Load Balanced Fuzzy-Based Clustering for WSNs

  • Deepika AgrawalEmail author
  • Sudhakar Pandey
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1059)


The wireless sensor networks (WSNs) form an integral part of the Internet of Things (IoT). The prospective use of WSNs in various applications has grown interested in WSNs. Since it is almost not possible to replace or recharge the nodes battery when they are deployed. Hence, energy consumption should be carefully monitored. Minimizing the consumption of the energy of the sensor nodes leads to the prolongation of network lifetime. This paper proposes a clustering protocol based on fuzzy logic which not only prolongs the network life span but also balances the load among nodes. The proposed protocol is evaluated with many protocols. The output obtained proved that the proposed protocol outperforms over existing standard protocols.


Fuzzy logic Clustering WSNs Network lifetime Energy efficiency 


  1. 1.
    Akyildiz IF, Weilian S, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422CrossRefGoogle Scholar
  2. 2.
    Akkaya K, Younis M (2005) A survey on routing protocols for wireless sensor networks. Ad Hoc Netw 3(3):325–349CrossRefGoogle Scholar
  3. 3.
    Abbasi AA, Younis M (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30(14):2826–2841CrossRefGoogle Scholar
  4. 4.
    Afsar MM, Tayarani NM H (2014) Clustering in sensor networks: a literature survey. J Netw Comput Appl 46:198–226CrossRefGoogle Scholar
  5. 5.
    Agarwal PK, Procopiuc CM (2002) Exact and approximation algorithms for clustering. Algorithmica 33(2):201–226MathSciNetCrossRefGoogle Scholar
  6. 6.
    Bagci H, Yazici A (2013) An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Appl Soft Comput 13(4):1741–1749CrossRefGoogle Scholar
  7. 7.
    Zungeru AM, Ang LM, Seng KP (2012) Classical and swarm intelligence based routing protocols for wireless sensor networks: a survey and comparison. J Netw Comput Appl 35(5):1508–1536CrossRefGoogle Scholar
  8. 8.
    Zadeh LA (1965) Information and control. Fuzzy Sets 8(3):338–353Google Scholar
  9. 9.
    Heinzelman WR, ChandrakasanA, Balakrishnan H (2002) nergy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences, 2000. IEEE, p 10Google Scholar
  10. 10.
    Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379CrossRefGoogle Scholar
  11. 11.
    Gupta I, Riordan D, Sampalli S (2005) Cluster-head election using fuzzy logic for wireless sensor networks. In: Proceedings of the 3rd Annual Communication Networks and Services Research Conference, 2005. IEEE pp 255–260Google Scholar
  12. 12.
    Taheri H, Neamatollahi P, Younis OM, Naghibzadeh S, Yaghmaee MH (2012) An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic. Ad Hoc Netw 10(7):1469–1481CrossRefGoogle Scholar
  13. 13.
    Balakrishnan B, Balachandran S (2017) FLECH: fuzzy logic based energy efficient clustering hierarchy for nonuniform wireless sensor networks. Wireless Commun Mobile ComputGoogle Scholar
  14. 14.
    Sert SA, Bagci H, Yazici A (2015) MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Appl Soft Comput 30:151–165CrossRefGoogle Scholar
  15. 15.
    Agrawal D, Pandey S (2018) FUCA: Fuzzy‐based unequal clustering algorithm to prolong the lifetime of wireless sensor networks. Int J Commun Syst 31(2)CrossRefGoogle Scholar
  16. 16.
    Almajidi AM, Pawar VP, Alammari A (2019) K-means-based method for clustering and validating wireless sensor network. In: International conference on innovative computing and communications. Springer, Singapore, pp 251–258Google Scholar
  17. 17.
    Agrawal P, Anand V, Tripathi S, Pandey S, Kumar S (2019) A solution for successful routing in low–mid-density network using updated Azimuthal protocol. In: International conference on innovative computing and communications. Springer, Singapore, pp 339–347Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of ITNIT RaipurRaipurIndia

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