The rapid development of communication, computation, and networking technologies has made connected devices become possible and at the same time has brought volumes of data to many fields. The ubiquitous Internet of Things (IoT) has extended the current Internet structure by enabling various interactions and connectivity among ubiquitous-connected devices. As a result, intelligent analyzing and processing big data generated by IoT devices are essential for developing smart IoT applications.

As a typical technique of artificial intelligence, machine learning employs supervised or unsupervised strategies to automatically learn multi-layer representations of data and has been successfully applied to the fields of speech recognition, collaborative filtering, and image processing. Although machine learning has made progress in some scientific fields, it still faces many scientific challenges for its application in ubiquitous IoT. For example, where to run the intensive computation of neural networks, and how to design lightweight learning models for resource-constraint devices.

Therefore, this Special Issue aims to seek high-quality papers from academics and industry-related researchers of ubiquitous IoT, machine learning, and big data, who conduct research on the development of machine learning models for IoT systems, and present the most recently advanced methods and applications.

Potential topics include but are not limited to the following:

• Big Data theory and methods
• Machine Learning and Deep Learning
• Knowledge discovery for Big Data
• The technologies for IoT devices
• Resource management for IoT system
• Ubiquitous edge computing
• Security and privacy for machine learning technologies
• Novel learning models for IoT system
• Intelligent data processing
• Big Data analysis and applications in other fields