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

TL;DR

This paper presents a scalable, energy-aware decentralized coordination model for drone swarms to improve large-scale, spatio-temporal sensing in smart city applications, demonstrating significant accuracy and efficiency gains.

Contribution

It introduces a novel decentralized multi-agent learning algorithm for energy-efficient drone coordination, enhancing scalability, resilience, and sensing performance in large-scale scenarios.

Findings

Outperforms state-of-the-art methods in experiments

Achieves 46.45% more accurate vehicle detection

Improves detection efficiency by 2.88%

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.