mlsanityData to 7 Oct 2026

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

arXiv 2506.12263 ↗First posted 13 Jun 2025cs.LG cs.AI cs.SYcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 106 of its 217 references that are in the map. First posted on arXiv 13 Jun 2025.

Builds on

106 of 217 references.

References · 106

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50 most cited of 106 All 106 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflowscited by 57
  2. 2025RARR : Robust Real-World Activity Recognition with Vibration by Scavenging Near-Surface Audio Onlinecited by 0
  3. 2026Evaluating Visual Prompts with Eye-Tracking Data for MLLM-Based Human Activity Recognitioncited by 0
  4. 2026Agentic IoT: Architectures, Applications, and Challenges Toward the Internet of Agentscited by 0
  5. 2026Act2Intention: A Benchmark For Developing Active Mobile Agents Through Inferring User Intention from GUI Actionscited by 0