Digital Twin Empowered Task Offloading for Mobile-Edge Computing in 6G Internet of Vehicles

The rapid development of the Internet of Vehicles (IoV) and sixth-generation (6G) technology has made traditional cloud computing architectures inadequate for vehicular networks. With ultra-low latency, high bandwidth, and massive connectivity, 6G networks provide essential support for task offloading in mobile edge computing. Task offloading transfers computational tasks from vehicles to edge base stations or cloud servers. However, traditional methods struggle to adapt to diverse user tasks and dynamic network environments. These limitations increase offloading delays. Digital twin (DT) offers real-time simulation to reflect system dynamics and address these challenges. In this paper, we propose a task offloading framework that combines DTs with deep reinforcement learning. We propose a multi-layer dynamic framework with an error-reward feedback mechanism to handle complexity, dynamic changes, and errors. This approach enables the conclusion of task offloading strategies that minimize latency. The framework includes a threshold-based warning mechanism to effectively manage edge base station loads. By monitoring load in real time, the system evaluates base station status and adjusts task allocation to maintain stability. We propose a multi-dimensional state encoder architecture, comprising environment encoders, base station state encoders, and task state encoders to effectively extract critical features. The proposed architecture enables the conclusion of task offloading strategies that minimize latency. Experimental results demonstrate that the proposed algorithm can accurately and quickly conclude the task offloading strategy to minimize energy consumption and latency.