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.
Similar content being viewed by others
References
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.
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.
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.
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.
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.
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.
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.
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.
Cardei, M., & Du, D.-Z. (2005). Improving wireless sensor network lifetime through power aware organization. Wireless Networks, 11, 333–340.
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.
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.
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.
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.
Fang, Z., & Wang, J. (2009). Hybrid approximation for minimum-cost target coverage in wireless sensor networks. Optimization Letters, 4, 371–381.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Acknowledgments
The authors would like to thank Dr. Jamshid Bagherzadeh from Urmia University for his assistance.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mostafaei, H., Shojafar, M. A New Meta-heuristic Algorithm for Maximizing Lifetime of Wireless Sensor Networks. Wireless Pers Commun 82, 723–742 (2015). https://doi.org/10.1007/s11277-014-2249-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-014-2249-2