A Multi-objective Disjoint Set Covers for Reliable Lifetime Maximization of Wireless Sensor Networks

Wireless Personal Communications

Volumn 81, Issue 2, 2015, Pages 819-838

  Attea, Bara’a A ^a   Khalil, Enan A ^b   Özdemir, Suat ^b   Yıldız, Oktay ^b  

^a UNIVERSITY OF BAGHDAD   (Iraq)

^b GAZI UNIVERSITY   (Turkey)

Author keywords

DSC; MOO; Non dominated solution; NP hard; Probabilistic coverage; Reliability; WSNs

Indexed keywords

EVOLUTIONARY ALGORITHMS; NP-HARD; PARETO PRINCIPLE; RELIABILITY;

ENVIRONMENTAL CONDITIONS; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI-OBJECTIVE PROBLEM; NONDOMINATED SOLUTIONS; PROBABILISTIC COVERAGE; SURVEILLANCE APPLICATIONS; WIRELESS SENSOR NETWORK (WSNS); WSNS;

WIRELESS SENSOR NETWORKS;

EID: 84925514060     PISSN: 09296212     EISSN: 1572834X     Source Type: Journal    
DOI: 10.1007/s11277-014-2159-3     Document Type: Article

References (36)

1. Garey, M. R., & Johnson, D. S. (1979). Computers and intractability. A guide to the theory of NP-completeness. New York: Freeman.
2. Slijepcevic, S., & Potkonjak, M. (2001). Power efficient organization of wireless sensor networks. In Proceedings of IEEE international conference communications, Vol. 2. Finland, pp. 472–476.
3. Cardei, M., Thai, M., Li, Y., & Wu, W. (2005). Energy-efficient target coverage in wireless sensor networks. In IEEE INFOCOM 2005, Mar. 2005, pp. 1976–1984.
4. Tian, D., & Georganas, N. D. (2002). A coverage-preserving node scheduling scheme for large wireless sensor networks. In Proceedings of 1st association computing machinery international workshop wireless sensor networks and applications, pp. 32–41.
5. Wang, X., Xing, G., Zhang, Y., Lu, C., Pless, R., & Gill, C. (2003). Integrated coverage and connectivity configuration in wireless sensor networks. In Proceedings of 1st international conference on embedded network sensor systems, Los Angeles, CA, pp. 28–39.
6. Liu, Y., & Liang, W. (2005). Approximate coverage in wireless sensor networks. In Proceedings of IEEE conference on local computing network, 30th anniversary, pp. 68–75.
7. Gupta, H., Zhou, Z., Das, S. R., & Gu, Q. (2006). Connected sensor cover: Self-organization of sensor networks for efficient query execution. IEEE, Association for Computing Machinery Transactions on Networking, 14(1), 55–67.
8. Ai, J., & Abouzeid, A. A. (2006). Coverage by directional sensors in randomly deployed wireless sensor networks. Journal of Combinatorial Optimization, 11(1), 21–41.
9. Funke, S., Kesselman, A., Kuhn, F., Lotker, Z., & Segal, M. (2007). Improved approximation algorithms for connected sensor cover. Wireless Networks, 13(2), 153–164.
10. Zhou, Z., Das, S. R., & Gupta, H. (2009). Variable radii connected sensor cover in sensor networks. Association Computing Machinery Transactions on Sensor Networks, 5(1, article 8), 1–36.
11. Martins, F. V. C., Carrano, E. G., Wanner, E. F., Takahashi, R. H. C., & Mateus, G. R. (2009). A dynamic multiobjective hybrid approach for designing wireless sensor networks. In Proceedings on IEEE congress on evolutionary computation, pp. 1145–1152.
12. Cardei, M., & Du, D.-Z. (2005). Improving wireless sensor network lifetime through power aware organization. Wireless Netwoks, 11(3), 333–340.
13. Cardei, I., & Cardei, M. (2008). Energy-efficient connected-coverage in wireless sensor networks. International Jounal on Sensor Networks, 3(3), 201–210.
14. He, J., Xiong, N., Xiao, Y., & Pan, Y. (2010). A reliable energy efficient algorithm for target coverage in wireless sensor networks. In IEEE 30th international conference on distributed computing systems workshops (ICDCSW), 2010, pp. 180–188.
15. Lai, C., Ting, C., & Ko, R. (2007). An effective genetic algorithm to improve wireless sensor network lifetime for large-scale surveillance applications. In IEEE congress on evolutionary computation, 2007. CEC 2007. IEEE, 2007.
