• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Computer Science & Engineering
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Computer Science & Engineering
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Travel Hopping Enabled Resource Allocation (THEResA) and delay tolerant networking through the use of UAVs in railroad networks

    Thumbnail
    View/Open
    Publisher version (You have accessOpen AccessIcon)
    Publisher version (Check access options)
    Check access options
    1-s2.0-S1570870521001487-main.pdf (1.573Mb)
    Date
    2021-07-27
    Author
    Elias, Yaacoub
    Metadata
    Show full item record
    Abstract
    This paper investigates the use of unmanned aerial vehicles (UAVs)/drones for providing high data rates to mobile relays (MRs) placed on top of high speed train wagons, thus introducing the concept of Travel Hopping Enabled Resource Allocation (THEResA). The objective is to provide high data rate connectivity to train passengers in 5G+/6G networks. With the drone flying at the same train speed, highly directive beams can be formed and steered between the drone and each of the MRs. The simulation results in the paper show that this leads to high data rate connectivity, which will be reflected in the indoor links between the MRs and the train passengers inside the wagons. The drones maintain the connectivity to the cellular infrastructure by using high speed links with the cellular base stations (BSs) deployed along the rail track, e.g., through free space optics (FSO). The drones use the BS sites for recharging and resuming their operation. Therefore, a separate set of drones, or the same drones when they are not flying over trains, can be used to provide connectivity to remote rural areas. In fact, high speed trains might travel through rural areas with low population density. Although it is practical to lay fiber optic cables along the rail track, villages and small rural population agglomerations far from the railroad might not have access to the internet backhaul. UAVs can provide this connectivity in a delay tolerant fashion, by heading from the BSs/train station sites towards remote areas, collecting/transferring data from/to these areas, then returning to the sites along the rail track to recharge their batteries. This paper presents an analysis that shows the feasibility of this approach.
    URI
    https://www.sciencedirect.com/science/article/pii/S1570870521001487
    DOI/handle
    http://dx.doi.org/10.1016/j.adhoc.2021.102628
    http://hdl.handle.net/10576/59862
    Collections
    • Computer Science & Engineering [‎2428‎ items ]

    entitlement


    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    About QSpace

    Vision & Mission

    Help

    Item Submission Publisher policiesUser guides FAQs

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video