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Link to original content: https://doi.org/10.1007/s11235-024-01176-9
Energy-efficient cluster head selection in wireless sensor networks-based internet of things (IoT) using fuzzy-based Harris hawks optimization | Telecommunication Systems Skip to main content

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Energy-efficient cluster head selection in wireless sensor networks-based internet of things (IoT) using fuzzy-based Harris hawks optimization

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Abstract

The Internet of things (IoT) has become a cornerstone of the fourth industrial revolution. IoT sensor devices in the network are provisioned with limited resources, such as little processing speed, minimal computing capacity, and less power. Furthermore, IoT devices are battery-powered, which cannot provide battery sufficiently to some applications resulting in an energy scarcity problem. Clustering is an efficient method in IoT networks to save energy. Nodes can coordinate communication by selecting an optimal cluster head (CH) within the cluster and transmitting information to a central node or sink. The CH minimizes energy consumption associated with communication overhead and extends the overall lifespan of the network by facilitating coordination between clusters and the central server. Many existing optimization techniques have proposed CH selection to improve the network's lifespan but all the existing algorithms on CH selection are not practical due to the long convergence time. This research paper proposes a novel fuzzy-based Harris Hawks Optimization (FHHO) algorithm that chooses optimal CH considering Residual energy (RER) and distance between sink and node. The fitness function is evaluated using fuzzy logic over maximization and minimization network parameters. Extensive experimentations were conducted to test and validate the performance of proposed FHHO algorithm on MATLAB 2019a tool. And, the results stated that the proposed method FHHO has better results as compared to other CH selection techniques, namely, PSO-ECHS, FIGWO, and GWO-C, in network lifespan by 18–44% and throughput by 5–20%.

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Data availability statement

We declare that all the data associated with the manuscript is mentioned in the manuscript.

References

  1. Senthil, G. A., Raaza, A., & Kumar, N. (2021). Internet of things multi hop energy efficient cluster-based routing using particle swarm optimization. Wireless Networks, 27, 5207–5215.

    Article  Google Scholar 

  2. Manuel, A. J., Deverajan, G. G., Patan, R., & Gandomi, A. H. (2020). Optimization of routing-based clustering approaches in wireless sensor network: Review and open research issues. Electronics, 9(10), 1630.

    Article  Google Scholar 

  3. Behera, T. M., Mohapatra, S. K., Samal, U. C., Khan, M. S., Daneshmand, M., & Gandomi, A. H. (2019). Residual energy-based cluster-head selection in WSNs for IoT application. IEEE Internet of Things Journal, 6(3), 5132–5139.

    Article  Google Scholar 

  4. Sennan, S., Ramasubbareddy, S., Balasubramaniyam, S., Nayyar, A., Abouhawwash, M., & Hikal, N. A. (2021). T2FL-PSO: Type-2 fuzzy logic-based particle swarm optimization algorithm used to maximize the lifetime of internet of things. IEEE Access, 9, 63966–63979.

    Article  Google Scholar 

  5. Somula, R., Cho, Y., & Mohanta, B. K. (2024). SWARAM: osprey optimization algorithm-based energy-efficient cluster head selection for wireless sensor network-based internet of things. Sensors, 24(2), 521.

    Article  Google Scholar 

  6. Chandrasekaran, S. K., & Rajasekaran, V. A. (2024). Energy-efficient cluster head using modified fuzzy logic with WOA and path selection using enhanced CSO in IoT-enabled smart agriculture systems. The Journal of Supercomputing, 80, 11149–11190.

  7. Somula, R., Cho, Y., & Mohanta, B. K. (2023). EACH-COA: an energy-aware cluster head selection for the internet of things using the coati optimization algorithm. Information, 14(11), 601.

    Article  Google Scholar 

  8. Gowda, S. S., & Ramalingappa, A. (2024). Energy optimized cluster head selection based on multi-objective sand cat swarm optimization in under water wireless sensor networks. International Journal of Intelligent Engineering & Systems, 17(1), 383.

