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, we introduce 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:智能城市应用,如交通监测和灾害应对,常常使用大批智能和合作型无人驾驶飞机来有效收集不同感兴趣领域和时间跨度的传感器数据;然而,当所需的遥感变得时时空大而有差异时,集体安排对大量受电池限制和分布式无人驾驶飞机的遥感任务具有挑战性;为解决这一问题,我们引入了一个可扩缩和能见度模型,用以规划和协调时空感测;协调模式建立在分散的多试剂集体学习算法(EPOS)上,以确保现有方法缺乏的可伸缩性、复原力和灵活性;实验结果表明,与最新方法相比,拟议方法的出色性能;分析结果有助于更深入了解无人驾驶飞机协调机动性如何影响遥感性能;这一新的协调解决办法用于利用现实世界数据进行交通监测,以显示对车辆的检测更加准确和2.88美元,因为无人驾驶飞机的数量已成为稀缺的资源。