Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation
This work addresses real-time computing needs for internet of vehicles applications, though it appears incremental as it builds on existing digital twin and reinforcement learning approaches.
The paper tackles the challenge of insufficient computing capability in vehicles by establishing a multi-task digital twin vehicular edge computing network to optimize task offloading and resource allocation, using a multi-agent reinforcement learning method that demonstrates effectiveness compared to benchmark algorithms.
With the increasing demand for multiple applications on internet of vehicles. It requires vehicles to carry out multiple computing tasks in real time. However, due to the insufficient computing capability of vehicles themselves, offloading tasks to vehicular edge computing (VEC) servers and allocating computing resources to tasks becomes a challenge. In this paper, a multi task digital twin (DT) VEC network is established. By using DT to develop offloading strategies and resource allocation strategies for multiple tasks of each vehicle in a single slot, an optimization problem is constructed. To solve it, we propose a multi-agent reinforcement learning method on the task offloading and resource allocation. Numerous experiments demonstrate that our method is effective compared to other benchmark algorithms.