  9. Sankar, S., Ramasubbareddy, S., Dhanaraj, R. K., Balusamy, B., Gupta, P., Ibrahim, W., & Verma, R. (2023). Cluster head selection for the internet of things using a sandpiper optimization algorithm (SOA). Journal of Sensors, 2023(1), 3507600.

  10. Janarthanan, A., & Srinivasan, V. (2024). Multi-objective cluster head-based energy aware routing using optimized auto-metric graph neural network for secured data aggregation in Wireless Sensor Network. International Journal of Communication Systems, 37(3), e5664.

    Article  Google Scholar 

  11. Aramuthakannan, S., Kumar, R. R., Mariammal, G., & Geetha, M. (2024). Enhanced cluster head selection and routing in wireless sensor networks using fuzzy logic and adaptive cat swarm optimization. International Journal of Intelligent Engineering & Systems, 17(1), 721.

  12. Kirubasri, G., Sankar, S., Guru Prasad, M. S., Naga Chandrika, G., & Ramasubbareddy, S. (2023). LQETA-RP: link quality based energy and trust aware routing protocol for wireless multimedia sensor networks. International Journal of System Assurance Engineering and Management, 15(1), 564–576.

  13. Srivastava, A., & Mishra, P. K. (2024). Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption. Concurrency and Computation: Practice and Experience, 36(4), e7921.

  14. Sankar, S., Ramasubbareddy, S., Luhach, A. K., & alnumay, W. S., & Chatterjee, P. (2022). NCCLA: New caledonian crow learning algorithm based cluster head selection for Internet of Things in smart cities. Journal of Ambient Intelligence and Humanized Computing, 13(10), 4651–4661.

    Article  Google Scholar 

  15. Wu, D., Yang, Z., Li, T., & Liu, J. (2024). JOCP: A jointly optimized clustering protocol for industrial wireless sensor networks using double‐layer selection evolutionary algorithm. Concurrency and Computation: Practice and Experience, 36(4), e7927.

  16. Sankar, S., Somula, R., Parvathala, B., Kolli, S., & Pulipati, S. (2022). SOA-EACR: Seagull optimization algorithm based energy aware cluster routing protocol for wireless sensor networks in the livestock industry. Sustainable Computing: Informatics and Systems, 33, 100645.

    Google Scholar 

  17. Janakiraman, S. (2024). Energy efficient clustering protocol using hybrid bald eagle search optimization algorithm for improving network longevity in WSNs. Multimedia Tools and Applications, 1–23.

  18. Sennan, S., Ramasubbareddy, S., Balasubramaniyam, S., Nayyar, A., Kerrache, C. A., & Bilal, M. (2021). MADCR: Mobility aware dynamic clustering-based routing protocol in Internet of Vehicles. China Communications, 18(7), 69–85.

    Article  Google Scholar 

  19. Sharma, S. K., & Chawla, M. (2024). PRESEP: Cluster based metaheuristic algorithm for energy-efficient wireless sensor network application in internet of things. Wireless Personal Communications, 133(2), 1243–1263.

  20. Sennan, S., Ramasubbareddy, S., Nayyar, A., Nam, Y., & Abouhawwash, M. (2021). LOA-RPL: Novel energy-efficient routing protocol for the internet of things using lion optimization algorithm to maximize network lifetime. Computers, Materials & Continua, 69(1) 351–371.

  21. Afzal, H., Kanwal, S., Zulfiqar, M., Gill, H. B., & Mufti, M. R. (2023). Performance evaluation of various algorithms for cluster head selection in WSNs. The Nucleus, 60(1), 35–44.

    Google Scholar 

  22. Sennan, S., Somula, R., Luhach, A. K., Deverajan, G. G., Alnumay, W., Jhanjhi, N. Z., Ghosh, U., & Sharma, P. (2021). Energy efficient optimal parent selection based routing protocol for Internet of Things using firefly optimization algorithm. Transactions on Emerging Telecommunications Technologies, 32(8), e4171.

