Skip to main content

Advertisement

Log in

Hybrid fuzzy logic and artificial Flora optimization algorithm-based two tier cluster head selection for improving energy efficiency in WSNs

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Wireless Sensor Network (WSN) is the one of the hot area of research in which energy stability and network lifetime are considered to be the twin challenges during its application. Clustering is the optimum energy efficiency strategy that organizes the sensor nodes into potential groups for the objective of attaining energy stability and network lifetime. In this energy potent clustering process, cluster head selection is determined to be highly significant in order to balance energy among the nodes sensor nodes. Moreover, two-tier cluster head selection that includes temporary and final cluster head is identified to be challenging in WSNs. In this paper, Hybrid Fuzzy Logic and Artificial Flora Optimization Algorithm (FL-AFA)-based Two Tier Cluster Head Selection is proposed for improving energy efficiency and prolog network lifetime. This FL-AFA scheme achieved the cluster head selection in two stages, such as, i) Temporary Cluster Head (TCH) selection using FL and, ii) Final Cluster Head (FCH) selection using AFA. In the first stage, the concept of fuzzy logic applied over the input parameters of residual energy (RE), distance to BS (DTBS), and node degree (NDE). In the second stage, the benefits of AFA is employed for computing the fitness function through distance to nearby nodes (DNN), cluster compactness estimation factor (CCEF), and position estimation (PE). Simulation experiments of the proposed FL-AFA scheme and the benchmarked schemes are conducted based on the evaluation metrics of energy efficiency, network lifetime, average delay, and packet delivery ratio (PDR) under the impact of different sensor nodes. .

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Vajdi A, Zhang G, Wang Y, Wang T (2016) A new self-management model for largescale event-driven wireless sensor networks. IEEE Sensors J 16(20):7537–7544

    Article  Google Scholar 

  2. Sabet M, Naji HR (2015) A decentralized energy efficient hierarchical cluster-based routing algorithm for wireless sensor networks. AEU-International Journal of Electronics and Communications 69(5):790–799

    Article  Google Scholar 

  3. Mittal N (2019) Moth flame optimization based energy efficient stable clustered routing approach for wireless sensor networks. Wirel Pers Commun 104(2):677–694

    Article  Google Scholar 

  4. Moh’d Alia O (2018) A dynamic harmony search-based fuzzy clustering protocol for energy-efficient wireless sensor networks. Ann Telecommun 73(5–6):353–365

    Article  Google Scholar 

  5. Morsy NA, AbdelHay EH, Kishk SS (2018) Proposed energy efficient algorithm for clustering and routing in WSN. Wirel Pers Commun 103(3):2575–2598

    Article  Google Scholar 

  6. Lalwani P, Das S, Banka H, Kumar C (2018) CRHS: clustering and routing in wireless sensor networks using harmony search algorithm. Neural Comput & Applic 30(2):639–659

    Article  Google Scholar 

  7. Lalwani P, Banka H, Kumar C (2017) CRWO: clustering and routing in wireless sensor networks using optics inspired optimization. Peer-to-Peer Networking and Applications 10(3):453–471

    Article  Google Scholar 

  8. Lalwani P, Banka H, Kumar C (2018) BERA: a biogeography-based energy saving routing architecture for wireless sensor networks. Soft Comput 22(5):1651–1667

    Article  Google Scholar 

  9. Rao PS, Banka H (2017) Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks. Wirel Netw 23(3):759–778

    Article  Google Scholar 

  10. Mekonnen MT, Rao KN (2017) Cluster optimization based on metaheuristic algorithms in wireless sensor networks. Wirel Pers Commun 97(2):2633–2647

    Article  Google Scholar 

  11. Yogarajan G, Revathi T (2018) Improved cluster based data gathering using ant lion optimization in wireless sensor networks. Wirel Pers Commun 98(3):2711–2731

    Article  Google Scholar 

  12. Ezhilarasi M, Krishnaveni V (2019) An evolutionary multipath energy-efficient routing protocol (EMEER) for network lifetime enhancement in wireless sensor networks. Soft Comput 23(18):8367–8377

    Article  Google Scholar 

  13. Gao F, Luo W, Ma X (2019) Energy constrained clustering routing method based on particle swarm optimization. Cluster Comput 22(3):7629–7635

    Article  Google Scholar 

  14. Sirdeshpande N, Udupi V (2017) Fractional lion optimization for cluster head-based routing protocol in wireless sensor network. J Frankl Inst 354(11):4457–4480

