# When IoT Meet LLMs: Applications and Challenges

arXiv: https://arxiv.org/abs/2411.17722 · HTML: https://mlsanity.com/paper/2411.17722/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 41 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better](https://mlsanity.com/paper/2106.08962/) (2021)
- [Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models](https://mlsanity.com/paper/2106.13353/) (2021)
- [FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model](https://mlsanity.com/paper/2406.17706/) (2024)
- [Penetrative AI: Making LLMs Comprehend the Physical World](https://mlsanity.com/paper/2310.09605/) (2023)
- [FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models](https://mlsanity.com/paper/2310.10049/) (2023)
- [EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting](https://mlsanity.com/paper/2406.15758/) (2024)
- [LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution](https://mlsanity.com/paper/2312.09007/) (2023)
- [PerLLM: Personalized Inference Scheduling with Edge-Cloud Collaboration for Diverse LLM Services](https://mlsanity.com/paper/2405.14636/) (2024)
- [Context Aware Computing for The Internet of Things: A Survey](https://mlsanity.com/paper/1305.0982/) (2013)
- [IoT-LLM: a framework for enhancing Large Language Model reasoning from real-world sensor data](https://mlsanity.com/paper/2410.02429/) (2024)
- [IoT-LM: Large Multisensory Language Models for the Internet of Things](https://mlsanity.com/paper/2407.09801/) (2024)
- [Efficient Prompting for LLM-based Generative Internet of Things](https://mlsanity.com/paper/2406.10382/) (2024)
- [Safely Learning with Private Data: A Federated Learning Framework for Large Language Model](https://mlsanity.com/paper/2406.14898/) (2024)
- [PLLM-CS: Pre-trained Large Language Model (LLM) for Cyber Threat Detection in Satellite Networks](https://mlsanity.com/paper/2405.05469/) (2024)
- [POLCA: Power Oversubscription in LLM Cloud Providers](https://mlsanity.com/paper/2308.12908/) (2023)
- [Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey](https://mlsanity.com/paper/2206.04800/) (2022)
- [DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model](https://mlsanity.com/paper/2404.05182/) (2024)
- [Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model](https://mlsanity.com/paper/2409.19390/) (2024)
- [Multi-Modal Transformer and Reinforcement Learning-based Beam Management](https://mlsanity.com/paper/2410.19859/) (2024)
- [Resource Allocation for Stable LLM Training in Mobile Edge Computing](https://mlsanity.com/paper/2409.20247/) (2024)
- [Dynamic Fog Computing for Enhanced LLM Execution in Medical Applications](https://mlsanity.com/paper/2408.04680/) (2024)
- [Internet of Nano, Bio-Nano, Biodegradable and Ingestible Things: A Survey](https://mlsanity.com/paper/2202.12409/) (2022)
- [Cyber Knowledge Completion Using Large Language Models](https://mlsanity.com/paper/2409.16176/) (2024)

## Built on by (most cited first, 11 of 11)

- [DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs](https://mlsanity.com/paper/2507.13737/) (2025)
- [A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection](https://mlsanity.com/paper/2412.19830/) (2024)
- [Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought](https://mlsanity.com/paper/2505.06307/) (2025)
- [Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models](https://mlsanity.com/paper/2506.20018/) (2025)
- [CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks](https://mlsanity.com/paper/2509.19855/) (2025)
- [Intelligence of Things: A Spatial Context-Aware Control System for Smart Devices](https://mlsanity.com/paper/2504.13942/) (2025)
- [The Future of Internet of Things and Multimodal Language Models in 6G Networks: Opportunities and Challenges](https://mlsanity.com/paper/2504.13971/) (2025)
- [AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space](https://mlsanity.com/paper/2506.22487/) (2025)
- [Agentic IoT: Architectures, Applications, and Challenges Toward the Internet of Agents](https://mlsanity.com/paper/2607.04219/) (2026)
- [Beyond Thresholds: A Quality-Aware Decision Intelligence Framework for Cold Chain IoT Systems](https://mlsanity.com/paper/2608.15082/) (2026)
- [Which LLM to pick? Online Active Model Selection for Large Language Models](https://mlsanity.com/paper/2610.01592/) (2026)

## Abstract (arXiv)

Recent advances in Large Language Models (LLMs) have positively and efficiently transformed workflows in many domains. One such domain with significant potential for LLM integration is the Internet of Things (IoT), where this integration brings new opportunities for improved decision making and system interaction. In this paper, we explore the various roles of LLMs in IoT, with a focus on their reasoning capabilities. We show how LLM-IoT integration can facilitate advanced decision making and contextual understanding in a variety of IoT scenarios. Furthermore, we explore the integration of LLMs with edge, fog, and cloud computing paradigms, and show how this synergy can optimize resource utilization, enhance real-time processing, and provide scalable solutions for complex IoT applications. To the best of our knowledge, this is the first comprehensive study covering IoT-LLM integration between edge, fog, and cloud systems. Additionally, we propose a novel system model for industrial IoT applications that leverages LLM-based collective intelligence to enable predictive maintenance and condition monitoring. Finally, we highlight key challenges and open issues that provide insights for future research in the field of LLM-IoT integration.