Large Language Models in the IoT Ecosystem -- A Survey on Security Challenges and Applications

TL;DR

This survey addresses the problem of integrating resource-intensive LLMs with resource-constrained IoT devices, aiming to unlock semantic reasoning, natural-language interaction, and autonomous decision-making while contending with latency, privacy, and security risks. It adopts a systematic literature review across major databases (IEEE Xplore, ACM Digital Library, Google Scholar, arXiv) covering 2020–mid-2024 and synthesizes findings from about 30 key papers, including IoT-LM, TENT, SAGE, and Penetrative AI. The paper maps applications across smart cities, healthcare, communications, smart homes, agriculture, and industry, and highlights how LLMs can bolster IoT security through anomaly detection and threat mitigation, alongside edge-computing strategies and semantic communication. It identifies core challenges—latency, bandwidth, cost, privacy, reliability, and security—and proposes mitigation strategies (edge processing, efficient prompting, privacy-preserving techniques, and robust validation). Finally, it outlines future research directions toward efficient edge-optimized models, explainable AI, standardized benchmarking, grounding of multi-modal sensor data, and scalable orchestration to realize trustworthy, large-scale LLM–IoT ecosystems.

Abstract

The Internet of Things (IoT) and Large Language Models (LLMs) have been two major emerging players in the information technology era. Although there has been significant coverage of their individual capabilities, our literature survey sheds some light on the integration and interaction of LLMs and IoT devices - a mutualistic relationship in which both parties leverage the capabilities of the other. LLMs like OpenAI's ChatGPT, Anthropic's Claude, Google's Gemini/BERT, any many more, all demonstrate powerful capabilities in natural language understanding and generation, enabling more intuitive and context-aware interactions across diverse IoT applications such as smart cities, healthcare systems, industrial automation, and smart home environments. Despite these opportunities, integrating these resource-intensive LLMs into IoT devices that lack the state-of-the-art computational power is a challenging task. The security of these edge devices is another major concern as they can easily act as a backdoor to private networks if the LLM integration is sloppy and unsecured. This literature survey systematically explores the current state-of-the-art in applying LLMs within IoT, emphasizing their applications in various domains/sectors of society, the significant role they play in enhancing IoT security through anomaly detection and threat mitigation, and strategies for effective deployment using edge computing frameworks. Finally, this survey highlights existing challenges, identifies future research directions, and underscores the need for cross-disciplinary collaboration to fully realize the transformative potential of integrating LLMs and IoT.