    Article  MathSciNet  Google Scholar 

  15. Xiu-wu YU, Hao YU, Yong L, Ren-rong X (2020) A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks. Comput Netw 167:106994

    Article  Google Scholar 

  16. Zhang Y, Wang J, Han D, Wu H, Zhou R (2017) Fuzzy-logic based distributed energy-efficient clustering algorithm for wireless sensor networks. Sensors 17(7):1554

    Article  Google Scholar 

  17. Cheng L, Wu XH, Wang Y (2018) Artificial flora (AF) optimization algorithm. Applied Sciences 8(3):329

    Article  Google Scholar 

  18. Xiuwu Y, Qin L, Yong L, Mufang H, Ke Z, Renrong X (2019) Uneven clustering routing algorithm based on glowworm swarm optimization. Ad Hoc Netw 93:101923

    Article  Google Scholar 

  19. Chen J, Mao G, Li C, Liang W, Zhang D (2018) "Capacity of cooperative vehicular networks with infrastructure support: multiuser case," in IEEE Transactions on Vehicular Technology, vol. 67, no. 2, pp. 1546–1560

  20. Zhang D, Li G, Zheng K, Ming X and Pan Z (2014) "An energy-balanced routing method based on forward-aware factor for wireless sensor networks," in IEEE Transactions on Industrial Informatics, vol. 10, no. 1, pp. 766–773

  21. Zhang D, Liu S, Zhang T, Liang Z (2017) Novel unequal clustering routing protocol considering energy balancing based on network partition & distance for mobile education. J Netw Comput Appl 88:1–9

    Article  Google Scholar 

  22. Zhang D, Zhang T, Liu X (2018) Novel self-adaptive routing service algorithm for application in VANET. Appl Intell 49(5):1866–1879

    Article  Google Scholar 

  23. Zhang D, Wang X, Song X, Zhao D (2014) "A Novel Approach to Mapped Correlation of ID for RFID Anti-Collision," in IEEE Transactions on Services Computing, vol. 7, no. 4, pp. 741–748

  24. Yang J, Ding M, Mao G, Lin Z, Zhang D, Luan TH (2019) Optimal base station antenna Downtilt in downlink cellular networks. IEEE Trans Wirel Commun 18(3):1779–1791

    Article  Google Scholar 

  25. Zhang D, Zhang T, Dong Y, Liu X, Cui Y, Zhao D (2018) Novel optimized link state routing protocol based on quantum genetic strategy for mobile learning. J Netw Comput Appl 122:37–49

    Article  Google Scholar 

  26. Zhang D, Ge H, Zhang T, Cui Y, Liu X, Mao G (2019) New multi-hop clustering algorithm for vehicular ad hoc networks. IEEE Trans Intell Transp Syst 20(4):1517–1530

    Article  Google Scholar 

  27. Zhang T, Zhang D, Yan H, Qiu J, Gao J (2021) A new method of data missing estimation with FNN-based tensor heterogeneous ensemble learning for internet of vehicle. Neurocomputing 420:98–110

    Article  Google Scholar 

  28. Kardi A, Zagrouba R (2020) Rach: a new radial cluster head selection algorithm for wireless sensor networks. Wirel Pers Commun 113(4):2127–2140

    Article  Google Scholar 

  29. Subramanian P, Sahayaraj JM, Senthilkumar S, Alex DS (2020) A hybrid grey wolf and crow search optimization algorithm-based optimal cluster head selection scheme for wireless sensor networks. Wirel Pers Commun 113(2):905–925

    Article  Google Scholar 

  30. Nagarajan L, Thangavelu S (2020) Hybrid grey wolf sunflower optimisation algorithm for energy-efficient cluster head selection in wireless sensor networks for lifetime enhancement. IET Commun 3(1):45–56

    Google Scholar 

  31. Sharma R, Vashisht V, Singh U (2020) EeTMFO/GA: a secure and energy efficient cluster head selection in wireless sensor networks. Telecommun Syst 74(3):253–268

    Article  Google Scholar 

  32. Kumar MM, Chaparala A (2020) A hybrid BFO-FOA-based energy efficient cluster head selection in energy harvesting wireless sensor network. International Journal of Communication Networks and Distributed Systems 25(2):205

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ramakrishnan Anandkumar.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anandkumar, R. Hybrid fuzzy logic and artificial Flora optimization algorithm-based two tier cluster head selection for improving energy efficiency in WSNs. Peer-to-Peer Netw. Appl. 14, 2072–2083 (2021). https://doi.org/10.1007/s12083-021-01174-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-021-01174-7

Keywords

Navigation