GIC-Net: AI-Driven Green IoT-CAN Bus Networks for Sustainable Energy Management with Optimization and Data Compression

The rapid expansion of Internet of Things (IoT) applications has significantly increased energy consumption and network complexity, so that it becomes very essential for the development of efficient and sustainable communication. This paper proposes GIC-Net, an Artificial Intelligence (AI)-driven Green IoT-CAN Bus network designed for sustainable energy management through intelligent optimization and data compression techniques. The proposed framework integrates a Green IoT network model with dynamic cluster-based architecture with cluster head selection, cluster member management, and sleep scheduling to enhance energy efficiency. Additionally, an IoT-CAN Bus communication layer is developed to enable reliable, low-latency data transmission, supported by optimized data collection and compression mechanisms to reduce bandwidth and storage overhead.To further improve system performance, AI-driven optimization algorithms are employed for adaptive decision-making, while a comprehensive energy model is formulated to evaluate energy consumption, residual energy, and system lifetime. The proposed approach is validated through extensive simulations, considering key performance metrics such as cluster head selection efficiency, number of alive nodes, energy consumption, delay, packet delivery ratio, and total system energy.