Metaheuristic Approaches for Energy Optimization in Wireless Sensor Networks: A Systematic Review of Trends, Challenges, and Future Directions
DOI:
https://doi.org/10.4108/eetiot.10328Keywords:
Wireless Sensor Networks, Metaheuristics, Energy Optimization, Routing, Clustering, Hybrid AlgorithmsAbstract
Wireless Sensor Networks (WSNs) have become a foundational technology across diverse domains, ranging from critical healthcare monitoring to large-scale environmental management. However, the severe energy constraints of sensor nodes remain a persistent bottleneck, threatening both operational efficiency and network longevity. While metaheuristic algorithms offer promising solutions, existing reviews often focus on isolated network layers or rely on outdated datasets. Addressing this gap, this Systematic Literature Review (SLR) analyzes 48 primary studies published between 2019 and 2024, offering a holistic taxonomy that integrates routing and clustering optimizations. The findings reveal that Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) continue to dominate the field, each appearing in 23.5% of studies. However, a decisive shift is observed toward hybrid techniques such as Firefly–PSO and Grey Wolf Optimization variants which demonstrate enhanced adaptability in avoiding local optima, albeit at higher computational costs. Performance evaluations remain heavily simulation-driven, primarily focusing on energy consumption (31.2%), network lifetime (29.8%), and throughput (19.9%), while real-world validations in domains like Industrial IoT remain scarce. Furthermore, the review identifies emerging trends integrating Machine Learning, Edge Computing, and UAV-assisted routing into metaheuristic frameworks, signaling a transition toward more secure and multi-objective optimization strategies. This study concludes by highlighting critical open issues in fault tolerance, heterogeneous node management, and security-aware routing, providing a strategic roadmap for developing resilient, deployment-ready WSN solutions.
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[1] N. Moussa, E. Nurellari, and A. El Belrhiti El Alaoui, ‘A novel energy-efficient and reliable ACO-based routing protocol for WSN-enabled forest fires detection’, J Ambient Intell Human Comput, vol. 14, no. 9, pp. 11639–11655, Sept. 2023, doi: 10.1007/s12652-022-03727-x.
[2] C. Rambabu, V. V. K. D. V. Prasad, and K. S. Prasad, ‘A New Version of Energy-Efficient Optimization Protocol Using ICMA-PSOGA Algorithm in Wireless Sensor Network’, SN COMPUT. SCI., vol. 3, no. 5, p. 353, June 2022, doi: 10.1007/s42979-022-01232-8.
[3] ‘A modified grasshopper optimization algorithm based on levy flight for cluster head selection in wireless sensor networks’, IJATEE, vol. 9, no. 97, Dec. 2022, doi: 10.19101/IJATEE.2021.875883.
[4] A. Sharmin, F. Anwar, and S. M. A. Motakabber, ‘Energy-Efficient Scalable Routing Protocol Based on ACO for WSNs’, in 2019 7th International Conference on Mechatronics Engineering (ICOM), Putrajaya, Malaysia: IEEE, Oct. 2019, pp. 1–6. doi: 10.1109/ICOM47790.2019.8952053.
[5] P. Rawat and S. Chauhan, ‘Particle swarm optimization-based energy efficient clustering protocol in wireless sensor network’, Neural Computing and Applications, vol. 33, pp. 14147–14165, 2021, doi: 10.1007/s00521-021-06059-7.
[6] V. Rajpoot and S. K. Mishra, ‘Reduced Energy Consumption In Wireless Sensor Network Using Particle Swarm Optimisation And Bellman-Ford Algorithm’, vol. 9, no. 02, 2020.
[7] M. Vanitha, N. Yamsani, I. H. Mohammed, S. Madhavan, and K. Al-Attabi, ‘Hybrid Salp Swarm and Particle Swarm Optimization based Secure Aware Energy-Efficient Routing in Wireless Sensor Network’, in 2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS), Kalaburagi, India: IEEE, Nov. 2023, pp. 1–5. doi: 10.1109/ICIICS59993.2023.10421116.
