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Smart City applications, such as traffic monitoring and disaster response,often use swarms of intelligent and cooperative drones to efficiently collectsensor data over different areas of interest and time spans. However, when therequired sensing becomes spatio-temporally large and varying, a collectivearrangement of sensing tasks to a large number of battery-restricted anddistributed drones is challenging. To address this problem, this paperintroduces a scalable and energy-aware model for planning and coordination ofspatio-temporal sensing. The coordination model is built upon a decentralizedmulti-agent collective learning algorithm (EPOS) to ensure scalability,resilience, and flexibility that existing approaches lack of. Experimentalresults illustrate the outstanding performance of the proposed method comparedto state-of-the-art methods. Analytical results contribute a deeperunderstanding of how coordinated mobility of drones influences sensingperformance. This novel coordination solution is applied to traffic monitoringusing 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 scarceresource.

Coordination of Drones at Scale: Decentralized Energy-aware Swarm Intelligence for Spatio-temporal Sensing