Wireless Networks

, Volume 25, Issue 8, pp 5137–5150 | Cite as

ANT-colony based disjoint set assortment in wireless sensor networks

  • Muhammad Yasir Shabir
  • Ata UllahEmail author
  • Zahid Mahmood


Wireless sensor network (WSN) consists of small sized devices containing different sensors to monitor physical, environmental and medical conditions during surveillance of fields, parking, borders and any targeted areas. Mostly WSN is deployed in harsh environments where battery can’t be changed or recharged easily, therefore, battery power should be used efficiently. Sensor nodes are randomly deployed in remote areas by using aero plane and as a result more than one sensor may be covering the same area. The main problem is that if these sensors become functional at the same time it results in the wastage of battery resources and reducing the network lifetime. This paper resolve this issue by identifying disjoint subsets of the sensors such that alternate nodes cover the whole target area at different ON–OFF intervals of time. We have proposed to adopt ant-colony optimization to find the disjoint subsets of deployed sensor nodes. We have explored the algorithms for sensor deployment, cover set initialization, field identification and allocation. Finally, the optimal disjoint set allocation mechanism is explored. We have simulated our work using NS 2.35 and results ensure the dominance of our scheme over preliminaries in terms of number of field identification, disjoint set allocation, processing time and energy consumption.


