Artificial intelligence (AI) is increasingly being applied in the Internet of Things (IoT), and allows the interconnection of real-world physical devices with the Internet. This interconnection aims to provide connectivity for physical devices by allowing them to transmit data to the internet. This can provide new dimensions to emerging applications, for example smart cities, industrial automation, smart manufacturing, intelligent transportation systems, smart farming, and military surveillance. The massive data (especially the multimedia data) generated by these applications needs to be processed in real-time to extract useful features, but this can be difficult to do due to their diverse nature, complex structure, and storage and processing requirements.

AI has a major role in this context and has been incorporated into a wide range of applications in recent years. Intelligent image processing can now occur in real-time at the network's edge, thanks to advancements in hardware processing speed, as well as deep and machine learning. Handwriting recognition, picture captioning, self-driving, and other applications have all used AI to accomplish sophisticated image processing. Similarly, the integration of AI with IoT has found many applications for multimedia data of IoT. Intelligent image processing techniques can be used in a smart factory to monitor and control the raw material inventory, for example by discovering abnormalities in images taken in a warehouse, or monitoring licence plates and assisting in the detection of stolen cars. For the success of artificial intelligence of things in real-time image processing and feature extraction from multimedia data, the following must be designed: resilient prototypes and frameworks for the optimization of real-time image processing; real-time machine and deep learning approaches to enhance the computational speed and recognition rate for different applications; intelligent and resilient image processing applications for real-time processing; and computationally intelligent and effective image processing approaches to balance the load among multimedia devices. These approaches need to ensure reliable and effective usage of cloud, edge and fog computing.

The aim of this Special Issue is to showcase novel, real-time solutions for intelligent processing of multimedia data in IoT systems. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• New architectural designs and protocols for real-time intelligent techniques for image processing in IoT
• Neural network, deep learning, or machine learning models for real-time image processing for IoT
• Intelligent processing techniques for multimedia data of IoT
• Real-time image detection and recognition for Artificial intelligence of things
• Real-time algorithms for processing multimedia data in cloud/fog/edge-enabled applications
• Novel and real-time techniques for compression/decompression of multimedia data of IoT applications
• Novel Hardware/software designs for real-time data of Artificial intelligence of things
• Machine learning and deep learning frameworks for images and video processing of IoT applications
• Dynamic, real-time data processing and load optimization of Artificial intelligence of things
• Real-time load optimization algorithms for cloud/fog/edge in Artificial intelligence of things
• Real-time vulnerabilities and malware detection in Artificial intelligence of things