    Article  Google Scholar 

  23. Srivastava, A., & Mishra, P. K. (2023). Load-balanced cluster head selection enhancing network lifetime in WSN using hybrid approach for IoT applications. Journal of Sensors, 2023(1), 4343404.

  24. Zheng, W. M., Xu, L. D., Pan, J. S., & Chai, Q. W. (2023). Cluster head selection strategy of WSN based on binary multi-objective adaptive fish migration optimization algorithm. Applied Soft Computing, 148, 110826.

    Article  Google Scholar 

  25. Sankar, S., Srinivasan, P., Ramasubbareddy, S., & Balamurugan, B. (2020). Energy-aware multipath routing protocol for internet of things using network coding techniques. International Journal of Grid and Utility Computing, 11(6), 838–846.

    Article  Google Scholar 

  26. Kumar, M., Mukherjee, P., Verma, K., Verma, S., & Rawat, D. B. (2021). Improved deep convolutional neural network based malicious node detection and energy-efficient data transmission in wireless sensor networks. IEEE Transactions on Network Science and Engineering, 9(5), 3272–3281.

  27. Ouyang, Y., Liu, A., Xiong, N., & Wang, T. (2020). An effective early message ahead join adaptive data aggregation scheme for sustainable IoT. IEEE Transactions on Network Science and Engineering, 8(1), 201–219.

    Article  Google Scholar 

  28. Sankar, S., Srinivasan, P., Luhach, A. K., Somula, R., & Chilamkurti, N. (2020). Energy-aware grid-based data aggregation scheme in routing protocol for agricultural internet of things. Sustainable Computing: Informatics and Systems, 28, 100422.

    Google Scholar 

  29. Zhang, R., Zhang, S., Wang, T., & Xiong, N. (2021). A class of differential data processing-based data gathering schemes in internet of things. IEEE Transactions on Network Science and Engineering, 8(4), 3113–3128.

  30. Sennan, S., Ramasubbareddy, S., Luhach, A. K., Nayyar, A., & Qureshi, B. (2020). CT-RPL: Cluster tree based routing protocol to maximize the lifetime of internet of things. Sensors, 20(20), 5858.

    Article  Google Scholar 

  31. Wu, D., Sun, X., & Ansari, N. (2019). An FSO-based drone assisted mobile access network for emergency communications. IEEE Transactions on Network Science and Engineering, 7(3), 1597–1606.

    Article  Google Scholar 

  32. Usman, M., Jan, M. A., He, X., & Chen, J. (2018). A mobile multimedia data collection scheme for secured wireless multimedia sensor networks. IEEE Transactions on Network Science and Engineering, 7(1), 274–284.

    Article  Google Scholar 

  33. Ravi, G., & Kashwan, K. R. (2015). A new routing protocol for energy efficient mobile applications for ad hoc networks. Computers & Electrical Engineering, 48, 77–85.

    Article  Google Scholar 

  34. Shende, D. K., & Sonavane, S. S. (2020). CrowWhale-ETR: CrowWhale optimization algorithm for energy and trust aware multicast routing in WSN for IoT applications. Wireless Networks, 26, 4011–4029.

  35. Shyjith, M. B., Maheswaran, C. P., & Reshma, V. K. (2021). Optimized and dynamic selection of cluster head using energy efficient routing protocol in WSN. Wireless Personal Communications, 116(1), 577–599.

    Article  Google Scholar 

  36. Alazab, M., Lakshmanna, K., Reddy, T., Pham, Q. V., & Maddikunta, P. K. R. (2021). Multi-objective cluster head selection using fitness averaged rider optimization algorithm for IoT networks in smart cities. Sustainable Energy Technologies and Assessments, 43, 100973.

    Article  Google Scholar 

  37. Sefati, S., Abdi, M., & Ghaffari, A. (2021). Cluster-based data transmission scheme in wireless sensor networks using black hole and ant colony algorithms. International Journal of Communication Systems, 34(9), e4768.

    Article  Google Scholar 

  38. Senthil, G. A., Raaza, A., & Kumar, N. (2022). Internet of things energy efficient cluster-based routing using hybrid particle swarm optimization for wireless sensor network. Wireless Personal Communications, 122(3), 2603–2619.

