FORA - Fog Computing for Robotics and Industrial Automation
Disruptive technologies such as cyber-physical systems, machine-to-machine communication, Big Data and machine learning, and human-robot collaboration will transform the manufacturing and industrial sectors. However, Industry 4.0 will only become a reality through the convergence of Operational and Information Technologies (OT & IT). The European Parliament, says that “a very wide range of skills is required for [Industry4.0] implementation”.
The convergence of IT, manufacturing, automation technology and software requires the development of a fundamentally new approach to training IT experts. The FOR A interdisciplinary, international, intersectoral network will train the next generation of researchers to lead this convergence and cross the IT-OT gap.
The convergence will be achieved through the new concept of Fog Computing, which is a logical extension from Cloud Computing towards the edge of the network (where machines are located), enabling applications that demand guarantees in safety, security, and real-time behavior. Research objectives focus on: a reference system architecture for Fog Computing; resource management mechanisms and middleware for deploying mixed-criticality applications in the Fog; safety and security assurance; service-oriented application modeling and real-time machine learning.
Thus FORA’s 15 Early Stage Researchers (ESRs) will receive integrated training across key areas (computer science, electrical engineering, control engineering, industrial automation, applied mathematics and data science) necessary to fully realize the potential of Fog Computing for Industry 4.0 and will move between academic and industrial environments to promote interdisciplinary and intersectoral learning.
FORA has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 764785.
TTTech will host one ESR that will propose a reference architecture for Fog Nodes for industrial automation and robotics use cases, and will develop deterministic virtualization solutions based on hypervisors and dynamics separation kernels, that minimize the interference of mixed-criticality applications on each other. Additionally, the ESR will collaborate with partner SYSGO to implement the solutions as extensions to PikeOS from SYSGO. Additionally, TTTech will host other ESRs as secondments over the course of the project.