Ant-colony optimization (ACO) Disjoint sets Sensor deployment WSN 



  1. 1.
    Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Journal of Computer Networks, 52(12), 2292–2330.CrossRefGoogle Scholar
  2. 2.
    Kim, B.-S., Park, H., Kim, K. H., Godfrey, D., Kim, K.-I. (2017). A survey on real-time communications in wireless sensor networks. Wireless communications and mobile computing, 2017, Article ID 1864847, p. 14.Google Scholar
  3. 3.
    Shwe, H. Y., Jiang, X.-H., & Horiguchi, S. (2009). Energy saving in wireless sensor networks. International Journal of Distributed Sensor Networks, 6(5), 20–27.Google Scholar
  4. 4.
    Sangwan, A., & Singh, R. P. (2015). Survey on coverage problems in wireless sensor networks. Wireless Personal Communications, 80(4), 1475–1500.CrossRefGoogle Scholar
  5. 5.
    Younis, M., & Akkaya, K. (2008). Strategies and techniques for node placement in wireless sensor networks: A survey. Elsevier Science Ad Hoc Networks, 6(4), 621–655.CrossRefGoogle Scholar
  6. 6.
    Wang, L., & Xiao, Y. (2006). A survey of energy-efficient scheduling mechanisms in sensor networks. Mobile Networks and Applications, 11(5), 723–740.CrossRefGoogle Scholar
  7. 7.
    Liu, C., Wu, K., Xiao, Y., & Sun, B. (2006). Random coverage with guaranteed connectivity: Joint scheduling for wireless sensor networks. IEEE Transactions on Parallel Distributed System, 17(6), 562–575.CrossRefGoogle Scholar
  8. 8.
    Zhu, Chuan, Zheng, Chunlin, Shu, Lei, & Han, Guangjie. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35, 619–635.CrossRefGoogle Scholar
  9. 9.
    Yu, J., Chen, Y., Ma, L., Huang, B., & Cheng, X. (2016). On connected target k-coverage in heterogeneous wireless sensor networks. Sensors, 16(1), 104.CrossRefGoogle Scholar
  10. 10.
    Othman, F., Bouabdallah, N., & Boutaba, R. (2009). Energy conservation in wireless sensor networks: A survey. IEEE Communication Magazine, 40(8), 1–6.Google Scholar
  11. 11.
    Ye, F., Zhong, G., Lu, S., & Zhang, L. (2003). PEAS: A robust energy conserving protocol for long-lived sensor networks. In Proceedings 23rd conference distributed computing systems, pp. 35–41.Google Scholar
  12. 12.
    Wu, S., Chou, W., Niu, J., & Guizani, M. (2018). Delay-aware energy-efficient routing towards a path-fixed mobile sink in industrial wireless sensor networks. Sensors, 2018, 1–18.Google Scholar
  13. 13.
    Kumar, S., Lai, T. H., Posner, M. E., & Sinha, P. (2010). Maximizing the lifetime of a barrier of wireless sensors. IEEE Transaction Mobile Computer (TMC), 9(8), 1161–1172.CrossRefGoogle Scholar
  14. 14.
    Ye, F., Zhong, G., Lu, S., Zhang, L. (2002) Energy efficient robust sensing coverage in large sensor networks. In International conference on security and privacy in communication networks, pp. 272–287.Google Scholar
  15. 15.
    Berman, P., Calinescu, G., Shah, C. (2004). Power efficient monitoring management in sensor networks. In Communications society IEEE wireless communications and networking conference, vol. 6, pp. 787–832.Google Scholar
  16. 16.
    Cardei, M., Thai, M. T., Li, Y. (2005). Energy-efficient target coverage in wireless sensor networks. In IEEE international conference on computer communications, pp. 1380–1387.Google Scholar
  17. 17.
    Slijepcevic, S., & Potkonjak, M. (2001). Power efficient organization of wireless sensor networks. In IEEE international conference on computer communications, pp. 519–528.Google Scholar
  18. 18.
    Zorbas, D., Glynos, D., & Kotzanikolaou, P. (2010). Solving coverage problems in wireless sensor networks using cover sets. Ad Hoc Networks, 29(8), 400–415.CrossRefGoogle Scholar
  19. 19.
    Cardei, M., & Wu, J. (2006). Energy-efficient coverage problems in wireless ad hoc sensor networks. Journal Computer Communications, 29(4), 413–420.CrossRefGoogle Scholar
  20. 20.
    Schaefer, R. (2007). Foundations of global genetic optimization, vol. 74. Springer, Berlin, pp. 1–6.zbMATHCrossRefGoogle Scholar
  21. 21.
    Price, K. V., Storn, R. M., & Lampienn, J. A. (2005). Differential evolution: A practical approach to global optimization (Vol. 1, pp. 83–94). Berlin: Springer.Google Scholar
  22. 22.
    Passino, K. M. (2002). Bio mimicry of bacterial foraging for distributed optimization and control. IEEE Control System Magazine, 22(3), 52–67.MathSciNetCrossRefGoogle Scholar
  23. 23.
    Diaz, S., Mendez, D. (2015). DACA-disjoint path and clustering algorithm for self-healing WSN. In Proceeding IEEE colombian conference on communications and computing (COLCOM), pp. 1–5.Google Scholar
  24. 24.
    Tian, W., Liu, J. (2014). A novel optimization method for the maximum coverage sets of WSN. In International conference on wireless networks and information systems, pp. 125–128.Google Scholar
  25. 25.
    Lin, Y., Zhang, J., Chung, H. S., Ip, W. H., Li, Y., & Shi, Y. (2012). An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, 42(3), 408–420.CrossRefGoogle Scholar
  26. 26.
    Lee, J.-J., & Lee, J.-W. (2012). Ant-colony-based scheduling algorithm for energy-efficient coverage of WSN. IEEE Sensors Journal, 12(10), 3036–3046.CrossRefGoogle Scholar
  27. 27.
    Chaturvedi, P., & Daniel, A. K. (2017). A hybrid scheduling protocol for target coverage based on trust evaluation for wireless sensor networks. IAENG International Journal of Computer Science, 44(01), 87–104.Google Scholar
  28. 28.
    Lee, J.-W., Choi, B., & Lee, J.-J. (2011). Energy-efficient coverage of wireless sensor networks using ant colony optimization with three types of pheromones. IEEE Transactions on Industrial Informatics, 7(3), 419–427.CrossRefGoogle Scholar
  29. 29.
    Chaturvedi, P., & Daniel, A. K. (2017). A novel sleep/wake protocol for target coverage based on trust evaluation for a clustered wireless sensor network. International Journal of Mobile Network Design and Innovation, 7(3/4), 199.CrossRefGoogle Scholar
  30. 30.
    Zorbas, D., Glynos, D., Kotzanikolaou, P., & Douligeris, C. (2009). Solving coverage problems in wireless sensor networks using cover sets. Ad Hoc Networks, 8(4), 400–415.CrossRefGoogle Scholar
  31. 31.
    Hu, X., Zhang, J., & Yu, Y. (2010). Hybrid genetic algorithm using a forward encoding scheme for lifetime maximization of wireless sensor networks. IEEE Transactions on Evolutionary Computation, 14(5), 14–15.CrossRefGoogle Scholar
  32. 32.
    Turgut, D., Das, S. K., Elmasri, R., Turgut, B. (2002). Optimizing clustering algorithm in mobile ad hoc networks using genetic algorithmic approach. In IEEE global telecom conference, vol. 01, no. 09, pp. 62–66.Google Scholar
  33. 33.
    Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical Computer Science, 344(2), 243–278.MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Putha, R., Quadrifoglio, L., & Zechman, E. (2012). Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Computer-Aided Civil and Infrastructure Engineering, 27(01), 14–28.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of CS & ITUniversity of Kotli, Azad Jammu and KashmirKotliPakistan
  2. 2.Department of Computer ScienceNational University of Modern LanguagesIslamabadPakistan
  3. 3.School of Information and EngineeringUniversity of Science and TechnologyBeijingChina

Personalised recommendations