Aqeel K. Kadhim; Haider K. Hoomod

A Comparative Benchmark of Evolutionary, Swarm, and RL Routing Protocols in Heterogeneous WSNs: Trade-offs and Deployment Guidelines

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Author(s)

Aqeel K. Kadhim ^1,* Haider K. Hoomod ²

1. Department of Computer Techniques Engineering, Imam Al Kadhum College University, Baghdad, 00964, Iraq

2. Department of Computer Science, College of Education, Al Mustanseria University, Baghdad, 00964, Iraq

* Corresponding author.

DOI: https://doi.org/10.5815/ijwmt.2026.05.18

Received: 12 Jun. 2026 / Revised: 9 Jul. 2026 / Accepted: 30 Aug. 2026 / Published: 8 Oct. 2026

Index Terms

Heterogeneous Wireless Sensor Networks (HWSNs), Binary Genetic Algorithm (BC-GA), Deep Q-Learning (DQL), Grey Wolf Optimization (GWO), Network Lifespan, Energy Efficiency, DEEC, PEGASIS

Abstract

Energy preservation remains a crucial challenge in the Heterogeneous Wireless Sensor Networks (HWSNs) deployment. The uneven distribution of primary energy leads to precocious node depletion and network segmentation. Five intelligent routing and clustering optimization strategies tailored for dynamic and heterogeneous environments are presented and comprehensively compared in this paper: the proposed Binary-Chromosome Genetic Algorithm (BC-GA), Grey Wolf Optimization (GWO), Deep Q-Learning (DQL), Distributed Energy-Efficient Clustering (DEEC) and Power-Efficient Gathering in Sensor Information Systems (PEGASIS). The simulations are performed on a 100×100 m² field with 50 randomly deployed nodes having different initial energy levels (0.507- 0.986 J) communicating with a Base Station. Empirical results obtained from five trial Monte Carlo simulations expose excellent performance trade-offs. BC-GA achieves an outstanding Last Node Dead (LND) of 5339.60 ± 157.57 rounds and maximum throughput of 166,219.40 ± 2534.72 packets. Among the classical baselines, PEGASIS achieves an LND of 3095.20 ± 212.68 rounds and throughput of 120,742.00. On the other hand, GWO and DQL have accelerated depletion phases with FND 432.40 ± 62.15 and 568.80 ± 48.30 rounds, respectively, due to extreme pressure on the bottleneck node near the Base Station. The End-to-End (E2E) delay provided by DEEC is the minimum (11.49 ± 0.36 ms) among all methods and the Energy-Delay Product (EDP) is the best (6.09 ± 0.38) which makes DEEC very suitable for latency-critical environments. Those results prove that the evolutionary cluster-head structuring provided top-level load balance and an extension of lifetime for intensive data monitoring.

Cite This Paper

Aqeel K. Kadhim, Haider K. Hoomod, "A Comparative Benchmark of Evolutionary, Swarm, and RL Routing Protocols in Heterogeneous WSNs: Trade-offs and Deployment Guidelines", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.5, pp. 299-315, 2026. DOI:10.5815/ijwmt.2026.05.18

