Abstract
Wireless sensor networks (WSN) are emerging versatile and low-cost solutions for several applications. However, energy efficiency is a major issue in WSNs. The sensor nodes typically have limited energy but the energy consumption exceeds during data transmission. An energy efficient cluster based routing protocol reduces the transmission distance among the base station (BS) and the sensor nodes in terms of organizing the nodes in the form of clusters and evade the nodes with lower energy. Therefore, energy efficient Ultra-Scalable Ensemble Clustering technique is introduced in this work to cluster the nodes for handling large data. Then, the Flamingo Search Algorithm is employed for cluster head (CH) selection due to its less computational complexity and high stability. Finally, Q-Learning approach is adopted to select the shortest path between CHs and BS as it is capable of path selection at complex network conditions. The reward points in this approach are generated based on the objective function that considers the distance among the CH and BS, coverage area and energy consumption. Experiments are evaluated and analyzed with existing approaches in terms of alive nodes, time consumption, rounds for last node dead, first node dead, half node dead, throughput and total residual energy. The consequences prove that the offered technique can enhance the energy efficiency of WSN compared to similar existing approaches.










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References
Yun, W. K., & Yoo, S. J. (2021). Q-learning-based data-aggregation-aware energy-efficient routing protocol for wireless sensor networks. IEEE Access, 9, 10737–10750. https://doi.org/10.1109/ACCESS.2021.3051360
Jin, Y., Kwak, K. S., & Yoo, S. J. (2020). A novel energy supply strategy for stable sensor data delivery in wireless sensor networks. IEEE Systems Journal, 1–12. https://doi.org/10.1109/jsyst.2019.2963695.
Maheshwari, P., Sharma, A. K., & Verma, K. (2020). Energy efficient cluster based Routing protocol for WSN using Butterfly optimization algorithm and ant colony optimization. Ad Hoc Networks, 102317. https://doi.org/10.1016/j.adhoc.2020.102317.
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: Practical Innovations, Open Solutions, 1–1. https://doi.org/10.1109/access.2020.3010313.
Mansourkiaie, F., & Ahmed, M. H. (2015). Cooperative routing in wireless networks: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 17(2), 604–626. https://doi.org/10.1109/comst.2014.2386799.
Haseeb, K., Islam, N., Almogren, A., Din, I. U., Almajed, H. N., & Guizani, N. (2019). Secret sharing-based Energy-aware and multi-hop routing protocol for IoT based WSNs. Ieee Access : Practical Innovations, Open Solutions, 1–1. https://doi.org/10.1109/access.2019.2922971.
Mazaideh, M. A., & Levendovszky, J. (2021). A multi-hop routing algorithm for WSNs based on compressive sensing and multiple objective genetic algorithm. Journal of Communications and Networks, 23(2), 138–147. https://doi.org/10.23919/jcn.2021.000003.
Adnan, M., Yang, L., Ahmad, T., & Tao, Y. (2021). An unequally clustered multi-hop routing protocol based on fuzzy logic for Wireless Sensor Networks. Ieee Access : Practical Innovations, Open Solutions, 9, 38531–38545. https://doi.org/10.1109/access.2021.3063097.
Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (n.d.) (Eds.). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. https://doi.org/10.1109/hicss.2000.926982.
SureshKumar, K., & Vimala, P. (2021). Energy efficient routing protocol using exponentially-ant lion whale optimization algorithm in wireless sensor networks. Computer Networks, 197, 108250.
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.
Al-Otaibi, S., Al-Rasheed, A., Mansour, R. F., Yang, E., Joshi, G. P., & Cho, W. (2021). Hybridization of metaheuristic algorithm for dynamic cluster-based routing protocol in wireless sensor Networksx. Ieee Access: Practical Innovations, Open Solutions, 9, 83751–83761.
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.
Nandan, A. S., Singh, S., & Awasthi, L. K. (2021). An efficient cluster head election based on optimized genetic algorithm for movable sinks in IoT enabled HWSNs. Applied Soft Computing, 107, 107318.
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.
