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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm for Mobile Edge Computing Networks

Hend Bayoumi1, Nahla B Abdel-Hamid1, Amr M T Ali-Eldin1

  • 1Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.

Plos One
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an Energy-Harvesting Reinforcement Learning (EHRL) algorithm for Mobile Edge Computing (MEC). EHRL optimizes task offloading for mobile devices with limited energy, improving computation performance and time efficiency.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Mobile Devices (MDs) face limitations in energy and computational resources for task execution.
  • Wireless Power Transfer (WPT) and Energy Harvesting (EH) offer solutions for energy constraints in wireless networks.
  • Mobile Edge Computing (MEC) aims to enhance computing services by bringing resources closer to the network edge.

Purpose of the Study:

  • To optimize binary offloading decisions in wireless-powered MEC networks under dynamic channel and energy harvesting conditions.
  • To develop an efficient algorithm that addresses limited energy availability and maximizes computational performance for MDs.
  • To minimize task execution latency and enhance overall system efficiency in MEC environments.

Main Methods:

  • Proposed an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) integrating Reinforcement Learning (RL) and Deep Neural Networks (DNNs).
  • Employed a binary offloading policy where tasks are executed locally or fully offloaded to an Edge Server (ES).
  • Integrated the Newton-Raphson method for optimizing computation rate under energy constraints and utilized the Nadam optimizer for DNN training.

Main Results:

  • The EHRL algorithm dynamically optimizes binary offloading decisions without requiring manually labeled data or repeated complex optimizations.
  • Achieved significant gains in computation performance and time efficiency compared to conventional offloading techniques.
  • Demonstrated the viability of real-time and optimal offloading design even in fast-fading wireless environments.

Conclusions:

  • The proposed EHRL algorithm effectively addresses the challenges of limited energy and computational resources in wireless-powered MEC networks.
  • EHRL provides a robust and efficient solution for dynamic task offloading, enhancing user experience and system performance.
  • The integration of RL, DNNs, and optimization techniques offers a promising direction for future MEC research and development.