–855.

    Google Scholar 

  20. 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.

    Article  MATH  Google Scholar 

  21. Mohamadi, H., Ismail, A., & Salleh, S. (2013). A learning automata-based algorithm for solving coverage problem in directional sensor networks. Computing, 95(1), 1–24.

    Article  MathSciNet  Google Scholar 

  22. Mohamadi, H., Ismail, A. S., & Salleh, S. (2013). Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks. Sensors and Actuators A: Physical, 198, 21–30. doi:10.1016/j.sna.2013.03.034.

    Article  Google Scholar 

  23. Mohamadi, H., Ismail, A., Salleh, S., & Nodehi, A. (2013, in press). Learning automata-based algorithms for solving the target coverage problem in directional sensor networks. Wireless Personal Communications.

  24. Thathachar, M. A. L., & Harita, B. R. (1987). Learning automata with changing number of actions. IEEE Transactions on Systems, Man and Cybernetics, 17(6), 1095–1100.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Department, Faculty of Computing, Universiti Teknologi Malaysia, 81310 , Johor Bahru, Malaysia

    Hosein Mohamadi & Abdul Samad Ismail

  2. Mathematics Department, Faculty of Science, Universiti Teknologi Malaysia, 81310 , Johor Bahru, Malaysia

    Shaharuddin Salleh

Authors
  1. Hosein Mohamadi
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  2. Abdul Samad Ismail
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  3. Shaharuddin Salleh
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Correspondence to Hosein Mohamadi.

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Mohamadi, H., Ismail, A.S. & Salleh, S. Solving Target Coverage Problem Using Cover Sets in Wireless Sensor Networks Based on Learning Automata. Wireless Pers Commun 75, 447–463 (2014). https://doi.org/10.1007/s11277-013-1371-x

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  • DOI: https://doi.org/10.1007/s11277-013-1371-x

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