Smart City applications, such as traffic monitoring and disaster response, often use swarms of intelligent and cooperative drones to efficiently collect sensor data over different areas of interest and time spans. However, when the required sensing becomes spatio-temporally large and varying, a collective arrangement of sensing tasks to a large number of battery-restricted and distributed drones is challenging. To address this problem, this paper introduces a scalable and energy-aware model for planning and coordination of spatio-temporal sensing. The coordination model is built upon a decentralized multi-agent collective learning algorithm (EPOS) to ensure scalability, resilience, and flexibility that existing approaches lack of. Experimental results illustrate the outstanding performance of the proposed method compared to state-of-the-art methods. Analytical results contribute a deeper understanding of how coordinated mobility of drones influences sensing performance. This novel coordination solution is applied to traffic monitoring using real-world data to demonstrate a $46.45\%$ more accurate and $2.88\%$ more efficient detection of vehicles as the number of drones become a scarce resource.

翻译：智慧城市应用（如交通监控与灾害响应）常采用智能协同无人机集群，在不同兴趣区域和时间跨度内高效采集传感器数据。然而，当所需感知任务在时空上呈现大规模动态变化时，如何将感知任务集体分配给大量受电池约束的分布式无人机便成为挑战。针对该问题，本文提出一种可扩展、能量感知的时空感知规划与协同模型。该协同模型基于分布式多智能体集体学习算法（EPOS）构建，以确保现有方法缺乏的可扩展性、弹性与灵活性。实验结果表明，所提方法相较现有最优方法具有显著优势。分析结果深入揭示了无人机协同移动对感知性能的影响机制。该新型协同方案被应用于基于真实数据的交通监控场景，在无人机资源稀缺时实现了车辆检测准确率提升46.45%，效率提升2.88%。