[8] B. Pitchaimanickam and G. Murugaboopathi, ‘A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks’, Neural Comput & Applic, vol. 32, no. 12, pp. 7709–7723, June 2020, doi: 10.1007/s00521-019-04441-0.
[9] P., T. R. M. Rahman, and N. B. Patil, ‘Energy Efficient Cluster Based Routing Using Multiobjective Improved Golden Jackal Optimization Algorithm in Wireless Sensor Networks’, IJCNA, vol. 11, no. 3, p. 304, June 2024, doi: 10.22247/ijcna/2024/19.
[10] T. Mazumder, B. V. R. Reddy, and A. Payal, ‘Energy based multi objective golden jackal optimization for cluster based routing in wireless sensor network’, Soft Comput, July 2024, doi: 10.1007/s00500-024-09920-8.
[11] H. Qabouche, A. Sahel, A. Badri, and I. E. Mourabit, ‘Energy efficient and coverage aware grey wolf optimizer based clustering process for Software-defined wireless sensor networks’, Ad Hoc Networks, vol. 151, p. 103288, 2023, doi: https://doi.org/10.1016/j.adhoc.2023.103288.
[12] Z. Wang, H. Ding, B. Li, L. Bao, and Z. Yang, ‘An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks’, IEEE Access, vol. 8, pp. 133577–133596, 2020, doi: 10.1109/ACCESS.2020.3010313.
[13] N. Ameen, N. Jamal, and L. A. Raj, ‘Comparative analysis of energy based optimized dynamic source multipath routing protocol in WSNs’, IJEECS, vol. 16, no. 1, p. 441, Oct. 2019, doi: 10.11591/ijeecs.v16.i1.pp441-455.
[14] S. Sen, L. Sahoo, K. Tiwary, V. Simic, and T. Senapati, ‘Wireless Sensor Network Lifetime Extension via K-Medoids and MCDM Techniques in Uncertain Environment’, Applied Sciences, vol. 13, no. 5, p. 3196, Mar. 2023, doi: 10.3390/app13053196.
[15] K. C. R, N. Pradeep, S. R. Devi, B. H. Pithadiya, J. Arumugam, and H. N, ‘Metaheuristic-Optimized Clustering for Improving QoS in IoT-Enabled Wireless Sensor Networks’, in 2024 International Conference on Expert Clouds and Applications (ICOECA), Bengaluru, India: IEEE, Apr. 2024, pp. 124–129. doi: 10.1109/ICOECA62351.2024.00035.
[16] G. Srinivasalu and H. Umadevi, ‘Energy and Distance Aware Multi-Objective Firebug Swarm Optimization Based Clustering and Routing in Wireless Sensor Networks’, Journal of Computer Science, vol. 19, no. 3, pp. 295–304, Mar. 2023, doi: 10.3844/jcssp.2023.295.304.
[17] M. Rathee, S. Kumar, A. H. Gandomi, K. Dilip, B. Balusamy, and R. Patan, ‘Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks’, IEEE Trans. Eng. Manage., vol. 68, no. 1, pp. 170–182, Feb. 2021, doi: 10.1109/TEM.2019.2953889.
[18] G. A. Senthil, A. Raaza, and N. Kumar, ‘Internet of Things Energy Efficient Cluster-Based Routing Using Hybrid Particle Swarm Optimization for Wireless Sensor Network’, Wireless Pers Commun, vol. 122, no. 3, pp. 2603–2619, Feb. 2022, doi: 10.1007/s11277-021-09015-9.
[19] M. S. Sivagamasundari, T. Thamaraimanalan, S. Ramalingam, and K. Balachander, ‘Improved Particle Swarm Optimization Based Distributed Energy-Efficient Opportunistic Algorithm for Clustering and Routing in WSNs’, JITM, vol. 15, no. Special Issue: Digital Twin Enabled Neural Networks Architecture Management for Sustainable Computing, Mar. 2023, doi: 10.22059/jitm.2023.91560.
