Dynamic IoT Protocol Adaptation for Smart Home Systems with Diverse Device Ecosystems

The swift proliferation of IoT devices in smart home ecosystems has resulted in notable interoperability issues due to the presence of numerous conflicting communication protocols, such as Zigbee, Z-Wave, MQTT, CoAP, Bluetooth, and Wi-Fi. Current solutions frequently depend on static protocol mapping, which proves inadequate for the dynamic nature of smart home environments where network conditions and device needs are in constant flux. In this paper, we introduce the Dynamic IoT Protocol Adaptation Framework (DIP A), which merges Reinforcement Learning (RL) with Multi-Armed Bandit (MAB) optimization to facilitate real-time, adaptive protocol selection. DIPA leverages edge computing for predictive decision-making and incorporates Graph Neural Networks (GNN) to assess protocol compatibility. A middleware-based protocol translation layer guarantees seamless integration across varied IoT systems. We assess the framework using a sandbox populated with actual IoT data to evaluate performance concerning adaptability, latency, energy consumption, and scalability. The findings indicate that DIP A diminishes latency by 35% and enhances energy efficiency by 28% when compared to static adaptation approaches. The proposed framework augments interoperability, optimizes resource management, and fosters flexible communication within smart home environments.