Qin, C. orcid.org/0000-0002-6178-7973 and Pournaras, E. orcid.org/0000-0003-3900-2057 (2023) Coordination of drones at scale: Decentralized energy-aware swarm intelligence for spatio-temporal sensing. Transportation Research Part C: Emerging Technologies, 157. 104387. ISSN 0968-090X
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Coordination; Drones; Smart city; Spatio-temporal sensing; Swarm intelligence; Unmanned aerial vehicles |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number MRC (Medical Research Council) MR/W009560/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Jan 2024 12:04 |
Last Modified: | 17 Jan 2024 12:04 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.trc.2023.104387 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:207743 |