Energy-Aware Cluster Head Selection and Routing using Hyb-WhiOp and Op-MulDRL in IoT Sensor Networks
| International Journal of Electronics and Communication Engineering |
| © 2026 by SSRG - IJECE Journal |
| Volume 13 Issue 3 |
| Year of Publication : 2026 |
| Authors : R. Ratheesh, M. Saranya Nair, Blessina Preethi R, N.V.S.Sree Rathna Lakshmi |
10.14445/23488549/IJECE-V13I3P105
How to Cite?
R. Ratheesh, M. Saranya Nair, Blessina Preethi R, N.V.S.Sree Rathna Lakshmi, "Energy-Aware Cluster Head Selection and Routing using Hyb-WhiOp and Op-MulDRL in IoT Sensor Networks," SSRG International Journal of Electronics and Communication Engineering, vol. 13, no. 3, pp. 51-70, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I3P105
Abstract:
Wireless Sensor Networks (WSN) are a vital component of many IoT applications, enabling the efficient gathering and transmission of data. Current methods, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimization (GWO), and Improved Multi-Dimensional Energy-Aware Cluster-Based Routing (IMD-EACBR), often struggle with early CH depletion, inefficient routing, and poor adaptability. This paper introduces an energy-aware, trust-based framework for CH selection and routing, aimed at extending Network lifetime (NLT) and enhancing performance. In order to overcome these issues, a novel energy-efficient Optimized Multi-Objective Deep Reinforcement Learning (Op-MulDRL) for a routing mechanism is proposed. The designed framework incorporates a Hybrid White Whale Optimization (Hy-WhiOp) algorithm for CH selection, an Op-MulDRL model for routing. CH selection in WSN based on residual energy, trust, distance, latency, and path quality. On the other hand, Op-MulDRL significantly increases the power factor by learning and adapting the optimal routing paths that are more suitable to the network conditions without needing any further input from the controller. The Tent Chaos Rabbit Optimization (Ten-Rabo) helps to adjust the DRL parameters, thus further increasing the performance of Op-MulDRL. The new framework is verified through extensive simulations. It is found to outperform the best models, including PSO, GWO, Ant Lion Optimization (ALO), GA, Sunflower Optimization (SFO), and IMD-EACBR. The models achieved throughput of 0.522 Mbps, 0.891 Mbps, 0.565 Mbps, 0.728 Mbps, 0.632 Mbps, 0.934 Mbps, and 0.975Mbps across several performance metrics. In conclusion, the hybrid Hy-WhiOp, along with Op-MulDRL, contributes significant improvements in efficient energy, routing flexibility, and network resilience, which makes it an up-and-coming option for upcoming IoT-enabled WSN applications.
Keywords:
Wireless Sensor Network, CH selection, Routing, Deep Reinforcement Learning, Rabbit Optimization, and Tent Chaos.
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