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Internet of Things Energy Efficient Cluster-Based Routing Using Hybrid Particle Swarm Optimization for Wireless Sensor Network

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Abstract

Specialized transducers in wireless sensor networks that offer sensing services to the internet of things devices have limited storage and energy resources. One of the most vital issues in WSN design is power usage, as it is nearly impossible to recharge or replace sensor nodes’ batteries. A prominent role in conserving power for energy-constrained networks is served by the clustering algorithm. It is possible to reduce network energy usage and network lifespan prolongation by proper balancing of the network load with Cluster Head (CH) election. The single-hop inter-cluster routing technique, in which there is a direct transfer from CHs to the base station, is done by the low energy adaptive clustering hierarchy. However, for networks with large-regions, this technique is not viable. An optimized Orphan-LEACH (O-LEACH) has been proposed in this work to facilitate the formation of a novel process of clustering, which can result in minimized usage of energy as well as enhanced network longevity. Sufficient energy is possessed by the orphan node, which will attempt to cover the network. The proposed work’s primary novel contribution is the O-LEACH protocol that supplies the entire network’s coverage with the least number of orphaned nodes and has extremely high connectivity rates. A hybrid optimization utilizing simulated annealing with Lightning Search Algorithm (LSA) (SA-LSA), and particle swarm optimization with LSA (PSO-LSA) Algorithm is proposed. These proposed techniques effectively manage the CH election achieving optimal path routing and minimization in energy usage, resulting in the enhanced lifespan of the WSN. The proposed technique’s superior performance, when compared with other techniques, is confirmed from the outcomes of the experimentations.

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References

  1. Yarinezhad, R., & Hashemi, S. N. (2020). A sensor deployment approach for target coverage problems in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 1–16.

  2. Rani, S., Talwar, R., Malhotra, J., Ahmed, S. H., Sarkar, M., & Song, H. (2015). A novel scheme for an energy-efficient Internet of Things based on wireless sensor networks. Sensors, 15(11), 28603–28626.

    Article  Google Scholar 

  3. Zhou, Y., Wang, N., & Xiang, W. (2016). Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm. IEEE Access, 5, 2241–2253.

    Article  Google Scholar 

  4. Sun, W., Tang, M., Zhang, L., Huo, Z., & Shu, L. (2020). A survey of using swarm intelligence algorithms in IoT. Sensors, 20(5), 1420.

    Article  Google Scholar 

  5. Wang, Y. (2020). Optimization of wireless sensor network for dairy cow breeding based on particle swarm optimization. In 2020 International conference on intelligent transportation, big data & smart city (ICITBS) (pp. 524–527). IEEE.

  6. Anand, V., & Pandey, S. (2020). A new approach of GA–PSO-based clustering and routing in wireless sensor networks. International Journal of Communication Systems, 33(16), e4571.

    Google Scholar 

  7. Zhang, G., & Zhang, L. (2019). WSN location algorithm based on efficient simulated annealing weighted DV-Hop. In 2019 4th international conference on communication and information systems (ICCIS) (pp. 113–117). IEEE.

  8. Zhang, Y., & Liu, Y. (2020). A novel localization algorithm based on grey wolf optimization for WSNs. In 2020 IEEE 10th international conference on electronics information and emergency communication (ICEIEC) (pp. 127–130). IEEE.

  9. Xu, M., Zhou, J., & Yang, R. (2020). Elite niche particle swarm optimization for energy clustering in aeronautical wireless sensor network. In IOP conference series: Materials science and engineering (Vol. 926, No. 1, p. 012024). IOP Publishing.

  10. Zhang, Y., & Wang, Y. (2020). A novel energy-aware bio-inspired clustering scheme for IoT communication. Journal of Ambient Intelligence and Humanized Computing, 1–10.

  11. Demri, M., Ferouhat, S., Zakaria, S., & Barmati, M. E. (2020). A hybrid approach for optimal clustering in wireless sensor networks using cuckoo search and simulated annealing algorithms. In 2020 2nd international conference on mathematics and information technology (ICMIT) (pp. 202–207). IEEE.

  12. Kadiravan, G., & Sujatha, P. (2019). Bat with teaching and learning based optimization algorithm for node localization in mobile wireless sensor networks. In Smart Network Inspired Paradigm and Approaches in IoT Applications (pp. 203–220). Springer, Singapore.

  13. Jerbi, W., Guermazi, A., & Trabelsi, H. (2016). O-LEACH of routing protocol for wireless sensor networks. In 2016 13th international conference on computer graphics, imaging, and visualization (CGiV) (pp. 399–404). IEEE.

  14. Li, D., & Wen, X. B. (2015). An improved PSO algorithm for distributed localization in wireless sensor networks. International Journal of Distributed Sensor Networks, 11(7), 970272.

    Article  Google Scholar 

  15. Potthuri, S., Shankar, T., & Rajesh, A. (2018). Lifetime improvement in wireless sensor networks using hybrid differential evolution and simulated annealing (DESA). Ain Shams Engineering Journal, 9(4), 655–663.

    Article  Google Scholar 

  16. Faris, H., Aljarah, I., Al-Madi, N., & Mirjalili, S. (2016). Optimizing the learning process of feedforward neural networks using lightning search algorithms. International Journal on Artificial Intelligence Tools, 25(06), 1650033.

    Article  Google Scholar 

  17. Lu, Y., & Zhou, Y. (2017). Design of multilayer microwave absorbers using hybrid binary lightning search algorithm and simulated annealing. Progress In Electromagnetics Research, 78, 75–90.

    Article  Google Scholar 

Download references

Acknowledgements

We claim that we are the only ones who wrote this article. Except where due attribution has been made, this essay contains no content previously published by any other individual, to the best of my knowledge.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India

    G. A. Senthil & Arun Raaza

  2. Department of Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India

    N. Kumar

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  1. G. A. Senthil
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  2. Arun Raaza
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  3. N. Kumar
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Correspondence to G. A. Senthil.

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Senthil, G.A., Raaza, A. & Kumar, N. Internet of Things Energy Efficient Cluster-Based Routing Using Hybrid Particle Swarm Optimization for Wireless Sensor Network. Wireless Pers Commun 122, 2603–2619 (2022). https://doi.org/10.1007/s11277-021-09015-9

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