POTMEC: A Novel Power Optimization Technique for Mobile Edge Computing Networks
The rapid growth of ultra-dense mobile edge computing (UDEC) in 5G IoT networks has intensified energy inefficiencies and latency bottlenecks exacerbated by dynamic channel conditions and imperfect CSI in real-world deployments. This paper introduces POTMEC, a power optimization framework that combi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/13/7/161 |
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| author | Tamilarasan Ananth Kumar Rajendirane Rajmohan Sunday Adeola Ajagbe Oluwatobi Akinlade Matthew Olusegun Adigun |
| author_facet | Tamilarasan Ananth Kumar Rajendirane Rajmohan Sunday Adeola Ajagbe Oluwatobi Akinlade Matthew Olusegun Adigun |
| author_sort | Tamilarasan Ananth Kumar |
| collection | DOAJ |
| description | The rapid growth of ultra-dense mobile edge computing (UDEC) in 5G IoT networks has intensified energy inefficiencies and latency bottlenecks exacerbated by dynamic channel conditions and imperfect CSI in real-world deployments. This paper introduces POTMEC, a power optimization framework that combines a channel-aware adaptive power allocator using real-time SNR measurements, a MATLAB-trained RL model for joint offloading decisions and a decaying step-size algorithm guaranteeing convergence. Computational offloading is a productive technique to overcome mobile battery life issues by processing a few parts of the mobile application on the cloud. It investigated how multi-access edge computing can reduce latency and energy usage. The experiments demonstrate that the proposed model reduces transmission energy consumption by 27.5% compared to baseline methods while maintaining the latency below 15 ms in ultra-dense scenarios. The simulation results confirm a 92% accuracy in near-optimal offloading decisions under dynamic channel conditions. This work advances sustainable edge computing by enabling energy-efficient IoT deployments in 5G ultra-dense networks without compromising QoS. |
| format | Article |
| id | doaj-art-fb7a2db9f76846e39d5371a98d1c578f |
| institution | Kabale University |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-fb7a2db9f76846e39d5371a98d1c578f2025-08-20T03:58:27ZengMDPI AGComputation2079-31972025-07-0113716110.3390/computation13070161POTMEC: A Novel Power Optimization Technique for Mobile Edge Computing NetworksTamilarasan Ananth Kumar0Rajendirane Rajmohan1Sunday Adeola Ajagbe2Oluwatobi Akinlade3Matthew Olusegun Adigun4Computer Science and Engineering, IFET College of Engineering, Gangarampalaiyam 605108, IndiaDepartment of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur 603203, IndiaDepartment of Computer Science, University of Zululand, Kwadlangezwa 3886, South AfricaDepartment of Computer Science, Birmingham City University, Birmingham B5 5JU, UKDepartment of Computer Science, University of Zululand, Kwadlangezwa 3886, South AfricaThe rapid growth of ultra-dense mobile edge computing (UDEC) in 5G IoT networks has intensified energy inefficiencies and latency bottlenecks exacerbated by dynamic channel conditions and imperfect CSI in real-world deployments. This paper introduces POTMEC, a power optimization framework that combines a channel-aware adaptive power allocator using real-time SNR measurements, a MATLAB-trained RL model for joint offloading decisions and a decaying step-size algorithm guaranteeing convergence. Computational offloading is a productive technique to overcome mobile battery life issues by processing a few parts of the mobile application on the cloud. It investigated how multi-access edge computing can reduce latency and energy usage. The experiments demonstrate that the proposed model reduces transmission energy consumption by 27.5% compared to baseline methods while maintaining the latency below 15 ms in ultra-dense scenarios. The simulation results confirm a 92% accuracy in near-optimal offloading decisions under dynamic channel conditions. This work advances sustainable edge computing by enabling energy-efficient IoT deployments in 5G ultra-dense networks without compromising QoS.https://www.mdpi.com/2079-3197/13/7/161mobile edge computingInternet of Thingstask offloadingultra-dense networkpower optimization |
| spellingShingle | Tamilarasan Ananth Kumar Rajendirane Rajmohan Sunday Adeola Ajagbe Oluwatobi Akinlade Matthew Olusegun Adigun POTMEC: A Novel Power Optimization Technique for Mobile Edge Computing Networks Computation mobile edge computing Internet of Things task offloading ultra-dense network power optimization |
| title | POTMEC: A Novel Power Optimization Technique for Mobile Edge Computing Networks |
| title_full | POTMEC: A Novel Power Optimization Technique for Mobile Edge Computing Networks |
| title_fullStr | POTMEC: A Novel Power Optimization Technique for Mobile Edge Computing Networks |
| title_full_unstemmed | POTMEC: A Novel Power Optimization Technique for Mobile Edge Computing Networks |
| title_short | POTMEC: A Novel Power Optimization Technique for Mobile Edge Computing Networks |
| title_sort | potmec a novel power optimization technique for mobile edge computing networks |
| topic | mobile edge computing Internet of Things task offloading ultra-dense network power optimization |
| url | https://www.mdpi.com/2079-3197/13/7/161 |
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