Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm for Mobile Edge Computing Networks (EHRL)
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| Izdano u: | PLoS One vol. 20, no. 11 (Nov 2025), p. e0336903 |
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| Glavni autor: | |
| Daljnji autori: | , , |
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Public Library of Science
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| Online pristup: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0336903 |2 doi | |
| 035 | |a 3276035657 | ||
| 045 | 2 | |b d20251101 |b d20251130 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Bayoumi, Hend | |
| 245 | 1 | |a Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm for Mobile Edge Computing Networks (EHRL) | |
| 260 | |b Public Library of Science |c Nov 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Mobile Edge Computing (MEC) is a computational paradigm that brings resources closer to the network edge to provide fast and efficient computing services for Mobile Devices (MDs). However, MDs are often constrained by limited energy and computational resources, which are insufficient to handle the high number of tasks. The problems of limited energy resources and the low computing capability of wireless nodes have led to the emergence of Wireless Power Transfer (WPT) and Energy Harvesting (EH) as a potential solution where electrical energy is transmitted wirelessly and then harvested by MDs and converted into power. This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. Simultaneously, the DNN is trained using the Nadam optimizer (Nesterov-accelerated Adaptive Moment Estimation), which combines the benefits of Adam and Nesterov momentum, offering improved convergence speed and training stability. The proposed algorithm addresses the dual challenges of limited energy availability in MDs and the need for efficient task offloading to minimize latency and maximize computational performance. Numerical results validate the superiority of the proposed approach, demonstrating significant gains in computation performance and time efficiency compared to conventional techniques, making real-time and optimal offloading design truly viable even in a fast-fading environment. | |
| 653 | |a Energy resources | ||
| 653 | |a Energy harvesting | ||
| 653 | |a Deep learning | ||
| 653 | |a Energy sources | ||
| 653 | |a Algorithms | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Edge computing | ||
| 653 | |a Neural networks | ||
| 653 | |a Mobile computing | ||
| 653 | |a Newton-Raphson method | ||
| 653 | |a Computer applications | ||
| 653 | |a Batteries | ||
| 653 | |a Machine learning | ||
| 653 | |a Energy consumption | ||
| 653 | |a Internet of Things | ||
| 653 | |a Reinforcement | ||
| 653 | |a Wireless power transmission | ||
| 653 | |a Decision making | ||
| 653 | |a Network latency | ||
| 653 | |a Computation offloading | ||
| 653 | |a Remote computing | ||
| 653 | |a Learning | ||
| 653 | |a Latency | ||
| 653 | |a Real time | ||
| 653 | |a Constraints | ||
| 653 | |a Data transmission | ||
| 653 | |a Decisions | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Abdel-Hamid, Nahla B | |
| 700 | 1 | |a Amr M.T. Ali-Eldin | |
| 700 | 1 | |a Labib, Labib M | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 11 (Nov 2025), p. e0336903 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3276035657/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3276035657/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3276035657/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |