scholarly journals Using the Context-Sensitive Policy Mechanism for Building Data Acquisition Systems in Large Scale Distributed Cyber-Physical Systems Built on Fog Computing Platforms

Computers ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 101
Author(s):  
Alexander Vodyaho ◽  
Nataly Zhukova ◽  
Igor Kulikov ◽  
Saddam Abbas
Keyword(s):  
Data Acquisition ◽  
Large Scale ◽  
Fog Computing ◽  
Cyber Physical Systems ◽  
Flow Graph ◽  
Physical Systems ◽  
Data Acquisition Systems ◽  
Context Sensitive ◽  
Multi Level ◽  
Computing Platforms

The article deals with the use of context-sensitive policies in the building of data acquisition systems in large scale distributed cyber-physical systems built on fog computing platforms. It is pointed out that the distinctive features of modern cyber-physical systems are their high complexity and constantly changing structure and behavior, which complicates the data acquisition procedure. To solve this problem, it is proposed to use an approach according to which the data acquisition procedure is divided into two phases: model construction and data acquisition, which allows parallel realization of these procedures. A distinctive feature of the developed approach is that the models are built in runtime automatically. As a top-level model, a multi-level relative finite state operational automaton is used. The automaton state is described using a multi-level structural-behavioral model, which is a superposition of four graphs: the workflow graph, the data flow graph, the request flow graph and the resource graph. To implement the data acquisition procedure using the model, the context-sensitive policy mechanism is used. The article discusses possible approaches to implementation of suggested mechanisms and describes an example of application.

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

A resource-oriented middleware in a prototype cyber-physical manufacturing system

2017 ◽  
Vol 232 (13) ◽  
pp. 2339-2352 ◽  
Author(s):  
Chao Liu ◽  
Pingyu Jiang ◽  
Chaoyang Zhang
Keyword(s):  
Data Acquisition ◽  
Cyber Physical Systems ◽  
Application Development ◽  
Heterogeneous Sensors ◽  
Physical Systems ◽  
Enabling Technologies ◽  
Application Programming ◽  
Low Efficiency ◽  
Key Enabling Technologies ◽  
Access Right

The interconnection among heterogeneous sensors and data acquisition equipment in cyber-physical systems have profound significance in achieving adaptability, flexibility, and transparency. Various middlewares have been developed in cyber-physical systems to collect, aggregate, correlate, and translate system monitoring data. Existing middleware solutions are normally highly customized, which face several challenges due to the highly dynamic and harsh production environments. The data generated by sensors can only be shared by specific applications, which prevents the reusability of sensors. Moreover, the lack of uniform access to sensors causes high cost and low efficiency in application development. To address these issues, a resource-oriented middleware architecture called ROMiddleware was proposed, and three key enabling technologies including heterogeneous sensor modeling and grouping, open application programming interfaces development, and token-based access right control mechanism have been developed. Under the guidance of the key enabling technologies, a prototype of ROMiddleware was implemented and its performance was evaluated. Finally, two applications were developed to stress the significance of ROMiddleware. The results show that ROMiddleware can meet the requirements of data acquisition in cyber-physical systems.

Data quality challenges in large-scale cyber-physical systems: A systematic review

2021 ◽  
pp. 101951
Author(s):  
Ahmed Abdulhasan Alwan ◽  
Mihaela Anca Ciupala ◽  
Allan J. Brimicombe ◽  
Seyed Ali Ghorashi ◽  
Andres Baravalle ◽  
...  
Keyword(s):  
Systematic Review ◽  
Data Quality ◽  
Large Scale ◽  
Cyber Physical Systems ◽  
Physical Systems

iDDAF: An Intelligent Deceptive Data Acquisition Framework for Secure Cyber-Physical Systems

2021 ◽  
pp. 338-359
Author(s):  
Md Hasan Shahriar ◽  
Mohammad Ashiqur Rahman ◽  
Nur Imtiazul Haque ◽  
Badrul Chowdhury ◽  
Steven G. Whisenant
Keyword(s):  
Data Acquisition ◽  
Cyber Physical Systems ◽  
Physical Systems

scholarly journals Methods for Large-Scale Time-Triggered Network Scheduling

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 738 ◽  
Author(s):  
Francisco Pozo ◽  
Guillermo Rodriguez-Navas ◽  
Hans Hansson
Keyword(s):  
Wireless Networks ◽  
Large Scale ◽  
Synthesis Method ◽  
Cyber Physical Systems ◽  
Network Scheduling ◽  
Synthesis Time ◽  
Physical Systems ◽  
Large Size ◽  
Np Complete ◽  
Communication Schedule

Future cyber–physical systems may extend over broad geographical areas, like cities or regions, thus, requiring the deployment of large real-time networks. A strategy to guarantee predictable communication over such networks is to synthesize an offline time-triggered communication schedule. However, this synthesis problem is computationally hard (NP-complete), and existing approaches do not scale satisfactorily to the required network sizes. This article presents a segmented offline synthesis method which substantially reduces this limitation, being able to generate time-triggered schedules for large hybrid (wired and wireless) networks. We also present a series of algorithms and optimizations that increase the performance and compactness of the obtained schedules while solving some of the problems inherent to segmented approaches. We evaluate our approach on a set of realistic large-size multi-hop networks, significantly larger than those considered in the existing literature. The results show that our segmentation reduces the synthesis time by up to two orders of magnitude.

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