An Energy-Efficient Distributed Unequal Clustering Approach for Lifetime Maximization in Wireless Sensor Network

  • S. Manikandan
  • M. Jeyakarthic
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


In Wireless Sensor Networks, higher energy consumption is caused due to gathering and transmission of a large amount of sensor data. In clustering, each sensor node forwards its sensed information to the Cluster Head, which further transmits the processed information to the sink. Thus, such cluster heads have more chances of being affected by node death due to higher workload and therefore rapidly decreases the lifetime of the sensor network and eventually affects the network performance. This research paper introduces a clustering algorithm named Energy-Efficient Distributed Unequal Clustering Approach for balancing the energy depletion among the cluster heads which could eliminate the hot spot problem and thus achieve lifetime maximization in Wireless Sensor Network. It implements unequal clustering technique over the sensor nodes where the cluster head election is based on fuzzy inference system, where the sensor nodes discovered with the higher chance are finalized as cluster heads. Based on the input fuzzy parameters, the cluster size is optimally adjusted to achieve load balancing among the clusters. The simulation is executed to demonstrate the performance of proposed approach with the existing LEACH, CHEF, DUCF energy-efficient clustering approach in various network scenarios.


Wireless Sensor Network Energy efficiency Unequal clustering Fuzzy logic Load balancing Distributed approach 


