LLMs and IoT: A Comprehensive Survey on Large Language Models and the Internet of Things

The rapid proliferation of the Internet of Things (IoT) has led to the emergence of highly interconnected systems that integrate a diverse array of devices. As the number of connected IoT devices continues to grow, the vast amounts of data generated by these devices pose substantial challenges in terms of collection, management, analysis, and utilization, particularly in large-scale deployments. Traditional data processing methods often fall short in addressing the complexity and real-time demands of these environments, necessitating advanced approaches for efficient data handling and intelligent interactions. Recently, Large Language Models (LLMs) have emerged as a powerful tool for enhancing natural and context-aware interactions within IoT systems. Their capabilities in processing and analyzing large datasets enable meaningful conversations, improved data interpretation, and better contextual understanding. However, integrating LLMs into IoT systems presents technical and practical challenges arising from resource limitations, privacy and security considerations, and the unique characteristics of IoT environments. This survey rigorously and comprehensively examines the current research landscape at the intersection of LLMs and IoT, covering all functional layers within the IoT ecosystem, including hardware and software, sensing, IoT networking, data analysis and management, privacy and security, and human interaction. We synthesize insights from over 261 articles, identifying open research gaps and highlighting future research directions. By addressing the characteristics, challenges, and opportunities associated with LLM integration, this article serves as a foundational resource for researchers and practitioners seeking to leverage LLM capabilities to enhance IoT functionalities while navigating the associated challenges.