NEMESIS: No-Regret E-health User Experience in Multi-Access Edge Computing Systems
Published in IEEE International Conference on Communications, 2025
The rapid growth of data and computing needs in the Internet of Medical Things (IoMT) necessitates efficient mechanisms for optimizing the resource management in e-health applications. This paper presents the NEMESIS framework, which enables the users to determine their optimal Multi-Access Edge Computing (MEC) server selection and data offloading strategies by considering the reliability of the MEC servers based on individual interactions and shared user experiences. A comprehensive system model is introduced that defines the users’ interactions, the data offloading processes, and the impact of various IoMT devices, along with a novel utility function that evaluates the tradeoffs in the MEC server selection and task offloading. Additionally, a reliability model is proposed that in- corporates the direct user interactions and their peers evaluations of the MEC servers’ computing services, while a regret learning mechanism is designed to optimize the users’ strategies under varying information scenarios. The results demonstrate that the NEMESIS framework operates efficiently in real-time and outperforms state-of-the-art scheduling and offloading schemes in terms of latency and energy consumption.
Recommended citation: 2025, Aisha B Rahman, Odyssefs Diamantopoulos Pantaleon, E. E. Tsiropoulou, "NEMESIS: No-Regret E-health User Experience in Multi-Access Edge Computing Systems", IEEE ICC 2025, Accepted
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