Digital Twin Assisted Task Offloading for Workload Management at Fog Nodes

The convergence of urban informatics and vehicle intelligence has given rise to smart connected vehicles, which have immense potential as edge computing platforms for various applications. However, harnessing the full efficiency of these platforms presents challenges due to the diverse resource requirements, capabilities, and vehicle types, as well as unpredictable vehicle movements. To address these obstacles, a novel task offloading framework based on Digital Twin (DT) technology has been proposed for the Internet of Vehicles (IoV). This DT-based framework capitalizes on historical data and workload predictions to optimize the utilization of edge devices. It streamlines the offloading process by enabling tasks to be accepted and processed by the source vehicle without relying on external devices. The proposed system is designed to learn and forecast vehicle mobility patterns and computation waiting times, facilitating efficient allocation of computing resources at edge locations. Consequently, this approach enhances the quality of service by ensuring swift and effective task processing, irrespective of the vehicles’ unpredictable movements. The proposed approach is compared with a deep sequential model based on reinforcement learning, collaborative multiaccess edge computing (MEC), and energy-efficient MEC via reinforcement learning model. Our method demonstrates an improvement in task execution and overall offloading performance compared to these techniques during peak vehicle arrival rates. Likewise, substantial enhancements are observed in other benchmark parameters.