Industrial Internet of Things: A Review of Improvements Over Traditional SCADA Systems for Industrial Automation


Babayiğit B., Abubaker M.

IEEE Systems Journal, cilt.18, sa.1, ss.120-133, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1109/jsyst.2023.3270620
  • Dergi Adı: IEEE Systems Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Sayfa Sayıları: ss.120-133
  • Anahtar Kelimeler: Deep learning (DL), digital twin (DT), IIoT protocols, industrial Internet of Things (IIoT), Internet of Things (IoT), machine learning (ML), security, supervisory control and data acquisition (SCADA)
  • Erciyes Üniversitesi Adresli: Evet

Özet

This review article provides an overview of the potential of the Industrial Internet of Things (IIoT) to revolutionize industrial automation. The IIoT is the Internet of Things (IoT) but in an industrial context, i.e., IIoT is used more to connect machines and devices in industrial environments. The IIoT has the potential to benefit from advances in artificial intelligence, particularly machine learning and deep learning, to increase efficiency and productivity and reduce overhead costs. We provide an overview of the supervisory control and data acquisition system, a definition of IIoT, and how IIoT can offer industry greater potential for system integration to improve automation and optimization. In addition, five of the major IIoT protocols are discussed, namely, message queue telemetry transport, advanced messaging queuing protocol, constrained application protocol, data distribution service, and open platform communication unified architecture. We then identified key IIoT improvements for industrial automation. These are; efficient and low-cost systems, digital twin, machine failure prediction, real-time remote monitoring, and security. We then discussed the key research in the literature for each category. We presented some public IIoT datasets so that researchers can use them to develop new learning models to improve the security of IIoT systems. Finally, we discussed some of the limitations, recommendations, and future perspectives for developing IIoT-enabled systems.