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Dispatch of UAVs for Urban Vehicular Networks: A Deep Reinforcement Learning Approach

Abstract : Due to the dynamic nature of connectivity in terrestrial vehicular networks, it is of great benefit to deploy unmanned aerial vehicles (UAVs) in these networks to act as relays. As a result, a remarkable number of studies have exploited UAVs to bridge the communication gaps between terrestrial vehicles, and sometimes despite their unoptimized mobility, their restricted communication coverage, and their limited energy resources. However, it was noted that for an intermittently connected vehicular network, UAVs could not cover all sparse areas all the time. Even worse, when deploying enough UAVs to cover all these areas, the probability of inter-UAV collisions increases, and it will be complex to control their movements efficiently. Consequently, it is required to dispatch an organized and intelligent group of UAVs to perform communication relays in the long term while keeping their connectivity, minimizing their average energy consumption, and providing an efficient coverage strategy. To meet these requirements, we propose a deep reinforcement learning (DRL) framework, called DISCOUNT (Dispatch of UAVs for Urban VANETs). Extensive simulations have been conducted to evaluate the performance of the proposed framework. It has been shown that the proposed framework significantly outperforms two commonly-used baseline techniques and some reinforcement learning methods in terms of energy consumption, coverage, and routing performances.
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Contributor : Omar sami Oubbati Connect in order to contact the contributor
Submitted on : Wednesday, October 13, 2021 - 2:23:23 PM
Last modification on : Saturday, October 16, 2021 - 3:38:50 AM
Long-term archiving on: : Friday, January 14, 2022 - 7:09:58 PM


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Omar Sami Oubbati, M Atiquzzaman, A Baz, H Alhakami, J Ben-Othman. Dispatch of UAVs for Urban Vehicular Networks: A Deep Reinforcement Learning Approach. IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers, 2021, ⟨10.1109/TVT.2021.3119070⟩. ⟨hal-03376381⟩



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