Machine learning and IoT for industrial measurement systems


Ivanovich Ivanovich Silva

Ivanovich Silva

Federal University of Rio Grande do Norte, Brazil

Ferrari Paolo Ferrari

Paolo Ferrari

University of Brescia, Italy


The Internet of Things allows for continuous improvement in terms of new devices, scalable systems and analysis algorithms often based on Machine Learning (ML). In particular, measurement systems are going to benefit from this new evolution, which opens new scenarios in all application sectors thanks to the increased connectivity and virtually unlimited complexity.
In the industrial applications there are similarities with IoT and ML for general systems (e.g. scalability) but, very often, there are significant differences because industrial systems must operate with low latencies, critical missions, high predictability and resilience to failures. Hence, specific measurement systems for Industrial IoT applications (industrial Internet, Industry 4.0) have to be considered.
The session will bring together all the innovative ideas and technologies about measurement challenges in the era of ML and Industrial IoT (including system architecture, uncertainty analysis and applications) with the aim of increasing the efficiency of industrial processes in terms of cost, productivity, and predictive maintenance opportunity.


Submissions are welcomed on (but not limited to):

  • Distributed measurement systems based on Industrial IoT or Machine Learning.
  • Industrial TinyML: enabling on-device sensor data analytics in the Industrial IoT Era.
  • Uncertainty propagation in measurement systems for Industrial IoT and Machine Learning.
  • IoT wireless technologies applied to industrial measurement system.
  • LPWAN wireless technology for sensor deployment in industrial context.
  • Fault tolerant measurement systems based on IoT paradigms for industrial application.
  • Security of measurement systems of industrial application with IoT enabled interfaces.
  • Enabling of predictive maintenance by means of Machine Learning and IoT based measurement systems.
  • Architectures for robust and predictable measurement systems in Industrial IoT applications.
  • Inclusion of heterogeneous network technologies (e.g. traditional industrial fieldbus) into IoT based measurement systems.
  • Efficient design and implementation of virtual measurement systems in terms of the timing and uncertainty constraints.
  • Allocation of measurement tasks and algorithms at different infrastructure levels ranging from edge to cloud.
  • Increasing the effectiveness of measurement result presentation by means of cloud based infrastructure.
  • Supporting service level virtualization for distributed measurement systems in industrial context.
  • Case studies of Industrial IoT or Machine Learning measurement systems.


Ivanovitch Silva received the licentiate, M.Sc., and Ph.D. degrees in Electrical and Computer Engineering from the Federal University of Rio Grande do Norte (UFRN), Natal, Brazil, in 2006, 2008, and 2013. He concluded in 2016 a short course about Big Data & Social Analytics at Massachusetts Institute of Technology (MIT). Since 2013 is professor at Digital Metropolis Institute (IMD,UFRN). He teaches and supervises Ph.D and master students in the Graduate Program of Electrical and Computer Engineering at UFRN. At present, he acts as the coordinator in the Lato Sensu Specialization in Big Data & Analytics at UFRN. His research interests include modeling and scientific d ata analysis, Internet of Things, Industry 4.0 and Smart Cities.

Paolo Ferrari received the M.Sc. (Hons.) degree in electronic engineering and the Ph.D. degree in “Electronic Instrumentation” from the University of Brescia, Brescia, Italy, in 1999 and 2003, respectively. He is currently a Full Professor with the Department of Information Engineering, University of Brescia. He has authored more than 200 international papers. His current research interests include embedded measurement instrumentation, smart sensors, sensor networking, smart grids, IoT and Industrial IoT, real-time Ethernet, and fieldbus applications. Dr. Ferrari is a member of IEC SC65C MT9, IEC TC65C WG10. In 2013, he received the Technical Award from the IEEE Instrumentation and Measurement Society.


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