Digital Twin-Assisted Contextual Bandit Learning for Peer Offloading in Fog-Based IoT Networks

This paper presents a novel approach, termed Digital Twin-assisted Contextual Bandit Learning (DT-CBL), designed to optimize peer offloading in fog-based IoT networks. The proposed framework leverages digital twins to create virtual representations of physical IoT devices and their environments, enabling real-time monitoring and analysis of network conditions and device states. By integrating contextual bandit learning, DT-CBL dynamically adapts offloading strategies based on contextual information, such as device capabilities, network conditions, and task characteristics, to make intelligent offloading decisions. This approach aims to minimize task completion latency and improve overall system performance for delay-sensitive applications. We provide a comprehensive analysis of the DT-CBL framework, detailing its theoretical foundations and algorithmic implementation. Extensive simulations demonstrate the effectiveness of DT-CBL in reducing offloading delays and enhancing resource utilization compared to traditional offloading techniques.