Synchronizing UAV Teams for Timely Data Collection and Energy Transfer by Deep Reinforcement Learning - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Vehicular Technology Année : 2022

Synchronizing UAV Teams for Timely Data Collection and Energy Transfer by Deep Reinforcement Learning

Résumé

Due to their promising applications and intriguing characteristics, Unmanned Aerial Vehicles (UAVs) can be dispatched as flying base stations to serve multiple energy-constrained Internet-of-Things (IoT) sensors. Moreover, to ensure fresh data collection while providing sustainable energy support to a large set of IoT devices, a required number of UAVs should be deployed to carry out these two tasks efficiently and promptly. Indeed, the data collection requires that UAVs first make Wireless Energy Transfer (WET) to supply IoT devices with the necessary energy in the downlink. Then, IoT devices perform Wireless Information Transmission (WIT) to UAVs in the uplink based on the harvested energy. However, it turns out that when the same UAV performs WIT and WET, its energy usage and the data collection time are severely penalized. Worse yet, it is difficult to efficiently coordinate between UAVs to improve the performance in terms of WET and WIT. This work proposes to divide UAVs into two teams to behave as data collectors and energy transmitters, respectively. A Multi-Agent Deep Reinforcement Learning (MADRL) method, called TEAM, is leveraged to jointly optimize both teams' trajectories, minimize the expected Age of Information (AoI), maximize the throughput of IoT devices, minimize the energy utilization of UAVs, and enhance the energy transfer. Simulation results depict that TEAM can effectively synchronize UAV teams and adapt their trajectories while serving a large-scale dynamic IoT environment.
Fichier principal
Vignette du fichier
IEEE_TVT_Double.pdf (4.14 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03819561 , version 1 (18-10-2022)

Identifiants

Citer

Omar Sami Oubbati, Mohammed Atiquzzaman, Hyotaek Lim, Abderrezak Rachedi, Abderrahmane Lakas. Synchronizing UAV Teams for Timely Data Collection and Energy Transfer by Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology, 2022, 71 (6), pp.6682-6697. ⟨10.1109/TVT.2022.3165227⟩. ⟨hal-03819561⟩
33 Consultations
152 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More