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Title	Coordination Of Drones At Scale: Decentralized Energy-Aware Swarm Intelligence For Spatio-Temporal Sensing
ID_Doc	16204
Authors	Qin C.; Pournaras E.
Year	2023
Published	Transportation Research Part C: Emerging Technologies, 157
DOI	http://dx.doi.org/10.1016/j.trc.2023.104387
Abstract	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. © 2023 The Author(s)
Author Keywords	Coordination; Drones; Smart city; Spatio-temporal sensing; Swarm intelligence; Unmanned aerial vehicles

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