Integration of data science with the intelligent IoT (IIoT): current challenges and future perspectives

Abstract

The Intelligent Internet of Things (IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence (AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most of the state-of-the-art focus is on the topic independently, either on data science or IIoT. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT (IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions to the existing challenges.