CECEHO-GCS: A New Green Energy-Efficient Clustering Protocol Based on Intelligent Optimization Theory in Industrial IoT

To address the issues of battery dependence, energy consumption, and energy management imbalance in industrial wireless sensor networks, this study proposes a green and energy-saving multi-objective clustering scheme to improve network efficiency and reduce environmental pollution. Given that conventional methods struggle to effectively optimize IWSN clustering, this paper specifically designs a novel multi-objective clustering model. During the optimization process, this model comprehensively considers four key performance indicators: total remaining energy, average network delay, average network packet loss rate, and average distance from cluster heads to the base station, achieving holistic optimization of network performance. To further enhance clustering efficiency and network stability, this paper also introduces a green energy-saving scheme based on the Chaotic Elite Clone Elephant Herding Optimization algorithm (i.e. CECEHO-GCS). This scheme ingeniously incorporates chaos operators in the initialization stage to enrich solution diversity and introduces clone and elite operators in the evolution stage, aiming to retain superior solutions and significantly enhance the algorithm's search capabilities. Through comparative experiments with four existing advanced clustering schemes: LEACH-C, LEACH-R, ESCVAD, and ARSH-FATI-CHS, the model and algorithm proposed in this paper, CECEHO-GCS, demonstrate significant advantages in improving network energy efficiency and service quality. Specifically, CECEHO-GCS has achieved an improvement of at least 19.27% in network lifespan and at least 16.89% in data throughput, opening up new avenues for green energy conservation and sustainable development in industrial wireless sensor networks.