[20] L. K. Ketshabetswe, A. M. Zungeru, C. K. Lebekwe, and B. Mtengi, ‘A compression-based routing strategy for energy saving in wireless sensor networks’, Results in Engineering, vol. 23, p. 102616, 2024, doi: https://doi.org/10.1016/j.rineng.2024.102616.
[21] K. Swetha, V. Lahari, G. V. V. Manikrisha, and K. B. Sai, ‘A Survey on Placement of Sensor Nodes in Deployment of Wireless Sensor Networks’, in 2019 International Conference on Intelligent Sustainable Systems (ICISS), Palladam, Tamilnadu, India: IEEE, Feb. 2019, pp. 132–139. doi: 10.1109/ISS1.2019.8907968.
[22] B. Raj, I. Ahmedy, M. Y. I. Idris, and R. Md. Noor, ‘A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks’, Wireless Communications and Mobile Computing, vol. 2022, pp. 1–53, Mar. 2022, doi: 10.1155/2022/5322649.
[23] M. Gheisari et al., ‘A Survey on Clustering Algorithms in Wireless Sensor Networks: Challenges, Research, and Trends’, in 2020 International Computer Symposium (ICS), Tainan, Taiwan: IEEE, Dec. 2020, pp. 294–299. doi: 10.1109/ICS51289.2020.00065.
[24] A. Singh, S. Sharma, and J. Singh, ‘Nature-inspired algorithms for Wireless Sensor Networks: A comprehensive survey’, Computer Science Review, vol. 39, p. 100342, Feb. 2021, doi: 10.1016/j.cosrev.2020.100342.
[25] M. H. A. Hussain, B. Mokhtar, and M. R. M. Rizk, ‘A comparative survey on LEACH successors clustering algorithms for energy-efficient longevity WSNs’, Egyptian Informatics Journal, vol. 26, p. 100477, 2024, doi: https://doi.org/10.1016/j.eij.2024.100477.
[26] A. Shahraki, A. Taherkordi, Ø. Haugen, and F. Eliassen, ‘Clustering objectives in wireless sensor networks: A survey and research direction analysis’, Computer Networks, vol. 180, p. 107376, Oct. 2020, doi: 10.1016/j.comnet.2020.107376.
[27] B. Kitchenham and S. Charters, ‘Guidelines for performing Systematic Literature Reviews in Software Engineering’, vol. 2, Jan. 2007.
[28] R. K. Yadav and R. P. Mahapatra, ‘Energy aware optimized clustering for hierarchical routing in wireless sensor network’, Computer Science Review, vol. 41, p. 100417, Aug. 2021, doi: 10.1016/j.cosrev.2021.100417.
[29] J. Ben-Othman and B. Yahya, ‘Energy efficient and QoS based routing protocol for wireless sensor networks’, Journal of Parallel and Distributed Computing, vol. 70, no. 8, pp. 849–857, Aug. 2010, doi: 10.1016/j.jpdc.2010.02.010.
[30] ‘QoS-Based Protocol for Routing in Wireless Sensor Networks | Wireless Personal Communications’. Accessed: Dec. 16, 2024. [Online]. Available: https://link.springer.com/article/10.1007/s11277-017-4731-0
[31] D. R. Raman, V. Kumar, B. G. Pillai, D. Rabadiya, S. Patre, and R. Meenakshi, ‘Optimizing Routes for Improved Quality of Service in Wireless Sensor Networks through an Energy-Aware Routing Protocol for Maximizing Lifetime’, in 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), Apr. 2024, pp. 1–5. doi: 10.1109/ICKECS61492.2024.10616757.
[32] S. J. Anandh and E. Baburaj, ‘Energy Efficient Routing Technique for Wireless Sensor Networks Using Ant-Colony Optimization’, Wireless Pers Commun, vol. 114, no. 4, pp. 3419–3433, Oct. 2020, doi: 10.1007/s11277-020-07539-0.
