A review on LLMs for IoT ecosystem: State-of-the-art, lightweight models, use cases, key challenges, future directions

The emergence of Large Language Models (LLMs) has profoundly reshaped computational linguistics, enabling unprecedented reasoning, context awareness, and semantic understanding capabilities. Integrating these sophisticated models into Internet-of-Things (IoT) ecosystems holds transformative potential for enabling intelligent, autonomous, and contextuallyaware applications. This article begins with an extensive state-ofthe-art survey of existing literature on the integration of LLMs within IoT environments, establishing foundational insights into current capabilities, limitations, and deployment frameworks. Subsequently, the manuscript contributes a comprehensive analysis of lightweight LLMs and embedding models suitable for resource-constrained IoT platforms while introducing a taxonomy of sub-billion-parameter (< 1B), mid-range (1B-2B), and exact 2B-parameter LLMs-spanning families such as Qwen, Llama, SmolLM, and IBM's Granite-as well as embedding models under 1B parameters optimized for low-latency retrieval. Comparative assessments elucidate trade-offs in model size, inference latency, context windows, energy consumption, and performance across models categorized by parameter count. Next, a diverse spectrum of prospective use cases-including home healthcare, smart agriculture, industrial optimization, and environmental monitoring-demonstrates the practical efficacy of deploying tailored LLM-IoT frameworks for real-world problemsolving. Later, the article systematically explores key challenges that must be addressed to fully realize the integration of LLMs within IoT contexts, encompassing resource constraints, heterogeneous data processing, privacy and security risks, latency requirements, model interpretability, and ethical considerations. Finally, we outline critical directions for future research, advocating advancements in IoT-specific model architectures, multimodal sensor fusion strategies, real-time adaptive inference methods, energy-aware inference scheduling, and privacy-preserving federated learning paradigms.