{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T17:26:25Z","timestamp":1779125185817,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/computation13070161","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T08:58:23Z","timestamp":1751878703000},"page":"161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["POTMEC: A Novel Power Optimization Technique for Mobile Edge Computing Networks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0494-7803","authenticated-orcid":false,"given":"Tamilarasan Ananth","family":"Kumar","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, IFET College of Engineering, Gangarampalaiyam 605108, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5606-0913","authenticated-orcid":false,"given":"Rajendirane","family":"Rajmohan","sequence":"additional","affiliation":[{"name":"Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur 603203, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7010-5540","authenticated-orcid":false,"given":"Sunday Adeola","family":"Ajagbe","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Zululand, Kwadlangezwa 3886, South Africa"},{"name":"Department of Computer Engineering, Abiola Ajimobi Technical University, Ibadan 200255, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8569-9504","authenticated-orcid":false,"given":"Oluwatobi","family":"Akinlade","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Birmingham City University, Birmingham B5 5JU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6256-5865","authenticated-orcid":false,"given":"Matthew Olusegun","family":"Adigun","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Zululand, Kwadlangezwa 3886, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e13823","DOI":"10.1111\/exsy.13823","article-title":"Enhancing Security and Performance in Live VM Migration: A Machine Learning-Driven Framework with Selective Encryption for Enhanced Security and Performance in Cloud Computing Environments","volume":"42","author":"Haris","year":"2025","journal-title":"Expert Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11549","DOI":"10.1109\/TMC.2024.3401096","article-title":"Manipulating voice assistants eavesdropping via inherent vulnerability unveiling in mobile systems","volume":"23","author":"Huang","year":"2024","journal-title":"IEEE Trans. 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