Generative Sky: A Neurosymbolic Framework for In-Orbit Computation Offloading

Satellite offloading is a critical issue in the Internet of Things (IoT) edge intelligence environment. In this work, we present a novel neurosymbolic framework for computation offloading decisions in satellite-enabled IoT edge intelligence scenarios. By combining the forecasting capabilities of time-series foundation models with the transparency of rule-based reasoning, our approach enables data-efficient and inherently explainable decision making under uncertainty. Specifically, we use TimeGPT to predict future throughput quality and satellite CPU load, which are then processed through a fuzzy logic controller to derive context-aware offloading decisions with transparent rationale. The results show effective forecast accuracy, high decision robustness, and improved explainability when compared to traditional reinforcement learning-based approaches that require task-specific training.