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    A Novel UAV-Aided Network Architecture Using Wi-Fi Direct

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    A Novel UAV-Aided Network Architecture Using Wi-Fi Direct.pdf (4.454Mb)
    Date
    2019
    Author
    Khan, Muhammad Asif
    Hamila, Ridha
    Kiranyaz, Mustafa Serkan
    Gabbouj, Moncef
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    Abstract
    The use of unmanned aerial vehicles (UAVs) in future wireless networks is gaining attention due to their quick deployment without requiring the existing infrastructure. Earlier studies on UAV-aided communication consider generic scenarios, and very few studies exist on the evaluation of UAV-aided communication in practical networks. The existing studies also have several limitations, and hence, an extensive evaluation of the benefits of UAV communication in practical networks is needed. In this paper, we proposed a UAV-aided Wi-Fi Direct network architecture. In the proposed architecture, a UAV equipped with a Wi-Fi Direct group owner (GO) device, the so-called Soft-AP, is deployed in the network to serve a set of Wi-Fi stations. We propose to use a simpler yet efficient algorithm for the optimal placement of the UAV. The proposed algorithm dynamically places the UAV in the network to reduce the distance between the GO and client devices. The expected benefits of the proposed scheme are to maintain the connectivity of client devices to increase the overall network throughput and to improve energy efficiency. As a proof of concept, realistic simulations are performed in the NS-3 network simulator to validate the claimed benefits of the proposed scheme. The simulation results report major improvements of 23% in client association, 54% in network throughput, and 33% in energy consumption using single UAV relative to the case of stationary or randomly moving GO. Further improvements are achieved by increasing the number of UAVs in the network. To the best of our knowledge, no prior work exists on the evaluation of the UAV-aided Wi-Fi Direct networks.
    DOI/handle
    http://dx.doi.org/10.1109/ACCESS.2019.2916041
    http://hdl.handle.net/10576/15503
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    • Electrical Engineering [‎2822‎ items ]

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