With multi-sensors embedded, flexible unmanned aerial vehicles (UAVs) can collect sensory data and provide various services for all walks of life. However, limited computing capability and battery energy put a great burden on UAVs to handle emerging compute-intensive applications, necessitating them to resort to innovative computation offloading technique to guarantee quality of service. Existing research mainly focuses on solving the offloading problem under known global information, or applying centralized offloading frameworks when facing dynamic environments. Yet, the maneuverability of today’s UAVs, their large-scale clustering, and their increasing operation in the environment with unrevealed information pose huge challenges to previous work. In this paper, in order to enhance the long-term offloading performance and scalability for multi-UAVs, we develop a decentralized offloading scheme named DELOFF with the support of mobile edge computing (MEC). DELOFF considers the information uncertainty caused by the dynamic environment, uses UAV-to-everything (U2X)-assisted heterogeneous networks to extend network resources and offloading flexibility, and tackles the joint strategy making related to computation mode, network selection, and offloading allocation for multi-UAVs. Specifically, the optimization problem of multi-UAVs is addressed by the proposed offloading algorithm based on a multi-arm bandit learning model, where each UAV itself can adaptively assess the offloading link quality through the fuzzy logic-based pre-screening mechanism designed. The convergence and effectiveness of the DELOFF proposed are also demonstrated in simulations. And, the results confirm that DELOFF is superior to the four benchmarks in many respects, such as reduced consumed energy and delay in the task completion of UAVs.


    Access

    Download


    Export, share and cite



    Title :

    DELOFF: Decentralized Learning-Based Task Offloading for Multi-UAVs in U2X-Assisted Heterogeneous Networks


    Contributors:
    Anqi Zhu (author) / Huimin Lu (author) / Mingfang Ma (author) / Zongtan Zhou (author) / Zhiwen Zeng (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Public Bus-Assisted Task Offloading for UAVs

    Park, JiHyun / Choi, Sieun / Kim, TaeYoung et al. | IEEE | 2024


    Learning-Aided Online Task Offloading for UAVs-Aided IoT Systems

    Zhu, Junge / Huang, Xi / Tang, Yinxu et al. | IEEE | 2019


    Robust Decentralized Task Assignment for Cooperative UAVs

    Alighanbari, Mehdi / How, Jonathan | AIAA | 2006


    Distributed access and offloading scheme for multiple UAVs assisted MEC networks

    He, Saifei / Cheng, Ming / Pan, Yijin et al. | IEEE | 2023


    Decentralized task allocation for multiple UAVs with task execution uncertainties *

    Liu, Ruifan / Seo, Minguk / Yan, Binbin et al. | IEEE | 2020