ScholarWorks@중앙대학교

초록

Efficient task offloading to low-Earth orbit (LEO) satellite-assisted mobile edge computing (MEC) servers is vital for addressing the computational delay and energy constraints of remote Internet of Things (IoT) devices. However, inherent challenges in LEO satellite networks–including dynamic topology, intermittent connectivity, and bandwidth scarcity undermine the performance of conventional offloading schemes. Moreover, raw data transmission inflates latency and energy consumption, hindering real-time and resource-aware decision-making. To overcome these limitations, this paper presents a digital twin (DT)-driven, semantic-aware offloading framework tailored for LEO-integrated MEC systems. The proposed framework aims to minimize end-to-end DT synchronization delay while adhering to strict energy, semantic fidelity, and computational constraints. Semantic encoders extract task-relevant information to compress payloads, and a binary offloading strategy ensures each task is either fully executed locally or offloaded entirely. The problem is formulated as a joint optimization of offloading decisions, edge server selection, semantic compression ratio, and transmission power allocation. A proximal policy optimization (PPO)-based deep reinforcement learning algorithm is developed to solve the problem, leveraging real-time DT feedback for adaptive, context-aware control under dynamic network conditions. Simulation results demonstrate that the proposed framework reduces DT synchronization delay by up to 45 % vs. local execution, by 26–35 % vs. non-semantic offloading, and by 5–10 % vs. DRL alternative at high load, with statistically significant gaps. Additionally, the system maintains energy and semantic fidelity requirements while scaling efficiently with data volume and device density. These findings offer a practical and scalable solution for enabling reliable, low-latency DT services in 6G satellite–ground integrated IoT networks.

키워드