The Artificial Intelligence of Things (AIoT) combines artificial intelligence (AI) technologies with the Internet of things (IoT) infrastructure to achieve more efficient IoT operations and provide convenient services. Recently, the use of sensing data from multiple sensors has been proven to be an efficient way to improve service experiences in different IoT fields, e.g., intelligent transportation, security, biomedical imaging, remote sensing, smart city, security surveillance, and UAVs.

In practice, the sensing data generated by millions of sensors in IoT are heterogeneous, so analyzing these sensing data to produce helpful information is challenging. Advanced multimodal sensing information fusion techniques can integrate a large amount of sensing data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of objects. Concurrently, machine learning-based methodology has become a de-facto and efficient tool in sensing data analysis. Newly proposed paradigms, such as federated learning, etc., could also provide more feasible solutions to handle the sensing data generated by large numbers of sensors. In addition, AIoT also faces other challenges in multimodal sensing information fusion theory and its application. For instance, visible images can provide abundant texture detail with high spatial resolution which is consistent with human visual perception. In contrast, thermal/infrared sensors may provide valuable radiation information of targets with high contrast to surroundings under different lighting conditions. In either case, information from these sensors must be considered together to provide a consistent interpretation of the environment and to provide decision support to users.

The goal of this Special Issue is to collate articles with a focus on challenging issues in the field of advanced multimodal sensing information fusion technologies, frameworks, architectures, algorithms, and applications for artificial intelligence of things. Both theoretical and experimental contributions containing novel applications with new insights and findings in the field of artificial intelligence of things are welcome. Review articles which detail the current state of the art are also welcome.

Potential topics include but are not limited to the following:

• Advances in multimodal sensing information fusion for artificial intelligence of things
• Multimodal sensing information fusion for Internet of medical things
• Multimodal sensing information fusion for Internet of vehicles
• Multimodal sensing information fusion for urban Internet of things
• Multimodal sensing information fusion for UAVs
• Deep learning models for multimodal medical diagnosis
• Machine learning models for multimodal medical diagnosis
• Multimodal sensing information fusion for intelligent transportation
• Explainable machine learning models for information fusion
• The basic theory of the information fusion
• IoT framework supports for multimodal sensing information fusion
• Distributed IoT frameworks for multimodal sensing information fusion
• Federated learning for multimodal sensing information fusion
• Metaverse oriented multimodal sensing information fusion