Boosted sooty tern and piranhav foraging meta-heuristic optimized cluster head selection-based routing algorithm for extending network lifetime in WSNs (original) (raw)

Abstract

Clustered Wireless Sensor Networks (WSNs) help in the construction of robust and scalable network infrastructure which increases the probability of minimizing energy consumption with extended network lifetime. But the clustered WSNs pose the challenges of non-uniform energy consumption, inadequate cluster head allocation and imbalanced distribution of load in the network. This challenges dramatically impact the network lifetime when improper clusters are constructed. This improper clusters in turn makes the sensor node to prematurely die due to increased energy consumption. Potential cluster formation and optimal cluster head selection techniques are essential for the purpose of improving the clustering quality that contributes towards better energy stability and extended network lifetime. In this paper, Boosted Sooty Tern Optimization Algorithm-based protocol with multiple objectives (BSHPFMOCS) is proposed for enhancing the quality of clustering with the objective of improving energy stability and prolonged network lifetime in clustered WSNs. This BSHOA facilitates an accurate search process which helps in selecting optimal CHs depending on the fitness function that concentrates on the improvement of clusters’ aggregation. This clustering protocol incorporated an advanced cluster formation strategy which entrusted the CHs to select their own cluster members depending on minimized intra-cluster distance. It further included Piranhav Foraging Optimization Algorithm (PFOA) for employing sink mobility that addresses the problem of hot-spot in WSNs. The simulation results of BSHOA protocol confirmed better network lifetime of 10.76%, improved throughput of 18.42% with reduced packet delay of 20.86%, and minimized energy consumption of 21.94%, compared to the baseline clustering protocols used for investigation.

Access this article

Log in via an institution

Subscribe and save

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

Data availability

Data sharing not applicable – no new data generated.