Reference

[1]R. Preethi, R., & A. Karthikeyan. “Enhancing network lifetime and stability in IoT-based HWSNs for elderly care through optimized cluster head selection,”. IEEE SENNET August 2025, International Conference on Sensors and Related Networks, India. DOI: 10.1109/SENNET64220.2025.11135950
[2]I. Zaatouri, A.B. Guiloufi, N. Alyaoui, et al. “A Comparative Study of the Energy Efficient Clustering Protocols in Heterogeneous and Homogeneous Wireless Sensor Networks,”. Wireless Pers Commun 97, 6453–6468 (2017). https://doi.org/10.1007/s11277-017-4847-2
[3]B. M. Sahoo, H. M. Pandey & T. Amgoth, T. “A genetic algorithm inspired optimized cluster head selection method in wireless sensor networks,” Swarm and Evolutionary Computation, 75, 101151, December 2022. https://doi.org/10.1016/j.swevo.2022.101151
[4]S. Nebti & M. Redjimi. “Enhancing heterogeneous wireless sensor networks using swarm intelligence–based routing protocols,” International Journal of Electrical and Computer Engineering Systems, 14(1), 1–11. January 2023. DOI: https://doi.org/10.32985/ijeces.14.1.1
[5]B. Kim, J. H. Kong, T. J. Moore, & F. T. Dagefu. “Deep reinforcement learning based routing for heterogeneous multi-hop wireless networks”. arXiv. https://arxiv.org/pdf/2508.14884
[6]Fei, Z., Li, B., Yang, S., Xing, S., Chen, H., & Hanzo, L. “A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems,”. IEEE Communications Surveys & Tutorials, 19(1), 550-586, 2017. https://doi.org/10.1109/COMST.2016.2610578
[7]Shahraki, A., Taherkordi, A., Haugen, Ø., & Eliassen, F. “A survey and future directions on clustering: From WSNs to IoT and modern networking paradigms,”. IEEE Transactions on Network and Service Management, 18(2), 2242-2274, June 2021. DOI: 10.1109/TNSM.2020.3035315.
[8]S. Qi, Y. Li, S. Pasricha & R. G. Kim. MOELA: A multi-objective evolutionary/learning design space exploration framework for 3D heterogeneous manycore platforms. 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1–6, 2023. DOI: 10.23919/DATE56975.2023.10137276
[9]Y. Wang, K. Deng, Y. Ren, J. Chan, G. Tian and C. S. Jensen, "Reinforcement Learning for Tackling Energy-Saving and Energy-Balance Dilemma of Cluster-Based Routing Protocols in WSNs," in IEEE Access, vol. 12, pp. 148090-148110, 2024, DOI: 10.1109/ACCESS.2024.3468377
[10]W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-Efficient Communication Protocol for Wireless Microsensor Networks," Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, HI, USA, 2000, pp. 10 pp. vol.2-, https://doi.org/10.1109/HICSS.2000.926982
[11]S. Lindsey and C. S. Raghavendra, "PEGASIS: Power-Efficient Gathering in Sensor Information Systems," Proceedings, IEEE Aerospace Conference, Big Sky, MT, USA, 2002, pp. 3-3. https://doi.org/10.1109/AERO.2002.1035242
[12]M. N. Fauzan, R. Munadi, S. Sumaryo, & H. H. Nuha (2026). “Enhanced wireless sensor network lifetime using EGWO-optimized neural network approach,”. Network, 6(1), 5. January 2026. https://doi.org/10.3390/network6010005
[13]A. Hameed, T. M. Hasan, S. K. Shakir & Khalil, W. “Adaptive thresholding in TDEEC to achieve energy saving and long life of WSNs,”. Academic Science Journal, 4(2), 104–112. May 2026. DOI: 10.24237/04.02.875
[14]L. Qing, Q. Zhu, and M. Wang, "Design of a Distributed Energy-Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks," Computer Communications, vol. 29, no. 12, pp. 2230–2237, August 2006. https://doi.org/10.1016/j.comcom.2006.02.017
[15]S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, vol. 69, pp. 46–61, March 2014. https://doi.org/10.1016/j.advengsoft.2013.12.007
[16]S. Sennan, S. Sankar, R. Somula, D. Pandey, & Y. Cho, Y. “A multi-objective grey wolf optimization algorithm for energy-efficient cluster-based routing in IoT-enabled WSNs,”. Scientific Reports, 16, Article 179. December 2026. https://doi.org/10.1038/s41598-025-28950-2
[17]S. Hussain, A. Matin and O. Islam, “Genetic Algorithm for Hierarchical Wireless Sensor Network,” Journal of Networks, Vol. 2, No. 5, 2007, pp. 87-97. DOI: 10.4304/jnw.2.5.87-97
[18]V. Mnih et al., "Human-Level Control through Deep Reinforcement Learning," Nature, vol. 518, no. 7540, pp. 529–533, February 2015. https://doi.org/10.1038/nature14236
[19]D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989. ISBN: 978-0201157673
[20]A. N. Aledaily. “IQL‐OCDA: An intelligent Q‐learning‐based for optimal clustering and data‐aggregation for wireless sensor networks,”. Internet Technology Letters, 8(3), Article e572. 2025. https://doi.org/10.1002/itl2.572
[21]L. Guangjie, “QPSODRL: an improved quantum particle swarm optimization and deep reinforcement learning based intelligent clustering and routing protocol for wireless sensor networks,”. Sci Rep 16, 5526, February 2026. https://doi.org/10.1038/s41598-026-35365-0

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