16. Hu, X. M., Zhang, J., Yu, Y., Chung, H. H., Li, Y. L., Shi, Y. H., et al. (2010). Hybrid genetic algorithm using a forward encoding scheme for lifetime maximization of wireless sensor networks. IEEE Transactions on Evolutionary Computation, 14(5), 766–781.
17. Gil, J. M., & Han, Y. H. (2011). A target coverage scheduling scheme based on genetic algorithms in directional sensor networks. Sensors 11, 11(2), 1888–1906.
18. Huang, C.-F., & Tseng, Y.-C. (2005). The coverage problem in a wireless sensor network. Mobile Networks and Applications, 10(4), 519–528.
19. Elfes, A. (1987). Sonar-based real-world mapping and navigation. IEEE Journal of Robotics and Automation, RA–3(3), 249–265.
20. Ghosh, A., & Das, S. K. (2006). Coverage and connectivity issues in wireless sensor networks. In Mobile, wireless, and sensor networks: Technology, applications, and future directions, pp. 221–256.
21. Hossain, A., Biswas, P. K., & Chakrabarti, S. (2008). Sensing models and its impact on network coverage in wireless sensor network. In IEEE region 10 and the third international conference on Industrial and information systems, 2008. ICIIS 2008, (pp. 1–5). IEEE.
22. Rajagopalan, R. (2010). A multi-objective optimization approach for data fusion in mobile agent based distributed sensor networks. In Instrumentation and measurement technology Conference (I2MTC), 2010 IEEE, pp. 208–212.
23. Rajagopalan, R., Mohan, C. K., Varshney, P., & Mehrotra, K. (2005). Multi-objective mobile agent routing in wireless sensor networks. In The 2005 IEEE congress on evolutionary computation, 2005, Vol. 2, pp. 1730–1737.
24. Özdemir, S., Attea, B. A., & Khalil, Ö. A. (2012). Multi-objective clustered-based routing with coverage control in wireless sensor networks. Soft computing, (pp. 1–12). Berlin: Springer. doi: 10.1007/s00500-012-0970-x.
25. Özdemir, S., Attea, B. A., & Khalil, Ö. A. (2012). Multi-objective evolutionary algorithm based on decomposition for energy eefficient coverage in wireless sensor networks. Wireless personal communications. Berlin: Springer. doi:10.1007/s11277-012-0811-3.
26. Konstantinidis, A. (2009). Multiobjective deployment and power assignment in wireless sensor networks using metaheuristics. University of Essex.
27. Jia, J., Chen, J., Chang, G., Wen, Y., & Song, J. (2009). Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius. Computers and Mathematics with Applications, 57(11), 1767–1775.
28. Konstantinidis, A., Yang, K., Zhang, Q., & Zeinalipour-Yazti, D. (2009). A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks. Computer Networks, 54(6), 960–976.
29. Konstantinidis, A., & Yang, K. (2011). Multi-objective energy-efficient dense deployment in wireless sensor networks using a hybrid problem-specific MOEA/D. Applied Soft Computing, 11(6), 4117–4134.
30. Abdulhalim, M. F., & Attea, B. A. (2014). Multi-layer genetic algorithm for maximum disjoint reliable set covers problem in wireless sensor networks. Accepted in Wireless Personal Communications. Berlin: Springer.
31. Coello Coello, C. A., Van Veldhuizen, D. A., & Lamont, G. B. (2002). Evolutionary algorithms for solving multi-objective problems. New York: Kluwer.
32. Srinivas, N., & Deb, K. (1994). Multi-objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation, 2(3), 221–248.
33. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2000). A fast and elitist multi-objective genetic algorithm: NSGA-II. In Proceedings parallel problem solving from nature VI, pp. 849–858.
34. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Chichester: Wiley.
35. Zhang, Q., & Li, H. (2007). MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731.
36. Zitzler, E. (1999). Evolutionary algorithms for multi-objective optimization: Methods and applications. Doctoral dissertation, Zurich: Swiss Federal Institute of Technology.