梳理物联网基础模型的四大核心目标，助力跨场景应用选择。
A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis
- 按效率、上下文感知、安全、隐私与安全四类目标组织方法
- 总结各目标下常用技术与评估指标，支持跨领域对比
- 适合想在物联网中落地基础模型的研究者与工程师
基础模型因其对标注数据依赖低、任务泛化能力强，正受到物联网领域的广泛关注，有效缓解了传统机器学习方法的局限。然而，现有基于基础模型的方法多针对特定物联网任务，导致不同领域间难以比较，也缺乏对新任务应用的指导。本文旨在填补这一空白，通过围绕效率、上下文感知、安全及隐私与安全四个共享性能目标，系统梳理当前主流方法，并分析代表性工作、常用技术和评估指标。该目标导向的组织方式使跨领域比较成为可能，为新物联网任务中基础模型的选择与设计提供实践启示。最后，文章总结未来研究关键方向，为研究人员与从业者提供指引。
原文摘要 · Abstract (English)
Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed for specific IoT tasks, making it difficult to compare approaches across IoT domains and limiting guidance for applying them to new tasks. This survey aims to bridge this gap by providing a comprehensive overview of current methodologies and organizing them around four shared performance objectives by different domains: efficiency, context-awareness, safety, and security & privacy. For each objective, we review representative works, summarize commonly-used techniques and evaluation metrics. This objective-centric organization enables meaningful cross-domain comparisons and offers practical insights for selecting and designing foundation model based solutions for new IoT tasks. We conclude with key directions for future research to guide both practitioners and researchers in advancing the use of foundation models in IoT applications.
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