Deadline Date: 31 March 2027
The rapid evolution of Large Language Models (LLMs) has significantly accelerated the development of intelligent systems across various engineering domains. By providing advanced capabilities in reasoning, planning, knowledge extraction, and human-machine interaction, LLMs are transforming conventional Internet of Things (IoT) infrastructures into intelligent and autonomous systems. The convergence of LLMs and IoT has the potential to revolutionize smart manufacturing, industrial automation, digital twins, intelligent transportation, smart healthcare, and other engineering applications.
Despite these opportunities, several fundamental challenges remain unresolved. IoT environments are characterized by limited computing resources, constrained communication bandwidth, heterogeneous devices, dynamic network conditions, and strict real-time requirements. Existing LLM frameworks are often designed for cloud-centric environments and cannot be directly deployed in large-scale engineering IoT systems. Therefore, new methodologies are needed to bridge the gap between large-scale foundation models and resource-constrained IoT infrastructures.
Given the increasing academic and industrial interest in this emerging field, a dedicated Special Issue is timely and necessary to promote innovative research and engineering solutions at the intersection of LLMs and intelligent IoT systems.
This Special Issue aims to provide a high-quality forum for researchers and practitioners to present recent advances in the design, modeling, optimization, deployment, and applications of LLM-enabled IoT systems. Particular emphasis will be placed on engineering methodologies, computational frameworks, optimization techniques, and practical implementations that enable efficient, scalable, secure, and reliable intelligent IoT services.
Both theoretical contributions and real-world engineering applications are welcome.
Topics of interest include, but are not limited to:
Large Language Models for IoT and AIoT systems
LLM-enabled intelligent sensing and perception
Engineering design of LLM-powered IoT architectures
Edge intelligence and edge deployment of LLMs
Resource-efficient and lightweight LLMs for IoT devices
Communication-efficient LLM frameworks in distributed IoT environments
Multi-agent systems and collaborative intelligence for IoT
Digital twins empowered by LLM technologies
Industrial IoT and smart manufacturing applications
Intelligent transportation systems using LLMs
Human-IoT interaction and natural language interfaces
Security, privacy, and trustworthiness in LLM-enabled IoT
Federated learning and distributed foundation models for IoT
Optimization and numerical methods for intelligent IoT systems
Reliability, scalability, and performance evaluation of LLM-based IoT applications
Real-world case studies and engineering deployments
Editorial board
