Unleashing the Potential of LLMs in Space-Based IoT Networks: Opportunities, Challenges, and Outlooks

作者：Zihan Ni, Yi Tao, Xuanhe Yang, Shuai Wang, Gaofeng Pan, Jianping An · 发表于：IEEE Internet of Things Magazine · 年份：2025 · DOI：10.1109/miot.2025.3583128 · 被引用次数：2 · 研究领域：IoT and Edge/Fog Computing、Satellite Communication Systems

The continuous evolution of next-generation mobile communication technologies has emerged space-based Internet of Things (IoT) as a promising communication system, leveraging satellite constellations to enable ubiquitous connectivity across space, air, ground, and sea. This paper explores the challenges faced by space-based IoT systems, including the integration of heterogeneous networks, variable communication channel conditions, and dynamic resource management. To address these issues, we propose the integration of large language models (LLMs), which offer unique capabilities in semantic understanding, multimodal data processing, and causal reasoning. These features enable LLMs to optimize resource allocation, manage complex networks, and enhance decision-making, improving the adaptability and efficiency of space-based IoT systems. The potential of LLMs to enable autonomous, coordinated communication across diverse devices and networks offers a transformative opportunity for space-based IoT systems. Future research will focus on enhancing the scalability and efficiency of LLMs to support large-scale space-based IoT networks and real-time decision-making, paving the way for more advanced and reliable connectivity across global and space environments.