[33] A. Nayyar and R. Singh, ‘IEEMARP- a novel energy efficient multipath routing protocol based on ant Colony optimization (ACO) for dynamic sensor networks’, Multimed Tools Appl, vol. 79, no. 47–48, pp. 35221–35252, Dec. 2020, doi: 10.1007/s11042-019-7627-z.
[34] K. Desai and K. Rana, ‘Clustering technique for Wireless Sensor Network’, in 2015 1st International Conference on Next Generation Computing Technologies (NGCT), Sept. 2015, pp. 223–227. doi: 10.1109/NGCT.2015.7375116.
[35] A. K. Jain, S. Jain, and G. Mathur, ‘Design and performance assessment of improved evolutionary computing based LEACH protocol for energy efficient and lifetime extension of wireless sensor network’, Eng. Res. Express, vol. 6, no. 2, p. 025213, June 2024, doi: 10.1088/2631-8695/ad4aeb.
[36] A. Varatharajan, P. Ramasamy, S. Marappan, D. Ananthavadivel, and C. S. Govardanan, ‘Energy efficient data fusion approach using squirrel search optimization and recurrent neural network’, IJEECS, vol. 31, no. 1, p. 480, July 2023, doi: 10.11591/ijeecs.v31.i1.pp480-490.
[37] G. Srinivasalu and H. Umadevi, ‘Proposed energy efficient clustering and routing for wireless sensor network’, IJECE, vol. 13, no. 4, p. 4127, Aug. 2023, doi: 10.11591/ijece.v13i4.pp4127-4135.
[38] M. Z. Abd Latif, K. G. Lim, M. K. Tan, H. S. Ee Chuo, T. Wang, and K. T. Kin Teo, ‘Energy-Efficient Ant Colony Based LEACH Routing Algorithm in Wireless Sensor Network’, in 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia: IEEE, Sept. 2022, pp. 1–6. doi: 10.1109/IICAIET55139.2022.9936851.
[39] I. Banerjee and P. Madhumathy, ‘QoS enhanced energy efficient cluster based routing protocol realized using stochastic modeling to increase lifetime of green wireless sensor network’, Wireless Netw, vol. 29, no. 2, pp. 489–507, Feb. 2023, doi: 10.1007/s11276-022-03124-4.
[40] S. L. Rex B R, S. T. Tumma, J. Chandra, L. Giffina, and S. Renuga Devi, ‘Particle Swarm Optimization Method for Energy Efficient Secondary Grid Cluster Head Selection to Avoid Energy Holes in WSN’, in 2021 Asian Conference on Innovation in Technology (ASIANCON), PUNE, India: IEEE, Aug. 2021, pp. 1–7. doi: 10.1109/ASIANCON51346.2021.9544990.
[41] P. Kumar, R. Dwivedi, and V. Tyagi, ‘Fuzzy Ant Colony Optimization Based Energy Efficient Routing For Mixed Wireless Sensor Network’, in 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), GHAZIABAD, India: IEEE, Sept. 2019, pp. 1–7. doi: 10.1109/ICICT46931.2019.8977699.
[42] M. Ahmed Hamza et al., ‘Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network’, Computer Systems Science and Engineering, vol. 45, no. 2, pp. 1711–1726, 2023, doi: 10.32604/csse.2023.031467.
[43] H. Khujamatov, M. Pitchai, A. Shamsiev, A. Mukhamadiyev, and J. Cho, ‘Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks’, Sensors, vol. 24, no. 13, p. 4406, July 2024, doi: 10.3390/s24134406.
[44] S. El Khediri, A. Selmi, R. U. Khan, T. Moulahi, and P. Lorenz, ‘Energy efficient cluster routing protocol for wireless sensor networks using hybrid metaheuristic approache’s’, Ad Hoc Networks, vol. 158, p. 103473, May 2024, doi: 10.1016/j.adhoc.2024.103473.