Xu, X. W., Pan, J. S., Mohamed, A. W., & Chu, S. C. (2022). Improved fish migration optimization with the opposition learning based on elimination principle for cluster head selection. Wireless Networks, 28(3), 1017–1038.
Yadav, R. K., & Mahapatra, R. P. (2022). Hybrid metaheuristic algorithm for optimal cluster head selection in wireless sensor network. Pervasive and Mobile Computing, 79, 101504.
Kathiroli, P., & Selvadurai, K. (2022). Energy efficient cluster head selection using improved Sparrow Search Algorithm in Wireless Sensor Networks. Journal of King Saud University-Computer and Information Sciences, 34(10), 8564–8575.
Sengathir, J., Rajesh, A., Dhiman, G., Vimal, S., Yogaraja, C. A., & Viriyasitavat, W. (2022). A novel cluster head selection using hybrid Artificial Bee colony and Firefly Algorithm for network lifetime and stability in WSNs. Connection Science, 34(1), 387–408.
Narayan, V., Daniel, A. K., & Chaturvedi, P. (2022). FGWOA: An efficient heuristic for cluster head selection in WSN using fuzzy based grey wolf optimization algorithm.
Sheriba, S. T., & Rajesh, D. H. (2021). Energy-efficient clustering protocol for WSN based on improved black widow optimization and fuzzy logic. Telecommunication Systems, 77(1), 213–230.
Yadav, R. K., & Mahapatra, R. P. (2021). Energy aware optimized clustering for hierarchical routing in wireless sensor network. Computer Science Review, 41, 100417.
Osamy, W., El-Sawy, A. A., & Salim, A. (2020). CSOCA: Chicken Swarm optimization based clustering algorithm for Wireless Sensor Networks. Ieee Access : Practical Innovations, Open Solutions, 8, 60676–60688. https://doi.org/10.1109/access.2020.2983483.
Han, Y., Li, G., Xu, R., Su, J., Li, J., & Wen, G. (2020). Clustering the Wireless Sensor Networks: A meta-heuristic approach. Ieee Access : Practical Innovations, Open Solutions, 8, 214551–214564. https://doi.org/10.1109/access.2020.3041118.
Arikumar, K. S., Natarajan, V., & Satapathy, S. C. (2020). EELTM: an energy efficient LifeTime maximization Approach for WSN by PSO and fuzzy-based unequal clustering. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-020-04616-1.
Arunachalam, N., Shanmugasundaram, G., & Arvind, R. (2021). Squirrel search optimization-based cluster head selection technique for prolonging lifetime in WSN’s. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-08843-z.
Liang, J., Xu, Z., Xu, Y., Zhou, W., & Li, C. (2021). Adaptive cooperative routing transmission for energy heterogeneous wireless sensor networks. Physical Communication, 49, 101460.
Guo, W., Yan, C., & Lu, T. (2019). Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing. International Journal of Distributed Sensor Networks, 15(2), 155014771983354. https://doi.org/10.1177/1550147719833541.
Younus, M. U., Khan, M. K., & Bhatti, A. R. (2021). Improving the software-defined wireless sensor networks routing performance using reinforcement learning. IEEE Internet of Things Journal, 9(5), 3495–3508.
Huang, D., Wang, C. D., Wu, J., Lai, J. H., & Kwoh, C. K. (2019). Ultra-scalable spectral clustering and ensemble clustering. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2019.2903410.
Zhiheng, W., & Jianhua, L. (2021). Flamingo search algorithm: A new swarm intelligence optimization algorithm. Ieee Access : Practical Innovations, Open Solutions, 9, 88564–88582. https://doi.org/10.1109/access.2021.3090512.
Rezaeipanah, A., Amiri, P., Nazari, H., Mojarad, M., & Parvin, H. (2021). An energy-aware hybrid approach for wireless sensor networks using re-clustering-based multi-hop routing. Wireless Personal Communications, 120(4), 3293–3314.
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Abraham, R., Vadivel, M. An Energy Efficient Wireless Sensor Network with Flamingo Search Algorithm Based Cluster Head Selection. Wireless Pers Commun 130, 1503–1525 (2023). https://doi.org/10.1007/s11277-023-10342-2
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DOI: https://doi.org/10.1007/s11277-023-10342-2