References

  1. Balamurugan A, Janakiraman S, Priya MD, Malar ACJ (2022) Hybrid Marine predators optimization and improved particle swarm optimization-based optimal cluster routing in wireless sensor networks (WSNs). China Commun 19(6):219–247
    Article Google Scholar
  2. Kumar A, Webber JL, Haq MA, Gola KK, Singh P, Karupusamy S, Alazzam MB (2022) Optimal cluster head selection for energy efficient wireless sensor network using hybrid competitive swarm optimization and harmony search algorithm. Sustain Energy Technol Assess 52:102243
    Google Scholar
  3. Sengathir J, Rajesh A, Dhiman G, Vimal S, Yogaraja CA, Viriyasitavat W (2022) A novel cluster head selection using hybrid Artificial Bee colony and Firefly Algorithm for network lifetime and stability in WSNs. Connection Sci 34(1):387–408
    Article Google Scholar
  4. Jayaraman G, Dhulipala VS (2022) FEECS: fuzzy-based energy-efficient cluster head selection algorithm for lifetime enhancement of wireless sensor networks. Arab J Sci Eng 47(2):1631–1641
    Article Google Scholar
  5. Yadav RK, Mahapatra RP (2022) Hybrid metaheuristic algorithm for optimal cluster head selection in wireless sensor network. Pervasive Mob Comput 79:101504
    Article Google Scholar
  6. Wu M, Li Z, Chen J, Min Q, Lu T (2022) A dual cluster-head energy-efficient routing Algorithm based on Canopy optimization and K-Means for WSN. Sensors 22(24):9731
    Article Google Scholar
  7. Kalburgi SS, Manimozhi M (2022) Taylor-spotted hyena optimization algorithm for reliable and energy-efficient cluster head selection based secure data routing and failure tolerance in WSN. Multimedia Tools Appl 81(11):15815–15839
    Article Google Scholar
  8. Yalçın S, Erdem E (2022) TEO-MCRP: thermal exchange optimization-based clustering routing protocol with a mobile sink for wireless sensor networks. J King Saud University-Computer Inform Sci 34(8):5333–5348
    Article Google Scholar
  9. Ajay P, Nagaraj B, Jaya J (2022) Smart spider monkey optimization (SSMO) for energy-based cluster-head selection adapted for biomedical engineering applications. Contrast Media & Molecular Imaging, 2022
  10. Khodeir MA, Ababneh JI, Alamoush BAS (2022) Manta ray foraging optimization (MRFO)-based energy-efficient cluster head selection algorithm for wireless sensor networks. Journal of Electrical and Computer Engineering, 2022
  11. Senthil Kumaran R, Nagarajan G (2022) Mobile sink and fuzzy based relay node routing protocol for network lifetime enhancement in wireless sensor networks. Wireless Netw 28(5):1963–1975
    Article Google Scholar
  12. Kavitha V, Ganapathy K (2022) Galactic swarm optimized convolute network and cluster head elected energy-efficient routing protocol in WSN. Sustain Energy Technol Assess 52:102154
    Google Scholar
  13. Priyanka BN, Jayaparvathy R, DivyaBharathi D (2022) Efficient and dynamic cluster head selection for improving network lifetime in WSN using whale optimization algorithm. Wireless Pers Commun 123(2):1467–1481
    Article Google Scholar
  14. Kaedi M, Bohlooli A, Pakrooh R (2022) Simultaneous optimization of cluster head selection and inter-cluster routing in wireless sensor networks using a 2-level genetic algorithm. Appl Soft Comput 128:109444
    Article Google Scholar
  15. Samiayya D, Radhika S, Chandrasekar A (2023) An optimal model for enhancing network lifetime and cluster head selection using hybrid snake whale optimization. Peer-to-Peer Netw Appl 16(4): 1–16
    Article Google Scholar
  16. Kumar MM, Chaparala A (2020) A hybrid BFO-FOA-based energy efficient cluster head selection in energy harvesting wireless sensor network. Int J Communication Networks Distrib Syst 25(2):205–222
    Article Google Scholar
  17. Nagarajan L, Thangavelu S (2021) Hybrid grey wolf sunflower optimisation algorithm for energy-efficient cluster head selection in wireless sensor networks for lifetime enhancement. IET Commun 15(3):384–396
    Article Google Scholar
  18. Kaur J, Rani P, Dahiya BP (2021) Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks. World Journal of Engineering
    Google Scholar
  19. Umashankar ML, Anitha TN, Mallikarjunaswamy S (2021) An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm. Indian J Sci Technol 14(3):270–288
    Article Google Scholar
  20. Roberts MK, Ramasamy P (2022) Optimized hybrid routing protocol for energy-aware cluster head selection in wireless sensor networks. Digit Signal Proc 130:103737
    Article Google Scholar
  21. Ramalingam R, Dinesh K, Sreedevi M, Jaleel A, Koushal KK, Thanuja G (2022) An opposition-based Grey Wolf Optimization for Cluster Head Selection in Wireless Sensor Networks. Electronics 11(16):2593. https://doi.org/10.3390/electronics11162593
    Article Google Scholar
  22. Chaurasia S, Kumar K, Kumar N (2023) MOCRAW: a meta-heuristic optimized cluster head selection-based routing algorithm for wsns. Ad Hoc Netw 141:103079
    Article Google Scholar
  23. Hemavathi S, Latha B (2023) FRHO: fuzzy rule-based hybrid optimization for optimal cluster head selection and enhancing quality of service in wireless sensor network. J Supercomputing 79(11): 1–28
    Article Google Scholar
  24. Suresh K, Sreeja Mole SS, Joseph S, Kumar A (2023) F2SO: an energy efficient cluster-based routing protocol using fuzzy firebug swarm optimization algorithm in WSN. Comput J 66(5):1126–1138
    Article Google Scholar
  25. Houssein EH, Oliva D, Celik E, Emam MM, Ghoniem RM (2023) Boosted sooty tern optimization algorithm for global optimization and feature selection. Expert Syst Appl 213:119015
    Article Google Scholar
  26. Jia H, Li Y, Sun K, Cao N, Zhou HM (2021) Hybrid Sooty Tern optimization and Differential Evolution for feature selection. Comput Syst Sci Eng, 39(3): 321-335.
    Article Google Scholar
  27. He J, Peng Z, Cui D, Qiu J, Li Q, Zhang H (2023) Enhanced sooty tern optimization algorithm using multiple search guidance strategies and multiple position update modes for solving optimization problems. Appl Intell 53(6):6763–6799
    Article Google Scholar
  28. Janakiraman S (2023) Improved bat optimization algorithm and enhanced artificial bee colony-based cluster routing scheme for extending network lifetime in wireless sensor networks. Int J Commun Syst, 36(5), e5428
    Article MathSciNet Google Scholar
  29. Jayalakshmi P, Sridevi S, Janakiraman S (2021) A hybrid artificial bee colony and harmony search algorithm-based metahueristic approach for efficient routing in WSNs. Wireless Pers Commun 121(4):3263–3279
    Article Google Scholar
  30. Balamurugan A, Janakiraman S, Priya DM (2022) Modified African buffalo and group teaching optimization algorithm-based clustering scheme for sustaining energy stability and network lifetime in wireless sensor networks. Trans Emerg Telecommunications Technol, 33(1)
  31. Sengathir J, Deva Priya M, Nithiavathy R, Peter S (2023), March S. COPRAS-Based Decision-Making Strategy for Optimal Cluster Head Selection in WSNs. In Proceedings of International Conference on Recent Trends in Computing: ICRTC 2022 (pp. 537–549). Singapore: Springer Nature Singapore
  32. Janakiraman S (2024) Energy efficient clustering protocol using hybrid bald eagle search optimization algorithm for improving network longevity in WSNs. Multimedia Tools Appl 83(25): 1–23
    Article Google Scholar
  33. Cao S, Qian Q, Cao Y, Li W, Huang W, Liang J (2023) A novel Meta-heuristic algorithm for Numerical and Engineering optimization problems. Piranha Foraging Optimization Algorithm (PFOA). IEEE Access
    Google Scholar
  34. Cao, S., Qian, Q., Cao, Y., Li, W., Zhou, L., He, H., … Liang, J. (2022, November).The Piranha Foraging Optimization Algorithm Oriented to Numerical Optimization Problems.In 2022 7th International Conference on Robotics and Automation Engineering (ICRAE)(pp. 388–395). IEEE

