The emergence of Internet of Things (IoT)-based applications has increased the challenges faced in intelligent data analytics in such constrained IoT devices. The huge volume of collected data cannot be efficiently analyzed in IoT devices due to their limited resources, therefore there is an increasing need for advanced learning and data mining techniques to efficiently and effectively understand, learn, and reason with these large volumes of information.

Due to recent developments in artificial intelligence (AI) technology, applications can count on an intelligent network of billions of sensors aware of their operating environment and able to listen, learn, and respond to their environment. This allows applications to offer new services and functionalities in the most disparate application domains, guaranteeing greater security, simplicity, and reliability. The analysis of data in a limited-resource network, such as IoT networks that imply heterogeneous systems, with a large percentage of devices being small and with very limited processing and energy resources is a significant challenge attracting the attention of both industry and academia.

This Special Issue aims to encourage researchers to address the challenges faced by emerging technologies in deep learning-driven methods of data analytics for IoT-based applications. Articles providing examples of the improvements brought by emerging technologies in privacy-aware analytics and efficient data analytics are encouraged. We are interested in submissions utilizing a variety of methodological perspectives, and we welcome both original research and review articles.

Potential topics include but are not limited to the following:

• AI-driven data analytics for IoT based applications
• Privacy-aware data analytics in IoT applications
• Real implementation of data analytics for IoT applications
• Computational intelligence in IoT data collection
• Artificial intelligence for IoT-based data analytics
• Outlier detection in wireless sensor networks (WSNs) and IoT
• Data analytics in IoT-based healthcare
• Edge and fog computing enabled data analytics