ArticlePatterns (New York, N.Y.)2026
IoT-LLM: A framework for enhancing large language model reasoning from real-world sensor data.
Article in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- An On-Device Edge AI Agent for Reference-Free Self-Diagnosis of Low-Cost Multi-Pollutant Sensors.Sensors (Basel, Switzerland) · 2026Article
- A Privacy-Preserving Artificial Intelligence-Driven Sensing System for Distributed Multimodal Risk Detection.Sensors (Basel, Switzerland) · 2026Article
- LLM-Conductor: A Closed-Loop Resource-Adaptive Architecture for Secure LLM Deployment in Industrial Sensor Networks and IIoT Systems.Sensors (Basel, Switzerland) · 2026Article
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4 authors.
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Abstract
Large language models (LLMs) excel in textual tasks but often struggle with physical-world reasoning tasks. Inspired by human cognition-where perception is fundamental to reasoning-we explore augmenting LLMs with enhanced perception abilities using Internet of Things (IoT) data and pertinent knowledge. In this work, we systematically study LLMs' capability to address IoT-sensory tasks, by augmenting their perception and knowledge base, and then propose a unified framework, IoT-LLM, to enhance such capability. In IoT-LLM, we customize three steps: preprocessing IoT data into suitable formats, expanding LLMs' knowledge via IoT-oriented retrieval-augmented generation, and activating LLMs' commonsense knowledge through chain-of-thought prompting. We design a benchmark comprising five real-world tasks with varying data types and reasoning complexities to evaluate the performance of IoT-LLM. Experimental results reveal that IoT-LLM significantly improves the performance of IoT-sensory task reasoning of LLMs, with models such as GPT-4o-mini showing a 49.4% average improvement over previous methods.
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