Augmenting Cyber Physical Systems through Data Collection and Machine Learning: A Perspective

Abstract

Industry 4.0, destined to an astounding breakthrough in the field of Production and Manufacturing through leading-edge Cyber Physical Systems, Monitoring systems and Automation. The next big Industrial Revolution focuses on digitalizing the industries which is popularly known as Digital Twinning, any changes to the Digital Twin reflects the Real Physical World. Cyber Physical Systems are the key players in the 4th industrial Revolution,Cyber Physical Systems are amalgamation of Theory of Cybernetics and Mechatronics where Physical Plant i.e., Full-Fledged Hardware component Controlled/Monitored by Computational platform i.e., Computer-Based Algorithms moderated by Network Fabrics and Sensors. CPS integrates the dynamics of physical processes, software and networking where components of Physical and Computational elements are deeply intertwined. The Backdoors of the system aren’t robust enough to tackle modern-day Cyber Threats. Digital Twinning gives us an upper hand in both Security and Production perspectives. Our paper aims at enhancing the production and security of the CPS through Machine learning approach, analysing the digital asserts statistically to set a favourable pay.

Keywords: Industry 4.0, Cyber Physical Systems, Game Theory,Deep Learning, Transfer Learning, Simulated Systems, Digital Twin, Security Mechanisms, Cloud Manufacturing, Big Data Analytics,NoSQL