Real-Time Health-related Data, Wearable Medical Sensor Devices, and Smart Cyber-Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

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Real-Time Attack-Recovery for Cyber-Physical Systems Using Linear Approximations

2020 IEEE Real-Time Systems Symposium (RTSS) ◽

10.1109/rtss49844.2020.00028 ◽

2020 ◽

Author(s):

Lin Zhang ◽

Xin Chen ◽

Fanxin Kong ◽

Alvaro A. Cardenas

Keyword(s):

Real Time ◽

Cyber Physical Systems ◽

Physical Systems ◽

Linear Approximations

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

J ◽

10.3390/j4020012 ◽

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.

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