Integration of SCADA and Industrial IoT: Opportunities and Challenges

Abstract

SCADA systems are used in industries to perform control and monitoring of industrial processes in real-time. The advent of the Internet of Things (IoT), and the Industrial IoT (IIoT) in particular, has brought new disruptive technology that has the potential to drive the industrial digitization agenda well beyond the capabilities demonstrated by conventional SCADA systems. Industries, particularly manufacturing industries and utilities keep thriving to be competitive and the adoption of new technologies in the form of IIoT is set to improve efficiency and productivity through enhanced real-time data analytics and production availability. This paper examines the opportunities and challenges presented by integrating IIoT into existing SCADA systems. Potential solutions to the challenges are presented together with future research outlooks that have a bearing on the SCADA/IIoT integration efforts.

Keywords:

• Industrial IoT (IIoT)
• Industry 4.0
• Internet of Things (IoT)
• SCADA

ACKNOWLEDGEMENT

The Authors would like to acknowledge the support of the Midlands State University, Department of Applied Physics and Telecommunications.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

A. Nechibvute

Action Nechibvute received PhD in physics from Midlands State University (MSU) in 2015. He holds an MSc in physics from the University of Botswana in 2008 and a BSc degree from MSU and the University of Zimbabwe in 2001. He is an academic researcher at MSU, Zimbabwe.

H. D. Mafukidze

Harry D Mafukidze received BSc degree (Hons) in physics from Midlands State University, Gweru, Zimbabwe, in 2009, and the MEng degree in electronic engineering from the University of Stellenbosch, Stellenbosch, South Africa, in 2014. He is currently pursuing PhD degree in electrical engineering at the University of Cape Town, Cape Town, South Africa. He is also working with the Department of Applied Physics and Telecommunications, Midlands State University. His research interests include machine learning and deep learning, and developing mmWave radar point cloud processing frameworks. Email: [email protected]