TY - JOUR
T1 - Industrial Internet of Things
T2 - Requirements, Architecture, Challenges, and Future Research Directions
AU - Alabadi, Montdher
AU - Habbal, Adib
AU - Wei, Xian
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - Industry 4.0 relates to the digital revolution of manufacturing and other sectors, such as retail, distribution, oil and gas, and infrastructure. Meanwhile, the Industrial Internet of Things (IIoT) is a technological advancement that leads to Industry 4.0 implementation by boosting the manufacturing sector's productivity and economic impact. IIoT provides the ability to provide global connectivity between components in different locations. The manufacturing sector has had various difficulties implementing IIoT, primarily due to IIoT characteristics. This paper offers an in-depth review of Industry 4.0 and IIoT, where the primary motivation behind this is to introduce the most recent advancements related to Industry 4.0 and IIoT, as well as to address the existing limitations. Firstly, this paper presents a novel taxonomy of IIoT challenges that includes aspects of each challenge, such as the terminology and approaches utilized to solve these challenges. Besides IIoT challenges, this survey provides an in-depth demonstration of the many concepts related to IIoT, such as architecture and use cases. Secondly, this paper provides a comprehensive review of the state-of-the-art of Industry 4.0 in terms of concepts, requirements, and supporting technology. In addition, the correlation between enabling technology and technical requirements is discussed in detail. Finally, this paper highlights deep learning, edge computing, and big data as key techniques for the future directions of IIoT. Furthermore, the presented techniques are thoroughly examined to present an alternative method for future adoption. In addition to the showcased techniques, a new architecture for the future of IIoT based on these three primary techniques is also proposed.
AB - Industry 4.0 relates to the digital revolution of manufacturing and other sectors, such as retail, distribution, oil and gas, and infrastructure. Meanwhile, the Industrial Internet of Things (IIoT) is a technological advancement that leads to Industry 4.0 implementation by boosting the manufacturing sector's productivity and economic impact. IIoT provides the ability to provide global connectivity between components in different locations. The manufacturing sector has had various difficulties implementing IIoT, primarily due to IIoT characteristics. This paper offers an in-depth review of Industry 4.0 and IIoT, where the primary motivation behind this is to introduce the most recent advancements related to Industry 4.0 and IIoT, as well as to address the existing limitations. Firstly, this paper presents a novel taxonomy of IIoT challenges that includes aspects of each challenge, such as the terminology and approaches utilized to solve these challenges. Besides IIoT challenges, this survey provides an in-depth demonstration of the many concepts related to IIoT, such as architecture and use cases. Secondly, this paper provides a comprehensive review of the state-of-the-art of Industry 4.0 in terms of concepts, requirements, and supporting technology. In addition, the correlation between enabling technology and technical requirements is discussed in detail. Finally, this paper highlights deep learning, edge computing, and big data as key techniques for the future directions of IIoT. Furthermore, the presented techniques are thoroughly examined to present an alternative method for future adoption. In addition to the showcased techniques, a new architecture for the future of IIoT based on these three primary techniques is also proposed.
KW - IIoT
KW - Industry 4.0
KW - deep learning
KW - edge computing
UR - https://www.scopus.com/pages/publications/85133685013
U2 - 10.1109/ACCESS.2022.3185049
DO - 10.1109/ACCESS.2022.3185049
M3 - 文献综述
AN - SCOPUS:85133685013
SN - 2169-3536
VL - 10
SP - 66374
EP - 66400
JO - IEEE Access
JF - IEEE Access
ER -