The Internet of Things (IoT) is evolving from simple connectivity to a paradigm of intelligent, autonomous, and context-aware systems. This shift is driven by the integration of Artificial Intelligence (AI) across the IoT stack, from sensors and devices to edge, fog, and cloud platforms. The convergence of AI and IoT—often termed AIoT—enables real-time analytics, predictive and prescriptive decision-making, and adaptive control in domains such as smart cities, transportation, healthcare, manufacturing, agriculture, and critical infrastructure.

However, deploying AI within large-scale, heterogeneous, and resource-constrained IoT environments introduces substantial challenges. These include designing lightweight AI models suitable for constrained devices, distributing intelligence across device–edge–cloud layers, ensuring interoperability among diverse components, maintaining energy efficiency and sustainability, and safeguarding security, privacy, and trust in AI-enabled IoT systems.

This Research Topic in Frontiers in the Internet of Things aims to bring together contributions that advance the design, analysis, and deployment of AI within IoT ecosystems. We encourage both foundational research and application-driven studies that highlight the benefits, limitations, and trade-offs of AIoT solutions.

We welcome original research articles, reviews, mini reviews, perspectives, brief research reports, and case studies on topics including, but not limited to:

o AI-enabled IoT devices and hardware: TinyML, on-device learning, and accelerators for constrained IoT nodes.

o Intelligent networking and communication: AI-driven optimization of IoT networks, and AI-assisted management of 5G/6G and LPWAN.

o Distributed intelligence: Edge, fog, and cloud AI architectures; model partitioning and real-time, distributed inference.

o AI-driven platforms and data management: Middleware integrating AI services; semantic and context-aware reasoning in AIoT.

o Security, privacy, and trust: AI-powered intrusion and anomaly detection, blockchain-supported AIoT security, and privacy-preserving learning.

o AI/ML methods for IoT constraints: Lightweight, energy-aware, and communication-efficient algorithms; federated and continual learning.

o Applications and case studies: AIoT in smart cities, healthcare, industrial IoT and Industry 4.0, agriculture, and environmental monitoring.

This Research Topic seeks to provide a focused forum for researchers and practitioners working at the intersection of AI and IoT to share new ideas, methodologies, and real-world insights that will drive the next generation of intelligent IoT systems.

Are you interested in contributing a manuscript to this Research Topic?

AIoT: The Convergence of Artificial Intelligence and IoT