  1. 1.
    D. Rajendra Prasad, P.V. Naganjaneyulu, K. Satya Prasad, A hybrid swarm optimization for energy efficient clustering in multi-hop wireless sensor network. Wirel. Pers. Commun. 94, 2459–2471 (2017)CrossRefGoogle Scholar
  2. 2.
    A. Pughat, V. Sharma, A review on stochastic approach for dynamic power management in wireless sensor networks. Hum. Centric Comput. Inf. Sci. 5, 4 (2015)CrossRefGoogle Scholar
  3. 3.
    N. Kumar, J. Kim, ELACCA: efficient learning automata based cell clustering algorithm for wireless sensor networks. Wirel. Pers. Commun. 73, 1495–1512 (2013)CrossRefGoogle Scholar
  4. 4.
    J. Huang, Y. Hong, Z. Zhao, Y. Yuan, An energy-efficient multi-hop routing protocol based on grid clustering for wireless sensor networks. Clust. Comput. 20, 3071–3083 (2017)CrossRefGoogle Scholar
  5. 5.
    Energy Efficient Backoff Hierarchical Clustering Algorithms for Multi-Hop Wireless Sensor Networks, Accessed 5 Nov 2018
  6. 6.
    R. Priyadarshi, S.K. Soni, V. Nath, Energy efficient cluster head formation in wireless sensor network. Microsyst. Technol. 24, 4775–4784 (2018)CrossRefGoogle Scholar
  7. 7.
    W. Zhou, Energy efficient clustering algorithm based on neighbors for wireless sensor networks. J. Shanghai Univ. Engl. Ed. 15, 150–153 (2011)CrossRefGoogle Scholar
  8. 8.
    K. Guravaiah, R. Leela Velusamy, Energy efficient clustering algorithm using RFD based multi-hop communication in wireless sensor networks. Wirel. Pers. Commun. 95, 3557–3584 (2017)CrossRefGoogle Scholar
  9. 9.
    Energy Efficient Clustering Scheme (EECS) for Wireless Sensor Network with Mobile Sink, Accessed 5 Nov 2018
  10. 10.
    M. Ulema, J.M. Nogueira, B. Kozbe, Management of wireless ad hoc networks and wireless sensor networks. J. Netw. Syst. Manag. 14, 327–333 (2006)CrossRefGoogle Scholar
  11. 11.
    D. Yun-Zhong, L. Ren-Ze, Research of energy efficient clustering algorithm for multilayer wireless heterogeneous sensor networks prediction research. Multimed. Tools Appl. 76, 19345–19361 (2017)CrossRefGoogle Scholar
  12. 12.
    Triangular fuzzy-based spectral clustering for energy-efficient routing in wireless sensor network, Accessed 5 Nov 2018
  13. 13.
    I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)CrossRefGoogle Scholar
  14. 14.
    A.A. Abbasi, M. Younis, A survey on clustering algorithms for wireless sensor networks. Comput. Commun. 30(14–15), 2826–2841 (2007)CrossRefGoogle Scholar
  15. 15.
    M. Liu, Y. Zheng, J. Cao, G. Chen, L. Chen, H. Gong, An energy-aware protocol for data gathering applications in wireless sensor networks, in Proceedings of the IEEE International Conference on Communications, Glasgow, UK, 24–28 Jun 2007, pp. 3629–3635Google Scholar
  16. 16.
    J.M. Kim, S.H. Park, Y.J. Han, T.M. Chung, CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks, in Proceedings of the 10th International Conference on Advanced Communication Technology (ICACT), Gangwon-Do, Korea, 17–20 Feb 2008, pp. 654–659Google Scholar
  17. 17.
    J. Yu, Y. Qi, G. Wang, Q. Guo, X. Gu, An energy-aware distributed unequal clustering protocol for wireless sensor networks. Int. J. Distrib. Sens. Networks 2011, 202145 (2011)CrossRefGoogle Scholar
  18. 18.
    F. Bajaber, I. Awan, Adaptive decentralized re-clustering protocol for wireless sensor networks. J. Comput. Syst. Sci. 77(2), 282–292 (2011)MathSciNetCrossRefGoogle Scholar
  19. 19.
    C.E. Perkins, E.M. Belding-Royer, S.R. Das, Ad hoc on demand distance vector (AODV) routing. IETF RFC 3561, 2003, pp. 1–67Google Scholar
  20. 20.
    A. Yadav, Y.N. Singh, R.R. Singh, Improving routing performance in AODV with link prediction in mobile adhoc networks. Wirel. Pers. Commun. 83(1), 603–618 (2015)MathSciNetCrossRefGoogle Scholar
  21. 21.
    V. Gupta, R. Pandey, An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks. Eng. Sci. Technol. Int. J. 19(2), 1050–1058 (2016)CrossRefGoogle Scholar
  22. 22.
    S. Thompson, K. Suresh Joseph, Particle swarm optimization-based energy efficient channel assignment technique for clustered cognitive radio sensor networks. Comput. J. 61(6), 926–936 (2018)CrossRefGoogle Scholar
  23. 23.
    S. Thompson, K. Suresh Joseph, Cognitive radio assisted OLSR routing for vehicular sensor networks. Proc. Comput. Sci. 89, 271–282 (2016)CrossRefGoogle Scholar
  24. 24.
    S. Thompson, K. Suresh Joseph, PSO assisted OLSR routing for cognitive radio vehicular sensor networks, in Proceedings of the International Conference on Informatics and Analytics, 2016, pp. 1–8Google Scholar
  25. 25.
    S. Naeimi, H. Ghafghazi, C.O. Chow, H. Ishii, A survey on the taxonomy of cluster-based routing protocols for homogeneous wireless sensor networks. Sensors (Switzerland) 12(6), 7350–7409 (2012)CrossRefGoogle Scholar
  26. 26.
    E.H. Mamdani, Application of fuzzy logic to approximate reasoning using linguistic synthesis, in Proceedings of the 1997 27th International Symposium on Multiple-Valued Logic, Los Alamitos, CA, 28–30 May 1997Google Scholar
  27. 27.
    W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocols for wireless microsensor networks, in Proceedings of the Hawaii International Conference on Systems Sciences, Maui, HI, 4–7 Jan 2000, pp. 1–10Google Scholar
  28. 28.
    O. Younis, S. Fahmy, HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans. Mob. Comput. 3, 366–379 (2004)CrossRefGoogle Scholar
  29. 29.
    L. Qing, Q. Zhu, M. Wang, Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Comput. Commun. 29, 2230–2237 (2006)CrossRefGoogle Scholar
  30. 30.
    Y. Liao, H. Qi, W. Li, Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks. IEEE Sensors J. 13, 1498–1506 (2013)CrossRefGoogle Scholar
  31. 31.
    D. Lin, Q. Wang, D. Lin, Y.A. Deng, Energy-efficient clustering routing protocol based on evolutionary game theory in wireless sensor networks. Int. J. Distrib. Sens. Netw. 2015, 1–12 (2015)CrossRefGoogle Scholar
  32. 32.
    A. Alaybeyoglu, A distributed fuzzy logic-based root selection algorithm for wireless sensor networks. Comput. Electr. Eng. 41, 216–225 (2015)CrossRefGoogle Scholar
  33. 33.
    R. Dutta, S. Gupta, M. Das, Low-energy adaptive unequal clustering protocol using fuzzy c-means in wireless sensor networks. Wirel. Pers. Commun. 79, 1187–1209 (2014)CrossRefGoogle Scholar
  34. 34.
    D.M.S. Bhatti, N. Saeed, H. Nam, Fuzzy C-means clustering and energy efficient cluster head selection for cooperative sensor network. Sensors 16, E1459 (2016)CrossRefGoogle Scholar
  35. 35.
    B. Baranidharan, B. Santhi, DUCF: distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Appl. Soft Comput. 40, 495–506 (2016)CrossRefGoogle Scholar
  36. 36.
    Y. Zhang, J. Wang, D. Han, H. Wu, R. Zhou, Fuzzy-logic based distributed energy-efficient clustering algorithm for wireless sensor networks. Sensors 17, 1554 (2017)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • S. Manikandan
    • 1
  • M. Jeyakarthic
    • 1
  1. 1.Department of Computer and Information SciencesAnnamalai UniversityChidambaramIndia

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