    Article  Google Scholar 

  39. Agrawal, D., Wasim Qureshi, M. H., Pincha, P., Srivastava, P., Agarwal, S., Tiwari, V., & Pandey, S. (2020). GWO-C: Grey wolf optimizer based clustering scheme for WSNs. International Journal of Communication Systems, 33(8), e4344.

    Article  Google Scholar 

  40. Mehta, D., & Saxena, S. (2020). MCH-EOR: Multi-objective cluster head based energy-aware optimized routing algorithm in wireless sensor networks. Sustainable Computing: Informatics and Systems, 28, 100406.

    Google Scholar 

  41. Karthick, P. T., & Palanisamy, C. (2019). Optimized cluster head selection using krill herd algorithm for wireless sensor network. Automatika: Časopis za Automatiku, Mjerenje, Elektroniku, Računarstvo i Komunikacije, 60(3), 340–348.

    Article  Google Scholar 

  42. Poluru, R. K., & Ramasamy, L. K. (2020). Optimal cluster head selection using modified rider assisted clustering for IoT. IET Communications, 14(13), 2189–2201.

    Article  Google Scholar 

  43. Ahmad, T. (2020). Energy EC: An artificial bee colony optimization based energy efficient cluster leader selection for wireless sensor networks. Journal of Information and Optimization Sciences, 41(2), 587–597.

    Article  Google Scholar 

  44. Pathak, A. (2020). A proficient bee colony-clustering protocol to prolong lifetime of wireless sensor networks. Journal of Computer Networks and Communications, 2020(1), 1236187.

  45. Sennan, S., Balasubramaniyam, S., Luhach, A. K., Ramasubbareddy, S., Chilamkurti, N., & Nam, Y. (2019). Energy and delay aware data aggregation in routing protocol for Internet of Things. Sensors, 19(24), 5486.

    Article  Google Scholar 

  46. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849–872.

    Article  Google Scholar 

  47. Lata, S., Mehfuz, S., Urooj, S., & Alrowais, F. (2020). Fuzzy clustering algorithm for enhancing reliability and network lifetime of wireless sensor networks. IEEE Access, 8, 66013–66024.

    Article  Google Scholar 

  48. Panchal, A., & Singh, R. K. (2021). EHCR-FCM: Energy efficient hierarchical clustering and routing using Fuzzy C-Means for wireless sensor networks. Telecommunication Systems, 76(2), 251–263.

    Article  Google Scholar 

  49. Rao, P. S., Jana, P. K., & Banka, H. (2017). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless networks, 23(7), 2005–2020.

    Article  Google Scholar 

  50. Zhao, X., Zhu, H., Aleksic, S., & Gao, Q. (2018). Energy-efficient routing protocol for wireless sensor networks based on improved grey wolf optimizer. KSII Transactions on Internet and Information Systems (TIIS), 12(6), 2644–2657.

    Google Scholar 

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Funding

The authors received no specific funding for this study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sona College of Technology, Salem, India

    Sankar Sennan

  2. Department of Information Technology, VNRVJIET, Hyderabad, India

    Somula Ramasubbareddy

  3. Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India

    Rajesh Kumar Dhanaraj

  4. Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Viet Nam

    Anand Nayyar

  5. Shiv Nadar Institution of Eminence, Greater Noida, India

    Balamurugan Balusamy

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  1. Sankar Sennan
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  2. Somula Ramasubbareddy
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  4. Anand Nayyar
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  5. Balamurugan Balusamy
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All the authors have equally contributed to the design and development of the manuscript.

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Correspondence to Rajesh Kumar Dhanaraj or Anand Nayyar.

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Sennan, S., Ramasubbareddy, S., Dhanaraj, R.K. et al. Energy-efficient cluster head selection in wireless sensor networks-based internet of things (IoT) using fuzzy-based Harris hawks optimization. Telecommun Syst 87, 119–135 (2024). https://doi.org/10.1007/s11235-024-01176-9

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