A digital twin assisted and clustering based task offloading method for vehicle edge computing

Abstract:

To address the issues of task offloading instability, underutilization of computing resources, high offloading costs, and low user service quality in vehicle edge computing, which are caused by highly dynamic network topologies, high-dimensional action exploration spaces, and strict low-latency constraints, a digital twin-assisted clustering method for vehicle edge computing task offloading is proposed. Firstly, a vehicle social relationship model is constructed to quantify the relationships between vehicles. By introducing two social trust factors to measure offloading stability between vehicles, the method reduces the waste of computing resources caused by unstable communication links. Next, a digital twin-vehicle edge computing network model is constructed. Through the bidirectional information interaction between the digital twin network model and the physical vehicle edge device network, the condition of vehicle edge devices is monitored in real time. In addition, a clustering algorithm based on the gravity model is designed to assist vehicle clustering, thus narrowing the space for action exploration, improving the efficiency of computing task offloading, and reducing the cost of edge task computation. Finally, based on the above optimization strategies, a dual-latency deep deterministic policy gradient edge computing task offloading algorithm assisted by digital twin clustering is designed and implemented. Simulation experiments demonstrate that, compared to existing offloading methods, the proposed method significantly reduces task offloading costs, decreases task execution latency, and improves task offloading success rates while fully utilizing computing resources. This enables the efficient and stable offloading of vehicle-edge computing tasks, thereby enhancing user service quality.