Evolutionary-Based Coverage Control Mechanism for Clustered Wireless Sensor Networks

  • Riham ElhabyanEmail author
  • Wei Shi
  • Marc St-Hilaire
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10866)


Many clustering protocols have been proposed for Wireless Sensor Networks (WSNs). However, most of these protocols focus on selecting the optimal set of Cluster Heads (CHs) in order to reduce or balance the network’s energy consumption and unfortunately, how to effectively cover the network area is often overlooked. Coverage optimization in WSNs is a well-known Non-deterministic Polynomial (NP)-hard optimization problem. In this paper, we propose a Genetic Algorithm (GA)-based Coverage Control Mechanism (GA-CCM) for clustered WSNs. GA-CCM provides an add-on mechanism that is designed to be integrated with any centralized clustering protocol to enhance its energy efficiency. GA-CCM finds the optimal set of active nodes that provides full area coverage and puts the redundant sensors into sleep mode to save energy. Extensive simulations of GA-CCM on 25 different WSNs topologies are conducted. Performance results are evaluated and compared against several well-known clustering protocols as well as a coverage-aware clustering protocol. Results show that GA-CCM always achieves full area coverage while minimizing the redundancy degree and the number of active nodes. To further evaluate the performance of GA-CCM as an add-on to existing clustering protocols, we integrate it with a Particle Swarm Optimization based CH selection protocol (PSO-CH), a comprehensive clustering protocol that considers many clustering objectives. To the best of our knowledge, PSO-CH has the lowest overall energy consumption among well-known clustering protocols. Experimental results show that this integration of GA-CCM to PSO-CH further improves its performance in terms of energy efficiency and packets delivery rate.


Sleep scheduling Clustering WSNs 


  1. 1.
    Afsar, M.M., Tayarani-N, M.H.: Clustering in sensor networks: a literature survey. J. Netw. Comput. Appl. 46, 198–226 (2014)CrossRefGoogle Scholar
  2. 2.
    Boudaren, M.E.Y., Senouci, M.R., Senouci, M.A., Mellouk, A.: New trends in sensor coverage modeling and related techniques: a brief synthesis. In: 2014 International Conference on Smart Communications in Network Technologies (SaCoNeT), pp. 1–6, June 2014Google Scholar
  3. 3.
    Elhabyan, R.S., Yagoub, M.C.E.: Particle swarm optimization protocol for clustering in wireless sensor networks: a realistic approach. In: Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014), pp. 345–350, August 2014Google Scholar
  4. 4.
    Elhabyan, R.S., Yagoub, M.C.: Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. J. Netw. Comput. Appl. 52, 116–128 (2015). Scholar
  5. 5.
    Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002)CrossRefGoogle Scholar
  6. 6.
    Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91(9), 992–1007 (2006)CrossRefGoogle Scholar
  7. 7.
    Kumar, D., Aseri, T.C., Patel, R.: Eehc: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Comput. Commun. 32(4), 662–667 (2009). Scholar
  8. 8.
    Latiff, N., Tsimenidis, C., Sharif, B.: Energy-aware clustering for wireless sensor networks using particle swarm optimization. In: IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2007), pp. 1–5, September 2007Google Scholar
  9. 9.
    Mostafaei, H., Montieri, A., Persico, V., Pescapé, A.: A sleep scheduling approach based on learning automata for WSN partialcoverage. J. Netw. Comput. Appl. 80, 67–78 (2017)CrossRefGoogle Scholar
  10. 10.
    Rahmanian, A., Omranpour, H., Akbari, M., Raahemifar, K.: A novel genetic algorithm in LEACH-C routing protocol for sensor networks. In: 2011 24th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 001096–001100, May 2011Google Scholar
  11. 11.
    Soro, S., Heinzelman, W.B.: Cluster head election techniques for coverage preservation in wireless sensor networks. Ad Hoc Netw. 7(5), 955–972 (2009)CrossRefGoogle Scholar
  12. 12.
    Texas Instruments: Chipcon CC2420 radio transceiver data sheet (2013). Accessed 25 September 2014
  13. 13.
    Wu, Y., Ai, C., Gao, S., Li, Y.: p-percent coverage in wireless sensor networks. In: Li, Y., Huynh, D.T., Das, S.K., Du, D.-Z. (eds.) WASA 2008. LNCS, vol. 5258, pp. 200–211. Springer, Heidelberg (2008). Scholar
  14. 14.
    Youssef, A., Younis, M., Youssef, M., Agrawala, A.: WSN16-5: distributed formation of overlapping multi-hop clusters in wireless sensor networks. IEEE Globecom 2006, 1–6 (2006)Google Scholar

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© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.School of Information Technology, Faculty of Engineering and DesignCarleton UniversityOttawaCanada

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