Minimizing Communication Costs and Dropped Tasks via Dynamic Human Digital Twin Placement in MEC

With the advent of 6G networks, digital twin (DT) systems have become critical for real-time monitoring, decisionmaking, and control across various sectors. A digital twin is a virtual representation of a physical twin (PT), enabling continuous interaction and data exchange. However, real-time communication between the DT and PT incurs variable costs as users change locations, significantly affecting system efficiency and user experience. In mobile environments, minimizing these communication costs is essential for maintaining DT performance, as increased latency and resource demands arise with user mobility. Consequently, an optimal dynamic placement strategy for DTs on edge servers is crucial to reducing communication overhead while ensuring responsiveness. This work introduces an optimization framework leveraging Lyapunov optimization to model and minimize communication costs between the Human digital twin (HDT) and PT, considering task drops during twin migration. The proposed solution dynamically adapts to user movements and network conditions, ensuring efficient real-time interactions with minimal costs. Evaluation results demonstrate the effectiveness of our approach in reducing communication costs and task drops while maintaining data exchange quality and reliability in 6G-enabled DT systems.