Joint Semantic Extraction and Resource Optimization in Communication-Efficient UAV Crowd Sensing

With the integration of IoT and 5G technologies, UAV crowd sensing has emerged as a promising solution to overcome the limitations of traditional Mobile Crowd Sensing (MCS) in terms of sensing coverage. As a result, UAV crowd sensing has been widely adopted across various domains. However, existing UAV crowd sensing methods often overlook the semantic information within sensing data, leading to low transmission efficiency. To address the challenges of semantic extraction and transmission optimization in UAV crowd sensing, this paper decomposes the problem into two sub-problems: semantic feature extraction and task-oriented sensing data transmission optimization. To tackle the semantic feature extraction problem, we propose a semantic communication module based on Multi-Scale Dilated Fusion Attention (MDFA), which aims to balance data compression, classification accuracy, and feature reconstruction under noisy channel conditions. For transmission optimization, we develop a reinforcement learning-based joint optimization strategy that effectively manages UAV mobility, bandwidth allocation, and semantic compression, thereby enhancing transmission efficiency and task performance. Extensive experiments conducted on real-world datasets and simulated environments demonstrate the effectiveness of the proposed method, showing significant improvements in communication efficiency and sensing performance under various conditions.