• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
    • QSpace policies
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.

    Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization

    Thumbnail
    Date
    2022
    Author
    Jouhari M.
    Al-Ali A.K.
    Baccour E.
    Mohamed A.
    Erbad A.
    Guizani M.
    Hamdi M.
    ...show more authors ...show less authors
    Metadata
    Show full item record
    Abstract
    Unmanned aerial vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors. Furthermore, thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. However, deep neural networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed onboard. In this article, we propose a DNN distribution methodology within UAVs to enable data classification in resource-constrained devices and avoid extra delays introduced by the server-based solutions due to data communication over air-to-ground links. The proposed method is formulated as an optimization problem that aims to minimize the latency between data collection and decision-making while considering the mobility model and the resource constraints of the UAVs as part of the air-to-air communication. We also introduce the mobility prediction to adapt our system to the dynamics of UAVs and the network variation. The simulation conducted to evaluate the performance and benchmark the proposed methods, namely, optimal UAV-based layer distribution (OULD) and OULD with mobility prediction (OULD-MP), was run in an HPC cluster. The obtained results show that our optimization solution outperforms the existing and heuristic-based approaches. 2014 IEEE.
    DOI/handle
    http://dx.doi.org/10.1109/JIOT.2021.3079164
    http://hdl.handle.net/10576/30046
    Collections
    • Computer Science & Engineering [‎1930‎ items ]

    entitlement

    Related items

    Showing items related by title, author, creator and subject.

    • Thumbnail

      An improved whale optimization algorithm for solving multi-objective design optimization problem of PFHE 

      Sulaiman, Muhammada; Samiullah, Ismata; Hamdi, A.b; Hussain, Zubaira ( IOS Press , 2019 , Article)
      In this paper, we have used a novel initialization strategy to improve Whale optimization algorithm (WOA), which is named as The Improved Whale Optimization Algorithm (IWOA). To evaluate the capability of the algorithm in ...
    • Thumbnail

      Optimizing cloud-service performance: Efficient resource provisioning via optimal workload allocation 

      Wang, Zhuoyao; Hayat, Majeed M.; Ghani, Nasir; Shaban, Khaled B. ( IEEE Computer Society , 2017 , Article)
      Cloud computing is being widely accepted and utilized in the business world. From the perspective of businesses utilizing the cloud, it is critical to meet their customers' requirements by achieving service-level-objectives. ...
    • Thumbnail

      On the optimal computing budget allocation problem for large scale simulation optimization 

      Al-Salem, Mohammed; Almomani, Mohammad; Alrefaei, Mahmoud; Diabatd, Ali ( Elsevier B.V. , 2017 , Article)
      Selecting a set that contains the best simulated systems is an important area of research. When the number of alternative systems is large, then it becomes impossible to simulate all alternatives, so one needs to relax the ...

    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 QSpace policies

    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