[45] D. Lubin Balasubramanian and V. Govindasamy, ‘Study on Evolutionary Approaches for Improving the Energy Efficiency of Wireless Sensor Networks Applications’, EAI Endorsed Trans IoT, vol. 5, no. 20, p. e2, Oct. 2019, doi: 10.4108/eai.13-7-2018.164856.
[46] N. M. Maroof and M. A. Waheed, ‘Energy Efficient Clustering and Routing using Energy Centric MJSO and MACO for Wireless Sensor Networks’, International Journal of Intelligent Systems and Applications in Engineering.
[47] K. C. Shilpa, S. Chetan, and H. D. Anand, ‘Performance Analysis by Improving Energy Efficiency in IoT Based Wireless Sensor Network for Routing Algorithm’, INDJST, vol. 16, no. 11, pp. 785–794, Mar. 2023, doi: 10.17485/IJST/v16i11.2388.
[48] L. Jiang, X. Jin, Y. Xia, B. Ouyang, D. Wu, and X. Chen, ‘A Scale-Free Topology Construction Model for Wireless Sensor Networks’, International Journal of Distributed Sensor Networks, vol. 10, no. 8, p. 764698, Aug. 2014, doi: 10.1155/2014/764698.
[49] M. D. S. Mohamed, F. Patrick, and C. Ohta, ‘LPCHS: Linear programming based cluster head selection method in wireless sensor networks’, IEICE Communications Express, vol. 12, no. 9, pp. 511–516, 2023, doi: 10.1587/comex.2023XBL0073.
[50] X.-H. Hao, K.-V. Yuen, and S.-C. Kuok, ‘Energy-aware versatile wireless sensor network configuration for structural health monitoring’, Structural Control and Health Monitoring, vol. 29, no. 11, p. e3083, 2022, doi: 10.1002/stc.3083.
[51] J.-Y. Chang and P.-H. Ju, ‘An efficient cluster-based power saving scheme for wireless sensor networks’, EURASIP Journal on Wireless Communications and Networking, vol. 2012, no. 1, p. 172, May 2012, doi: 10.1186/1687-1499-2012-172.
[52] O. Ben Amor, Z. Chelly Dagdia, S. Bechikh, and L. Ben Said, ‘Many-objective optimization of wireless sensor network deployment’, Evol. Intel., vol. 17, no. 2, pp. 1047–1063, Apr. 2024, doi: 10.1007/s12065-022-00784-1.
[53] M. Tian, W. Jiao, J. Liu, and S. Ma, ‘A Charging Algorithm for the Wireless Rechargeable Sensor Network with Imperfect Charging Channel and Finite Energy Storage’, Sensors, vol. 19, no. 18, Art. no. 18, Jan. 2019, doi: 10.3390/s19183887.
[54] ‘Optimal data collection of multi‐radio multi‐channel multi‐power wireless sensor networks for structural monitoring applications: A simulation study - Chen - 2019 - Structural Control and Health Monitoring - Wiley Online Library’. Accessed: Dec. 19, 2024. [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1002/stc.2328
[55] ‘Energy Consumption Averaging and Minimization for the Software Defined Wireless Sensor Networks With Edge Computing | IEEE Journals & Magazine | IEEE Xplore’. Accessed: Dec. 19, 2024. [Online]. Available: https://ieeexplore-ieee-org.ezproxy.ugm.ac.id/document/8911459
[56] F. Zhang, H. Liu, Z. Ma, Y. Yang, and X. Wan, ‘Study of UAV Application in Wireless Sensor Networks’, in 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT), June 2020, pp. 343–348. doi: 10.1109/MECnIT48290.2020.9166681.
[57] F. Xu, H. Ye, F. Yang, and C. Zhao, ‘Software Defined Mission-Critical Wireless Sensor Network: Architecture and Edge Offloading Strategy’, IEEE Access, vol. 7, pp. 10383–10391, 2019, doi: 10.1109/ACCESS.2019.2890854.
