Special lssues

Machine Learning and Big Data Analytics for Real-Time IoT Applications

Submission Deadline: 31 May 2022 (closed)

Guest Editors

Dr. Vijayakumar Ponnusamy, SRM Institute of Science and Technology, India.
Dr. Osamah Ibrahim, Al Nahrain University, Iraq.
Dr. Pethuru Raj, Reliance Jio Platforms Ltd. (JPL), India.


The Internet of Things (IoT) involves the interconnection of millions of devices, Things, networks, and human resources to achieve a common goal Those millions of things are connected through the Internet produce data continuously at every millisecond. IoT applications require real-time data analysis to extract useful information. This real-time analytic demand of the IoT applications on a massive volume of data results in a unique challenge for data analytics. This challenge can be solved using machine learning and big data analytics. Machine learning automates and creates analytical models which enable algorithms to learn continuously using available data. The success of IoT applications relies on developing an efficient new data analytic mechanism using machine learning. Incorporating Big Data analytics using machine learning in IoT accelerates the research advances and new business models of IoT. So there is a need to develop an efficient Big Data analytic mechanism, data mining approach, and machine learning mechanism to handle such a massive volume of data and provide real-time analytical results. This special issue focuses on new algorithms, methods, and architecture in data mining, Big data analytics using machine learning for the IoT application.

Potential topics under the special issue include but are not limited to the following:

• Machine learning algorithm for IoT and Big Data analytics

• Deep learning model for real-time IoT

• Low complex computing AI algorithms for processing IoT Big data

• Big Data programming model for IoT

• Mathematical models for Data analytic

• Distributive machine learning models for distributive in-network processing of data

• Real-time data stream mining / analytics / visualization

• Big data analytics tools/platforms for IoT

• Collaborative data analytic at the edge for IoT

• Reinforcement learning for IoT data analytics

• IoT big data applications (power grid, manufacturing, agriculture, smart city, healthcare, etc)

• The statistical mechanism for IoT


Big Data, data analytic, Deep learning, IoT, machine learning, real-time systems

Share Link