TY - JOUR
T1 - The role of big data analytics in industrial Internet of Things
AU - ur Rehman, Muhammad Habib
AU - Yaqoob, Ibrar
AU - Salah, Khaled
AU - Imran, Muhammad
AU - Jayaraman, Prem Prakash
AU - Perera, Charith
N1 - Funding Information:
Muhammad Imran’s work is supported by the Deanship of Scientific Research at King Saud University, Saudi Arabia through Research group No. ( RG # 1435-051 ).
Funding Information:
Muhammad Imran's work is supported by the Deanship of Scientific Research at King Saud University, Saudi Arabia through Research group No. (RG # 1435-051).
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/10
Y1 - 2019/10
N2 - 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, serving as future research directions.
AB - 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, serving as future research directions.
KW - Analytics
KW - Big data
KW - Cloud computing
KW - Cyber-physical systems
KW - Internet of Things
UR - https://www.scopus.com/pages/publications/85065055289
U2 - 10.1016/j.future.2019.04.020
DO - 10.1016/j.future.2019.04.020
M3 - Article
AN - SCOPUS:85065055289
SN - 0167-739X
VL - 99
SP - 247
EP - 259
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -
