NeuroCluster: Neural Networks for Intelligent Energy-Aware Clustering in IIoT

The advent of Industrial Internet of Things (IIoT) has ushered in a new era of connectivity, providing unprece-dented insights and control over industrial processes. However, the energy efficiency of IIoT networks remains a critical concern, particularly in clustered environments. This paper introduces “NeuroCluster,” a pioneering approach leveraging neural networks for intelligent and energy-aware clustering in IIoT networks. NeuroCluster harnesses the power of neural networks to dynamically adapt clustering strategies based on realtime energy consumption patterns and environmental fac-tors. By incorporating learning mechanisms, the proposed system intelligently allocates devices into clusters, considering their current energy states and predictive models of future energy demands. This adaptive clustering not only optimizes energy usage but also extends the overall network lifetime. The neural network architecture employed by NeuroCluster is designed to capture intricate relationships between device characteristics, contextual variables, and energy consumption profiles. Training the neural network involves learning from historical data and continuous updates, enabling the system to evolve and adapt to changing industrial environments. To validate the effectiveness of NeuroCluster, extensive simulations were conducted using representative IIoT scenarios. Results demonstrate significant improvements in energy efficiency, showcasing the ability of the system to outperform traditional clustering methods. Further-more, NeuroCluster exhibits robustness in diverse operational conditions, making it a scalable and adaptable solution for a wide range of IIoT applications.