scholarly journals Minimizing Overloads of Critical Tasks Using Machine Learning in CPS by Extending Resources

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 413-424
Author(s):  
S. Krishna Narayanan ◽  
Dr.S. Dhanasekaran ◽  
Dr.V. Vasudevan
Keyword(s):  
Machine Learning ◽  
Cyber Physical Systems ◽  
Online Resources ◽  
Physical Systems ◽  
To Come ◽  
Almost All ◽  
Mixed Criticality

With all the growing variety of solutions plus industries as well as nuclear, substance, aerospace, as well as auto sectors to come down with cyber-physical Systems (CPSs), methods remain now actuality seriously loaded. CPSs includes varied dangerous jobs that stand protection dangerous (1) that is high or perhaps non-protection crucial (2) that is low. For conventional job arranging, nearly almost on the current arranging algorithms offer terrible functionality for high criticality jobs, if the method suffers from overburden as well as doesn't present explicit splitting up with various criticality duties to make the most of utilizing cloud online resources. Below, a framework is proposed by us to plan the mixed criticality duties by examining the deadlines of theirs as well as delivery occasions that use the overall presentation of similar handling done by OpenMP (Open Multi-Processing). The suggested agenda presents a piece of ML-based estimate for a job unloading within the area of cloud. Furthermore, it clarifies to perform the nominated variety of low dangerous things within the area of cloud even though the extraordinary serious jobs are operated over the regional CPUs over the method clog. Consequently, the high criticality jobs fulfil almost all the deadlines of theirs and also the method accomplishes a tremendous enhancement within the general delivery period as well as much better throughput. Additionally, the investigational outcomes using OpenMP present the usefulness of utilizing the subdivided arranging during a worldwide arranging technique upon multiprocessor methods to accomplish the works isolation.

scholarly journals Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 487 ◽  
Author(s):  
Mahmoud Elsisi ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
Mohamed M. F. Darwish
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Industry 4.0 ◽  
Learning Algorithm ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Smart Meters ◽  
Industrial Environment ◽  
Physical Systems ◽  
Smart Machines

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

Adoption of Machine Learning With Adaptive Approach for Securing CPS

2020 ◽  
pp. 388-415
Author(s):  
Rama Mercy Sam Sigamani
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Smart Grids ◽  
False Negative ◽  
False Negative Rate ◽  
Cyber Physical Systems ◽  
Detection Accuracy ◽  
Physical Systems ◽  
Positive Rate ◽  
Iot Devices

The cyber physical system safety and security is the major concern on the incorporated components with interface standards, communication protocols, physical operational characteristics, and real-time sensing. The seamless integration of computational and distributed physical components with intelligent mechanisms increases the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems. In IoT-enabled cyber physical systems, cyber security is an essential challenge due to IoT devices in industrial control systems. Computational intelligence algorithms have been proposed to detect and mitigate the cyber-attacks in cyber physical systems, smart grids, power systems. The various machine learning approaches towards securing CPS is observed based on the performance metrics like detection accuracy, average classification rate, false negative rate, false positive rate, processing time per packet. A unique feature of CPS is considered through structural adaptation which facilitates a self-healing CPS.

Towards mixed criticality task scheduling in cyber physical systems: Challenges and perspectives

2019 ◽  
Vol 156 ◽  
pp. 204-216 ◽  
Author(s):  
Eugenia Ana Capota ◽  
Cristina Sorina Stangaciu ◽  
Mihai Victor Micea ◽  
Daniel-Ioan Curiac
Keyword(s):  
Task Scheduling ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Mixed Criticality
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