Enhancing Indoor IoT Edge Intelligence With Deep Reinforcement Learning in Hybrid WiFi/LiFi Networks

The increasing demand for high-speed wireless connectivity in indoor environments has driven the development of hybrid wireless (Wi-Fi) and light fidelity (LiFi) networks. These systems combine the ubiquitous coverage of Wi-Fi with the high data rates of LiFi, providing an effective solution for dynamic indoor applications such as smart homes, offices, and industrial automation. As a critical component of edge intelligence, hybrid networks enable real-time data processing at the network edge, addressing the connectivity demands of indoor IoT systems. However, deploying these networks in dynamic indoor settings introduces challenges, including managing downlink transmission power, resource allocation, energy consumption, and access point selection amidst varying user mobility and interference. This study proposes a machine learning framework for enhancing edge intelligence, leveraging deep reinforcement learning with a proximal policy optimization algorithm (DRL-PPO) to optimize hybrid Wi-Fi/LiFi network performance in indoor environments. The proposed framework improves channel allocation and load balancing at the edge, ensuring energy efficiency while achieving high data throughput. We also introduce a comprehensive system model to manage dynamic traffic in indoor scenarios and develop resource allocation algorithms for efficient bandwidth distribution. Extensive simulations show that our approach, DRL-PPO, achieves up to an average of 15% higher throughput and 20% lower transmission power compared to baseline methods, demonstrating its potential for real-world deployment in indoor IoT scenarios.