A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis

TL;DR

This survey reviews how foundation models are applied in IoT, organizing existing methods around key performance objectives to facilitate cross-domain comparison and guide future research and application.

Contribution

It provides a comprehensive, objective-centric taxonomy of foundation model applications in IoT, aiding in method comparison and guiding future research directions.

Findings

Organized existing IoT foundation model methods by efficiency, context-awareness, safety, security & privacy.

Summarized techniques and evaluation metrics for each objective.

Identified future research directions for foundation models in IoT.

Abstract

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.