The Role of Big Data Analytics in Industrial Internet of Things
By Muhammad Habib Ur Rehman, Ibrar Yaqoob, Khaled Hamed Salah, Muhammad Imran, Prem Prakash Jayaraman, Charith Perera. Future generations computer systems, 2019
297 citations recorded by the source corpus.
Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well.
Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering, Management Information Systems
View Paper: https://www.semanticscholar.org/paper/ac1eeb98c388fa6be4579817721a75ab17b4de59
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In Computer Networks and Communications
Citations in the corpus
- Blockchain for 5G-enabled IoT for industrial automation: A systematic review, solutions, and challenges (2020, background): “Nowadays, majority of IoT-based systems are built using the centralized client-server, cloud servers, strong database [6,7] and the Internet.”
- Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects (2019, methodology): “Then, the authors of Reference [16] provided a thorough analysis of the role of BDA in IoT and a taxonomy of ML algorithms in BDA-enabled applications, such as self-maintenance, self-prediction and self-configuration.”