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Evolving Systems

, Volume 10, Issue 4, pp 659–677 | Cite as

Energy efficient clustering protocol for WSNs based on bio-inspired ICHB algorithm and fuzzy logic system

  • Prateek GuptaEmail author
  • Ajay K. Sharma
Original Paper

Abstract

This paper explores the capabilities of Intelligent cluster head selection based on bacterial foraging optimization (ICHB) algorithm and fuzzy logic system (FLS) for searching better cluster head (CH) nodes without using any randomized algorithms in the network. ICHB-HEED is one of the recent clustering based protocol in the field of wireless sensor networks (WSNs). In this paper, the clustering procedures of ICHB-HEED is further improved by applying the combination of ICHB algorithm and FLS system based on residual energy, node density and distance to base station (BS) parameters which results in ICHB-Fuzzy Logic based HEED (ICFL-HEED) protocol. It alleviates the formation of holes and hot-spots in the network, delays the death of sensor nodes (SNs), minimizes the energy consumption of SNs, forms even-sized clusters and extends the network lifetime competently. The proposed ICFL-HEED protocol is compared with existing HEED & ICHB-HEED protocols and observed that the performance of ICFL-HEED is far better than these protocols.

Keywords

Clustering Wireless sensor networks ICHB BFOA HEED Fuzzy logic system Network lifetime 

Notes

References

  1. Adnan MA, Razzaque MA, Ahmed I, Isnin IF (2013) Bio-mimic optimization strategies in wireless sensor networks: a survey. Sensors 14(1):299–345CrossRefGoogle Scholar
  2. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422CrossRefGoogle Scholar
  3. Baranidharan B, Santhi B (2016) DUCF: Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Appl Soft Comput 40:495–506CrossRefGoogle Scholar
  4. Chu Y, Fei J (2017) Dynamic global PID sliding control using neural compensator for active power filter. In: Proc. of 56th annual conference of the society of instrument and control engineers of Japan (SICE), pp 1513–1517Google Scholar
  5. Du T, Qu S, Liu F, Wang Q (2015) An energy efficiency semi-static routing algorithm for WSNs based on HAC clustering method. Inf Fusion 21:18–29CrossRefGoogle Scholar
  6. Fang Y, Fei J, Hu T (2018) Adaptive backstepping fuzzy sliding mode vibration control of flexible structure. J Low Freq Noise Vib Active Control.  https://doi.org/10.1177/1461348418767097 CrossRefGoogle Scholar
  7. Fei J, Lu C (2018) Adaptive fractional order sliding mode controller with neural estimator. J Frankl Inst 355(5):2369–2391MathSciNetCrossRefGoogle Scholar
  8. Fei J, Wang T (2018) Adaptive fuzzy-neural-network based on RBFNN control for active power filter. Int J Mach Learn Cybern.  https://doi.org/10.1007/s13042-018-0792-y CrossRefGoogle Scholar
  9. Gupta I, Riordan D, Sampalli S (2005) Cluster-head election using fuzzy logic for wireless sensor networks. In Proceedings of 3rd annual communication networks and services research conference (CNSR’05), pp 255–260Google Scholar
  10. Gupta P, Sharma AK (2017) Clustering-based optimized HEED protocols for WSNs using bacterial foraging optimization and fuzzy logic system. Soft Comput.  https://doi.org/10.1007/s00500-017-2837-7 CrossRefGoogle Scholar
  11. Gupta P, Sharma AK (2018) Designing of energy efficient stable clustering protocols based on BFOA for WSNs. J Ambient Intell Humaniz Comput.  https://doi.org/10.1007/s12652-018-0719-1 CrossRefGoogle Scholar
  12. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670CrossRefGoogle Scholar
  13. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of 33rd annual hawaii international conference on system sciences, vol 2, pp  1–10Google Scholar
  14. Huang H, Wu J (2005) A probabilistic clustering algorithm in wireless sensor networks. In: Proceedings of IEEE 62nd vehicular technology conference, VTC, vol 3, pp 1796–1798Google Scholar
  15. Khedo K, Subramanian R (2009) Misense hierarchical cluster based routing algorithm (MiCRA) for wireless sensor networks. Int J Electr Comput Energ Electron Commun Eng 3(4):28–33Google Scholar
  16. Kim JM, Park SH, Han YJ, Chung TM (2008) CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks. Proc Int Conf Adv Commun Technol 1:654–659Google Scholar
  17. Kour H, Sharma AK (2010) Hybrid energy efficient distributed protocol for heterogeneous wireless sensor network. Int J Comput Appl 4(5):37–41Google Scholar
  18. Kulkarni RV, Forster A, Venayagamoorthy GK (2011) Computational intelligence in wireless sensor networks: a survey. Commun Surv Tutor 13(1):68–96CrossRefGoogle Scholar
  19. Kumar D, Aseri TC, Patel R (2009) EEHC: energy efficient heterogeneous clustered scheme for wireless sensor networks. Comput Commun 32(4):662–667CrossRefGoogle Scholar
  20. Kumarawadu P, Dechene DJ, Luccini M, Sauer A (2008) Algorithms for node clustering in wireless sensor networks: a survey. In: Proceedings of 4th international conference on information and automation for sustainability, pp 295–300Google Scholar
  21. Mao S, Zhao C (2011) Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO. J China Univ Posts Telecommun 18(6):89–97CrossRefGoogle Scholar
  22. Mhatre V, Rosenberg C (2004) Design guidelines for wireless sensor networks: communication, clustering and aggregation. Ad Hoc Netw 2(1):45–63CrossRefGoogle Scholar
  23. Negnevitsky M (2001) Artificial intelligence: a guide to intelligent systems, 1st edn. Addison-Wesley Longman Publishing Co., Inc., BostonGoogle Scholar
  24. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 22(3):52–67MathSciNetCrossRefGoogle Scholar
  25. Qing L, Zhu Q, Wang M (2006) Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Comput Commun 29(12):2230–2237CrossRefGoogle Scholar
  26. Sabet M, Naji HR (2015) A decentralized energy efficient hierarchical cluster-based routing algorithm for wireless sensor networks. AEU Int J Electron Commun 69(5):790–799CrossRefGoogle Scholar
  27. Salim A, Osamy W, Khedr AM (2014) IBLEACH: intra-balanced leach protocol for wireless sensor networks. Wirel Netw 20(6):1515–1525CrossRefGoogle Scholar
  28. Sharma N, Sharma AK (2016) Cost analysis of hybrid adaptive routing protocol for heterogeneous wireless sensor network. Sādhanā 41(3):283–288MathSciNetzbMATHGoogle Scholar
  29. Wang LX (1997) A course in fuzzy systems and control, 1st edn. Prentice-Hall Inc, Upper Saddle RiverzbMATHGoogle Scholar
  30. Wang MY, Ding J, Chen WP, Guan WQ (2015) SEARCH: a stochastic election approach for heterogeneous wireless sensor networks. IEEE Commun Lett 19(3):443–446CrossRefGoogle Scholar
  31. Xie WX, Zhang QY, Sun ZM, Zhang F (2015) A clustering routing protocol for WSN based on type-2 fuzzy logic and ant colony optimization. Wirel Pers Commun 84(2):1165–1196CrossRefGoogle Scholar
  32. 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

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringDr B R Ambedkar National Institute of TechnologyJalandharIndia
  2. 2.I. K. Gujral Punjab Technical UniversityJalandharIndia

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