Awoyemi, Babatunde SeunMaharaj, Bodhaswar T. Sunil2025-05-072025-05-072025-01Awoyemi, B.S. & Maharaj, B.T. 2025, 'Adaptive power management for multiaccess edge computing-based 6G-inspired massive Internet of Things', IET Wireless Sensor Systems, vol. 15, no. 1, art. e70000, pp. 1-13, doi : 10.1049/wss2.70000.2043-6386 (print)2043-6394 (online)10.1049/wss2.70000http://hdl.handle.net/2263/102314DATA AVAILABILITY STATEMENT : Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.Multiaccess edge computing (MEC) is a dynamic approach for addressing the capacity and ultra-latency demands caused by the pervasive growth of real-time applications in next-generation (xG) wireless communication networks. Powerful computational resource-enriched virtual machines (VMs) are used in MEC to provide outstanding solutions. However, a major challenge with using VMs in xG networks is the high overhead caused by the excessive energy demands of VMs. To address this challenge, containers, which are generally more energy-efficient and less computationally demanding, are being advocated. This paper proposes a containerised edge computing model for power optimisation in 6G-inspired massive Internet-of-Things applications. The problem is formulated as a central processing unit energy consumption cost function based on quasi-finite system observations. To achieve practicable computational complexity, an approach that uses a search heuristic based on Lyapunov techniques is employed to obtain near-optimal solutions. Important performance metrics are successfully predicted using the online look-ahead technique. The predictive model used achieves an accuracy of 97% prediction compared to actual data. To further improve resource demand, an adaptive controller is used to schedule computational resources on a time slot basis in an adaptive manner while continuing to receive workload levels to plan future resource provisioning. The proposed technique is shown to perform better compared to a competitive baseline algorithm.en© 2025 The Author(s). IET Wireless Sensor Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License.Multiaccess edge computing (MECNext-generation wireless sensor networks (xWSN)Cloud computingInternet of Things (IoT)Learning (artificial intelligence)Massive IoTOptimisationReliabilityArticle