[58] G. Li and X. Song, ‘Data Distribution Optimization Strategy in Wireless Sensor Networks With Edge Computing’, IEEE Access, vol. 8, pp. 214332–214345, 2020, doi: 10.1109/ACCESS.2020.3041356.
[59] J. Tang, A. Liu, M. Zhao, and T. Wang, ‘An Aggregate Signature Based Trust Routing for Data Gathering in Sensor Networks’, Security and Communication Networks, vol. 2018, no. 1, p. 6328504, 2018, doi: 10.1155/2018/6328504.
[60] S. Gnana Selvan, G. G. Jerith, C. Mahesh, S. Ravikumar, S. S. Shaffi, and S. Jagadeesh, ‘A Trust Based Energy Efficient Routing Design Based on Hybrid Particle Swarm Optimization (HPSO) for Wireless Sensor Networks’, EAI Endorsed Trans IoT, vol. 11, Sept. 2025, doi: 10.4108/eetiot.9427.
[61] S. Kumar and V. K. Chaurasiya, ‘A Strategy for Elimination of Data Redundancy in Internet of Things (IoT) Based Wireless Sensor Network (WSN)’, IEEE Systems Journal, vol. 13, no. 2, pp. 1650–1657, June 2019, doi: 10.1109/JSYST.2018.2873591.
[62] A. Mavromatis, C. Colman-Meixner, A. P. Silva, X. Vasilakos, R. Nejabati, and D. Simeonidou, ‘A Software-Defined IoT Device Management Framework for Edge and Cloud Computing’, IEEE Internet of Things Journal, vol. 7, no. 3, pp. 1718–1735, Mar. 2020, doi: 10.1109/JIOT.2019.2949629.
[63] X. Shang, H. Yin, Y. Wang, M. Li, and Y. Wang, ‘Secrecy Performance Analysis of Wireless Powered Sensor Networks Under Saturation Nonlinear Energy Harvesting and Activation Threshold’, Sensors, vol. 20, no. 6, Art. no. 6, Jan. 2020, doi: 10.3390/s20061632.
[64] ‘PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme - Khan - 2019 - International Journal of Communication Systems - Wiley Online Library’. Accessed: Dec. 19, 2024. [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1002/dac.4144
[65] M. S. ud Din, M. A. U. Rehman, R. Ullah, C.-W. Park, and B. S. Kim, ‘Towards Network Lifetime Enhancement of Resource Constrained IoT Devices in Heterogeneous Wireless Sensor Networks’, Sensors, vol. 20, no. 15, Art. no. 15, Jan. 2020, doi: 10.3390/s20154156.
[66] M. Elmonser, H. B. Chikha, and R. Attia, ‘An Energy Efficient WSN-Assisted IoT Network using Heterogeneous Dynamic Multi-hop Cluster-based Routing Protocol’, Mar. 28, 2023, Research Square. doi: 10.21203/rs.3.rs-2729716/v1.
[67] O. J. Aroba, N. Naicker, and T. Adeliyi, ‘A Hyper-Heuristic Heterogeneous Multisensor Node Scheme for Energy Efficiency in Larger Wireless Sensor Networks Using DEEC-Gaussian Algorithm’, Mobile Information Systems, vol. 2021, no. 1, p. 6658840, 2021, doi: 10.1155/2021/6658840.
[68] S. Sharmin, I. Ahmedy, and R. Md Noor, ‘An Energy-Efficient Data Aggregation Clustering Algorithm for Wireless Sensor Networks Using Hybrid PSO’, Energies, vol. 16, no. 5, p. 2487, Mar. 2023, doi: 10.3390/en16052487.
[69] J. A. I. S. Masood, M. Jeyaselvi, N. Senthamarai, S. Koteswari, M. Sathya, and N. S. K. Chakravarthy, ‘Privacy preservation in wireless sensor network using energy efficient multipath routing for healthcare data’, Measurement: Sensors, vol. 29, p. 100867, 2023, doi: https://doi.org/10.1016/j.measen.2023.100867.
