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Pareto Multi-objective Termite Colony Optimization Based EDT Clustering for Wireless Chemical Sensor Network

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Abstract

Wireless Chemical Sensor Network (WSCN) is one of the significant research areas in terrestrial networking due to their versatility. Expanding the overall runtime without adding additional batteries or hardware is one of the most challenging tasks. To overcome this challenge a novel Pareto Multi-objective Termite Colony Optimization-based EDT (Energy, Distance, Time) clustering technique, has been proposed in this research. Initially, Pareto multi-objective optimization strategy is used in the termite colony optimization technique to address WCSN routing's resource constraints and security concerns. In addition, a function called Substitute Cluster Head (SCH) is utilized, which replaces the current Cluster Head and the rotating system was proposed to reduce the energy loss during the CH rotation. Improving energy reliability and network lifetime are the main aspects of this research. The effectiveness of the proposed strategy is examined using the parameters, such as lifetime of the network, energy consumption, load balancing, and Throughput. The existing technique attains the network lifetime of 12.4% of LEACH, 15.4% of Genetic Algorithm, and 23.37% of EE-LEACH, but our suggested PMTCO-based EDT Clustering extends network lifetime by 41.2% and decreases energy usage by 7% when determining the SCH.

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References

  1. Kassal, P., Steinberg, M. D., & Steinberg, I. M. (2018). Wireless chemical sensors and biosensors: a review. Sensors and Actuators B: Chemical, 266, 228–245.

    Article  Google Scholar 

  2. Shahraki, A., Taherkordi, A., Haugen, Ø., & Eliassen, F. (2020). Clustering objectives in wireless sensor networks: A survey and research direction analysis. Computer Networks, 180, 107376.

    Article  Google Scholar 

  3. Alghamdi, T. A. (2020). Energy efficient protocol in wireless sensor network: Optimized cluster head selection model. Telecommunication Systems, 74(3), 331–345.

    Article  MathSciNet  Google Scholar 

  4. Singh, H., & Singh, D. (2021). Hierarchical clustering and routing protocol to ensure scalability and reliability in large-scale wireless sensor networks. The Journal of Supercomputing, 77(9), 10165–10183.

    Article  Google Scholar 

  5. Moussa, N., Alaoui, E. B. E., & A. (2021). An energy-efficient cluster-based routing protocol using unequal clustering and improved ACO techniques for WSNs. Peer-to-Peer Networking and Applications, 14(3), 1334–1347.

    Article  Google Scholar 

  6. Verma, S., Sood, N., & Sharma, A. K. (2019). A novelistic approach for energy efficient routing using single and multiple data sinks in heterogeneous wireless sensor network. Peer-to-Peer Networking and Applications, 12(5), 1110–1136.

    Article  Google Scholar 

  7. Gupta, G. P., & Jha, S. (2018). Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony Search based metaheuristic techniques. Engineering Applications of Artificial Intelligence, 68, 101–109.

    Article  Google Scholar 

  8. Khabiri, M., & Ghaffari, A. (2018). Energy-aware clustering-based routing in wireless sensor networks using cuckoo optimization algorithm. Wireless Personal Communications, 98(3), 2473–2495.

    Article  Google Scholar 

  9. Chen, L., Liu, W., Gong, D. and Chen, Y., 2020, June. Clustering and routing optimization algorithm for heterogeneous wireless sensor networks. In 2020 International Wireless Communications and Mobile Computing (IWCMC) (pp. 407–411). IEEE.

  10. 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 

  11. Wang, Z., Ding, H., Li, B., Bao, L., & Yang, Z. (2020). An energy efficient routing protocol based on improved artificial bee colony algorithm for wireless sensor networks. IEEE Access, 8, 133577–133596.

    Article  Google Scholar 

  12. Sah, D. K., & Amgoth, T. (2020). A novel efficient clustering protocol for energy harvesting in wireless sensor networks. Wireless Networks, 26(6), 4723–4737.

    Article  Google Scholar 

  13. Puneeth, D., & Kulkarni, M. (2020). Data aggregation using compressive sensing for energy efficient routing strategy. Procedia Computer Science, 171, 2242–2251.

    Article  Google Scholar 

  14. 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 

  15. Dehghani, S., Barekatain, B., & Pourzaferani, M. (2018). An enhanced energy-aware cluster-based routing algorithm in wireless sensor networks. Wireless Personal Communications, 98(1), 1605–1635.

    Article  Google Scholar 

  16. Asha, G. R. (2018). Energy efficient clustering and routing in a wireless sensor network. Procedia computer science, 134, 178–185.

    Article  Google Scholar 

  17. Darabkh, K. A., Al-Maaitah, N. J., Jafar, I. F., & Ala’F, K.,. (2018). EA-CRP: A novel energy-aware clustering and routing protocol in wireless sensor networks. Computers & Electrical Engineering, 72, 702–718.

    Article  Google Scholar 

  18. Zachariah, U. E., & Kuppusamy, L. (2022). A hybrid approach to energy efficient clustering and routing in wireless sensor networks. Evolutionary Intelligence, 15(1), 593–605.

    Article  Google Scholar 

  19. Maheshwari, P., Sharma, A. K., & Verma, K. (2021). Energy efficient cluster-based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Networks, 110, 102317.

    Article  Google Scholar 

  20. Dwivedi, A. K., & Sharma, A. K. (2021). EE-LEACH: Energy enhancement in LEACH using fuzzy logic for homogeneous WSN. Wireless Personal Communications, 120(4), 3035–3055.

    Article  Google Scholar 

  21. Lee, W., Yonghee, J., Hyunseung, J., Chulhun, S., Hosung, C., & Hojin, L. (2019). Wireless-Powered Chemical Sensor by 2.4 GHz Wi-Fi Energy-Harvesting Metamaterial. Micromachines, 10(1), 12.

    Article  Google Scholar 

  22. Wang, Q., Lin, D., Yang, P., & Zhang, Z. (2019). An energy-efficient compressive sensing-based clustering routing protocol for WSNs. IEEE Sensors Journal, 19(10), 3950–3960. https://doi.org/10.1109/JSEN.2019.2893912

    Article  Google Scholar 

  23. Wang, T., Zhang, G., Yang, X., & Vajdi, A. (2018). Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks. Journal of Systems and Software, 146, 196–214.

    Article  Google Scholar 

  24. Pachlor, R., & Shrimankar, D. (2018). LAR-CH: A cluster-head rotation approach for sensor networks. IEEE Sensors Journal, 18(23), 9821–9828.

    Article  Google Scholar 

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

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Cochin University of Science and Technology (CUSAT), Kuttanad, Kerala, India

    Abin John Joseph

  2. Department of Applied Science, Cochin University of Science and Technology (CUSAT), Kuttanad, Kerala, India

    R. Asaletha

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  1. Abin John Joseph
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  2. R. Asaletha
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Correspondence to Abin John Joseph.

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Joseph, A.J., Asaletha, R. Pareto Multi-objective Termite Colony Optimization Based EDT Clustering for Wireless Chemical Sensor Network. Wireless Pers Commun 130, 2329–2343 (2023). https://doi.org/10.1007/s11277-023-10245-2

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