Download references

Funding

There is no funding received for this research work.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
    R. S. Amshavalli & D. Devi
  2. Department of Computer Science and Engineering, RMD Engineering College, Kavarapettai, Chennai, Tamil Nadu, India
    S. Srinivasan
  3. School of Computing, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Chennai, 600119, Tamil Nadu, India
    R ShaliniRajan
  4. Department of Computer Science and Engineering, Sri Sai Ram Engineering College, Chennai, Tamil Nadu, India
    S Anitha Jebamani

Authors

  1. R. S. Amshavalli
  2. D. Devi
  3. S. Srinivasan
  4. R ShaliniRajan
  5. S Anitha Jebamani

Contributions

R S.Amshavalli formulated the problem, Devi implemented, Srinivasan performed the experimental validation process. Shalini Rajan conducted the literature review, Anitha Jebamani has written and reviewed the complete manuscript.

Corresponding author

Correspondence toR. S. Amshavalli.

Ethics declarations

Subscription only.

Ethics approval

Not applicable.

Conflicts of interest/Competing interests

The author declare that there is no competing interest.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article

Amshavalli, R.S., Devi, D., Srinivasan, S. et al. Boosted sooty tern and piranhav foraging meta-heuristic optimized cluster head selection-based routing algorithm for extending network lifetime in WSNs.Peer-to-Peer Netw. Appl. 18, 66 (2025). https://doi.org/10.1007/s12083-024-01880-y

Download citation

Keywords