[70] T. Luo, J. Xie, B. Zhang, Y. Zhang, C. Li, and J. Zhou, ‘An improved levy chaotic particle swarm optimization algorithm for energy-efficient cluster routing scheme in industrial wireless sensor networks’, Expert Systems with Applications, vol. 241, p. 122780, May 2024, doi: 10.1016/j.eswa.2023.122780.
[71] I. Ahmad, T. Hussain, B. Shah, A. Hussain, I. Ali, and F. Ali, ‘Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN’, Computers, Materials and Continua, vol. 79, no. 3, pp. 3585–3629, 2024, doi: https://doi.org/10.32604/cmc.2024.050596.
[72] H. Luo, J. Wang, D. Lin, L. Kong, Y. Zhao, and Y. L. Guan, ‘A Novel Energy-Efficient Approach Based on Clustering Using Gray Prediction in WSNs for IoT Infrastructures’, IEEE Internet Things J., vol. 11, no. 14, pp. 24748–24760, July 2024, doi: 10.1109/JIOT.2024.3379394.
[73] V. Prakash and S. Pandey, ‘Metaheuristic algorithm for energy efficient clustering scheme in wireless sensor networks’, Microprocessors and Microsystems, vol. 101, p. 104898, 2023, doi: https://doi.org/10.1016/j.micpro.2023.104898.
[74] M. Ahmed Hamza et al., ‘Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol’, Computer Systems Science and Engineering, vol. 45, no. 2, pp. 1759–1773, 2023, doi: 10.32604/csse.2023.030581.
[75] S. Sharmin, I. Ahmedy, R. M. Noor, and H. Ismail, ‘Clustering Mechanism in Particle Swarm Optimization Algorithm for Data Aggregation’, in 2023 IEEE 13th Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia: IEEE, May 2023, pp. 72–77. doi: 10.1109/ISCAIE57739.2023.10165458.
[76] A. M. B. Dr, ‘Bibliometric Analysis of Particle Swarm Optimization Techniques used to enhance Low-Energy Adaptive Clustering Hierarchy Protocol for Wireless Sensor Networks’.
[77] ‘An Optimized Energy Efficient Routing for Wireless Sensor Network using Improved Spider Monkey Optimization Algorithm’, IJIES, vol. 15, no. 1, Feb. 2022, doi: 10.22266/ijies2022.0228.18.
[78] G. Rajeswarappa and S. Vasundra, ‘Red Deer and Simulation Annealing Optimization Algorithm-Based Energy Efficient Clustering Protocol for Improved Lifetime Expectancy in Wireless Sensor Networks’, Wireless Pers Commun, vol. 121, no. 3, pp. 2029–2056, Dec. 2021, doi: 10.1007/s11277-021-08808-2.
[79] P. Maheshwari, A. K. Sharma, and K. Verma, ‘Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization’, Ad Hoc Networks, vol. 110, p. 102317, 2021, doi: https://doi.org/10.1016/j.adhoc.2020.102317.
[80] S. K. Chaurasiya, A. Biswas, and R. Banerjee, ‘Metaheuristic Multilevel Heterogeneous Clustering Technique for Heterogeneous Wireless Sensor Networks’, in 2021 10th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON), Jaipur, India: IEEE, Dec. 2021, pp. 1–6. doi: 10.1109/IEMECON53809.2021.9689098.
[81] C. Xu, Z. Xiong, G. Zhao, and S. Yu, ‘An Energy-Efficient Region Source Routing Protocol for Lifetime Maximization in WSN’, IEEE Access, vol. 7, pp. 135277–135289, 2019, doi: 10.1109/ACCESS.2019.2942321.
[82] G. Devika, D. Ramesh, and A. G. Karegowda, ‘An Energy Efficient Routing and Compression Based Data Collection Applying Bio-Inspired Ant-Cuckoo Technique for Wireless Sensor Network’, in 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), Bengaluru, India: IEEE, Dec. 2019, pp. 1–8. doi: 10.1109/CSITSS47250.2019.9031048.
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