Semantic-driven Computation Offloading and Resource Allocation for UAV-assisted Monitoring System in Vehicular Networks.

In the vehicular networks monitoring scenario where the unmanned aerial vehicle (UAV) assists the vehicle data collection and the edge-cloud server cooperates to complete the visual intelligent task (e.g. object detection), a large amount of video data needs to be transmitted. For task-oriented communication systems, traditional resource allocation schemes mainly focus on the network performance or user experience of video transmission, without considering the impact of semantic content on task performance. In this paper, we propose a semantic-driven computation offloading and resource allocation scheme, namely semantic-driven CO&RA. In the offloading decision, we propose a convolutional neural network (CNN) segmentation scheme, which makes full use of computing resources and better completes intelligent tasks. At the same time, facing the complex resource allocation problem, we design a multi-agent deep Q-network (DQN) algorithm. Last, the experimental results show that our proposed scheme has more advantages in the optimization of multiple objectives of latency, energy consumption and task performance.