Special Session 14

The Internet of Things There is pushing in the market toward a continuous improvement in terms of new devices, scalable systems and analysis algorithms. In particular, measurements systems are going to benefit of this new evolution, which opens new scenarios in all application sectors thanks to the increased connectivity and virtually unlimited bandwidth.
This is also true in the industrial domain, and although there are similarities between IoT for general systems and industrial systems, (e.g. scalability) there are significant differences because industrial systems must have low latencies, critical missions, high predictability and resilience to failures. Hence, there is justification for specific measurement systems for Industrial IoT applications (industrial Internet, Industry 4.0).
The session will bring together all the innovative ideas and technologies about measurement challenges in the Industrial IoT era, ranging from system architecture, uncertainty analysis and applications with the aim of increasing the efficiency of industrial processes in terms of cost, productivity, and predictive maintenance.

Submissions are welcomed on (but not limited to):

• Distributed measurement systems based on Industrial IoT infrastructure
• Architectures for robust and predictable measurement systems in Industrial IoT applications.
• Uncertainty propagation in measurement systems for Industrial IoT.
• IoT wireless technologies applied to industrial measurement system
• LPWAN wireless technology for sensor deployment in industrial context
• Inclusion of heterogeneous network technologies (e.g. traditional industrial fieldbus) into IoT based measurement systems.
• 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 IoT based measurement systems
• Efficient design and implementation of virtual measurement systems in terms of the timing and uncertainty constrains.
• Allocation of measurement tasks and algorithms at different infrastructure levels ranging from edge to cloud.
• Increasing of 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 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 data analysis, Internet of Things, Industry 4.0 and Smart Cities.

Dennis Brandão graduate at Engenharia Mecânica from Universidade de São Paulo (1998), master's at Mechanical Engineering from Universidade de São Paulo (2000) and ph.d. at Mechanical Engineering from Universidade de São Paulo (2005). Currently, he is associate professor at the Departamento de Engenharia Elétrica, Escola de Engenharia de São Carlos, Universidade de São Paulo . He has experience in Mechanical Engineering, acting on the following subjects: industrial automation, fieldbus foundation, wireless sensors, Profibus and Real Time Ethernet fieldbus.

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.