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Joint Deployment and Task Scheduling Optimization for Large-Scale Mobile Users in Multi-UAV-Enabled Mobile Edge

Yong Wang, Zhi-Yang Ru, Kezhi Wang

    IEEE Transactions on Cybernetics
    |September 13, 2019
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    Summary
    This summary is machine-generated.

    This study introduces a mobile edge computing system using multiple unmanned aerial vehicles (UAVs) to optimize user task execution and minimize energy consumption. The system efficiently determines the minimum number of UAVs and their optimal locations for task scheduling.

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    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Network Engineering

    Background:

    • Mobile edge computing (MEC) offers low latency and high bandwidth for mobile users.
    • Deploying unmanned aerial vehicles (UAVs) as flying edge clouds presents a novel approach to MEC.
    • Optimizing UAV deployment and task scheduling is crucial for efficient MEC system performance.

    Purpose of the Study:

    • To establish a multi-UAV-enabled MEC system for large-scale mobile users.
    • To minimize system energy consumption through joint optimization of UAV deployment and task scheduling.
    • To develop a two-layer optimization method for adaptive UAV deployment and efficient task allocation.

    Main Methods:

    • A two-layer optimization framework is proposed, integrating UAV deployment and task scheduling.
    • The upper layer utilizes a differential evolution algorithm with an elimination operator for adaptive UAV number and location optimization.
    • The lower layer employs an efficient greedy algorithm to solve the 0-1 integer programming problem for task scheduling.

    Main Results:

    • The study found that minimizing the number of UAVs is key to completing all tasks.
    • The proposed differential evolution algorithm adaptively adjusts the number of UAVs.
    • The greedy algorithm provides near-optimal task scheduling solutions efficiently for large-scale problems.
    • The system's effectiveness was validated on instances with up to 1000 users.

    Conclusions:

    • The developed multi-UAV-enabled MEC system effectively minimizes energy consumption.
    • The two-layer optimization method successfully balances UAV deployment and task scheduling.
    • This approach provides a scalable and efficient solution for mobile edge computing challenges.