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Article

Energy-Aware Management in Multi-UAV Deployments: Modelling and Strategies

1
IMDEA Networks Institute, Avda. del Mar Mediterráneo, 22, 28918 Madrid, Spain
2
Departamento de Ingeniería Telemática, Universidad Carlos III de Madrid, Avda. Universidad, 30, 28911 Leganés, Madrid, Spain
3
Department of Network Engineering, Universitat Politècnica de Catalunya, Calle Jordi Girona 1-3, E-08034 Barcelona, Spain
4
Escuela Politécnica Nacional, Ecuador, Avda. Ladrón de Guevara, E11-253 Quito, Ecuador
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2791; https://doi.org/10.3390/s20102791
Received: 30 March 2020 / Revised: 11 May 2020 / Accepted: 12 May 2020 / Published: 14 May 2020
(This article belongs to the Special Issue Optimization and Communication in UAV Networks)
Nowadays, Unmanned Aerial Vehicles (UAV) are frequently present in the civilian environment. However, proper implementations of different solutions based on these aircraft still face important challenges. This article deals with multi-UAV systems, forming aerial networks, mainly employed to provide Internet connectivity and different network services to ground users. However, the mission duration (hours) is longer than the limited UAVs’ battery life-time (minutes). This paper introduces the UAV replacement procedure as a way to guarantee ground users’ connectivity over time. This article also formulates the practical UAV replacements problem in moderately large multi-UAV swarms and proves it to be an NP-hard problem in which an optimal solution has exponential complexity. In this regard, the main objective of this article is to evaluate the suitability of heuristic approaches for different scenarios. This paper proposes betweenness centrality heuristic algorithm (BETA), a graph theory-based heuristic algorithm. BETA not only generates solutions close to the optimal (even with 99% similarity to the exact result) but also improves two ground-truth solutions, especially in low-resource scenarios. View Full-Text
Keywords: UAV; UAV fleet; UAV swarm; energy consumption; self-organization; algorithms; optimization; UAV replacement UAV; UAV fleet; UAV swarm; energy consumption; self-organization; algorithms; optimization; UAV replacement
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MDPI and ACS Style

Sanchez-Aguero, V.; Valera, F.; Vidal, I.; Tipantuña, C.; Hesselbach, X. Energy-Aware Management in Multi-UAV Deployments: Modelling and Strategies. Sensors 2020, 20, 2791. https://doi.org/10.3390/s20102791

AMA Style

Sanchez-Aguero V, Valera F, Vidal I, Tipantuña C, Hesselbach X. Energy-Aware Management in Multi-UAV Deployments: Modelling and Strategies. Sensors. 2020; 20(10):2791. https://doi.org/10.3390/s20102791

Chicago/Turabian Style

Sanchez-Aguero, Victor, Francisco Valera, Ivan Vidal, Christian Tipantuña, and Xavier Hesselbach. 2020. "Energy-Aware Management in Multi-UAV Deployments: Modelling and Strategies" Sensors 20, no. 10: 2791. https://doi.org/10.3390/s20102791

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