EcoEdgeTwin: Driving 6G With AI‐Enhanced Edge Integration and Sustainable Digital Twins
Synthia Hossain Karobi, Shakil Ahmed, Saifur Rahman Sabuj, Ashfaq Khokhar · Digital twins and applications. · 2025
ABSTRACT Integrating mobile edge computing (MEC) and digital twin (DT) technologies to enhance network performance through predictive, adaptive control for energy‐efficient and low‐latency communication is a significant challenge. This paper presents the EcoEdgeTwin model, an innovative framework that harnesses the harmony between MEC and DT technologies to ensure an efficient network operation. We optimise the utility function to balance enhancing users' quality of experience (QoE) and minimising latency and energy consumption at edge servers. This approach ensures efficient and adaptable network operations, utilising DT to synchronise and integrate real‐time data seamlessly. Our framework implements robust mechanisms for task offloading, service caching and cost‐effective service migration. Additionally, it manages energy consumption related to task processing, communication and the influence of DT predictions, all essential for optimising latency and minimising energy usage. Through the utility model, we also prioritise QoE, fostering a user‐centric approach to network management that balances network efficiency with user satisfaction. A cornerstone of our approach is integrating the advantage actor‐critic algorithm, marking a pioneering use of deep reinforcement learning for dynamic network management. This strategy addresses challenges in service mobility and network variability, ensuring optimal network performance matrices. Our extensive simulations demonstrate that compared to benchmark models, the EcoEdgeTwin framework significantly reduces energy usage and latency while enhancing QoE.