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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Divide-and-Conquer Bilevel Optimization Algorithm for Jointly Pricing Computing Resources and Energy in Wireless

Pei-Qiu Huang, Yong Wang, Kezhi Wang

    IEEE Transactions on Cybernetics
    |October 6, 2021
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    Summary

    This study introduces a new algorithm for wireless-powered mobile edge computing (MEC) systems. The divide-and-conquer bilevel optimization algorithm (DACBO) efficiently optimizes resource pricing and allocation for Internet-of-Things devices.

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    Area of Science:

    • Wireless communication
    • Mobile edge computing
    • Optimization algorithms

    Background:

    • Mobile edge computing (MEC) systems enable efficient task execution for Internet-of-Things (IoT) devices.
    • Optimizing resource allocation and pricing in wireless-powered MEC is complex due to interdependent decisions.

    Purpose of the Study:

    • To develop an efficient algorithm for jointly optimizing computing resource and energy prices in a wireless-powered MEC system.
    • To address the challenges posed by mixed integer and continuous variables in the bilevel optimization problem (BOP).

    Main Methods:

    • Formulated a bilevel optimization problem (BOP) for joint price and resource optimization.
    • Derived relationships to simplify the BOP by focusing on discrete mode selection variables.
    • Proposed a novel divide-and-conquer bilevel optimization algorithm (DACBO) to handle large search spaces.
    • Utilized analytical methods, nested bilevel optimization with differential evolution and variable neighborhood search (VNS), and a greedy initialization strategy within DACBO.

    Main Results:

    • The proposed DACBO algorithm effectively solves the transformed BOP by decomposing tasks into smaller sets.
    • DACBO demonstrates superior performance compared to other algorithms in optimizing wireless-powered MEC systems.
    • The algorithm efficiently manages computing resource and energy pricing and allocation.

    Conclusions:

    • DACBO provides an effective solution for optimizing wireless-powered MEC systems.
    • The proposed method offers a scalable and efficient approach to complex resource management problems in edge computing.
    • This research contributes to the advancement of efficient resource utilization in IoT environments.