• Login
    View Item 
    •   Home
    • Brock Theses
    • Masters Theses
    • M.Sc. Computer Science
    • View Item
    •   Home
    • Brock Theses
    • Masters Theses
    • M.Sc. Computer Science
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of BrockUCommunitiesPublication DateAuthorsTitlesSubjectsThis CollectionPublication DateAuthorsTitlesSubjectsProfilesView

    My Account

    LoginRegister

    Statistics

    Display statistics

    Adaptive Q-learning-supported Resource Allocation Model in Vehicular Fogs

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Brock_Hossain_Md_Tahmid_2021.pdf
    Size:
    4.319Mb
    Format:
    PDF
    Download
    Author
    Hossain, Md Tahmid
    Keyword
    Vehicular Fog Computing
    Resource Allocation
    Cloudlet Dwell Time
    Reinforcement Learning
    Q-Learning
    
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/10464/15431
    Abstract
    Urban computing has become a significant driver in supporting the delivery and sharing of services, being a strong ally to intelligent transportation. Smart vehicles present computing and communication capabilities that allow them to enable many autonomous vehicular safety and infotainment applications. Vehicular Cloud Computing (VCC) has already proven to be a technology shifting paradigm harnessing the computation resources from on board units from vehicles to form clustered computing units to solve real world computing problems. However, with the rise of vehicular application use and intermittent network conditions, VCC exhibits many drawbacks. Vehicular Fog computing appears as a new paradigm in enabling and facilitating efficient service and resource sharing in urban environments. Several vehicular resource management works have attempted to deal with the highly dynamic vehicular environment following diverse approaches, e.g. MDP, SMDP, and policy-based greedy techniques. However, the high vehicular mobility causes several challenges compromising consistency, efficiency, and quality of service. RL-enabled adaptive vehicular Fogs can deal with the mobility for properly distributing load and resources over Fogs. Thus, we propose a mobility-based cloudlet dwell time estimation method for accurately estimating vehicular resources in a Fog. Leveraging the CDT estimation model, we devise an adaptive and highly dynamic resource allocation model using mathematical formula for Fog selection, and reinforcement learning for iterative review and feedback mechanism for generating optimal resource allocation policy.
    Collections
    M.Sc. Computer Science

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.