Towards Green IoT: Energy Conservation in Core IoT Network Using Network Coding and Soft Actor Critic

The rapid expansion of internet of things (IoT) networks has intensified energy efficiency challenges in core IoT networks, where data routing places high demands on node energy. This paper proposes a hybrid model integrating network coding (NC) with soft actor-critic (SAC) reinforcement learning to address these challenges. NC minimizes redundant transmissions by combining packets, while SAC agents dynamically select energy-aware routing paths based on node state, link quality, and residual energy. The system was implemented using the NS-3 simulator with a 5 × 5 grid topology of IoT nodes (512-byte packets, 100s runtime) under varying traffic conditions. Results demonstrate that NC + SAC achieves a 40% reduction in energy consumption, a 97% packet delivery ratio, 50% improvement in throughput, and an extended network lifetime compared to traditional routing and standalone approaches. Key contributions include novel NC-SAC integration, comprehensive NS-3 validation across multiple metrics, and demonstration of learning-driven coded communication for sustainable IoT infrastructures. The hybrid approach balances reliability, energy preservation, and performance, offering a scalable framework for next-generation green IoT networks.