Synthesis of Monitoring Rules with STL

2020 ◽  
Vol 29 (11) ◽  
pp. 2050177
Author(s):  
Sertac Kagan Aydin ◽  
Ebru Aydin Gol
Keyword(s):  
Genetic Algorithms ◽  
Web Services ◽  
Real Time ◽  
Temporal Logic ◽  
Online Monitoring ◽  
Cyber Physical Systems ◽  
Time Signal ◽  
Piecewise Constant ◽  
Physical Systems ◽  
Monitoring Algorithm

Online monitoring is essential to enhance the reliability for various systems including cyber-physical systems and Web services. During online monitoring, the system traces are checked against monitoring rules in real time to detect deviations from normal behaviors. In general, the rules are defined as boundary conditions by the experts of the monitored system. This work studies the problem of synthesizing online monitoring rules in the form of temporal logic formulas in an automated way. The monitoring rules are described as past-time signal temporal logic (ptSTL) formulas and an algorithm to synthesize such formulas from a given set of labeled system traces is proposed. The algorithm searches the formula space using genetic algorithms and produces the best formula representing a monitoring rule. In addition, online STL monitoring algorithm is improved to efficiently compute a quantitative valuation for piecewise-constant signals from ptSTL formulas, thus, to reduce the overhead of the real-time computation. The effectiveness of the results is shown on two illustrative examples inspired from online monitoring of Web services.

scholarly journals An automated system repair framework with signal temporal logic

2021 ◽  
Author(s):  
Mert Ergurtuna ◽  
Beyazit Yalcinkaya ◽  
Ebru Aydin Gol
Keyword(s):  
Temporal Logic ◽  
Timed Automata ◽  
Cyber Physical Systems ◽  
Automated System ◽  
Second Step ◽  
Time Signal ◽  
Physical Systems ◽  
Temporal Properties ◽  
Modeling Formalisms ◽  
Discrete Time Dynamical Systems

AbstractWe present an automated system repair framework for cyber-physical systems. The proposed framework consists of three main steps: (1) system simulation and fault detection to generate a labeled dataset, (2) identification of the repairable temporal properties leading to the faulty behavior and (3) repairing the system to avoid the occurrence of the cause identified in the second step. We express the cause as a past time signal temporal logic (ptSTL) formula and present an efficient monotonicity-based method to synthesize a ptSTL formula from a labeled dataset. Then, in the third step, we modify the faulty system by removing all behaviors that satisfy the ptSTL formula representing the cause of the fault. We apply the framework to two rich modeling formalisms: discrete-time dynamical systems and timed automata. For both of them, we define repairable formulae, the corresponding repair procedures, and illustrate them over case studies.

scholarly journals Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

2021 ◽  
Vol 35 (2) ◽  
pp. 251-264
Author(s):  
Dimitrios Boursinos ◽  
Xenofon Koutsoukos
Keyword(s):  
Neural Networks ◽  
Distance Learning ◽  
Real Time ◽  
Speaker Recognition ◽  
Deep Neural Networks ◽  
Cyber Physical Systems ◽  
Error Rates ◽  
Traffic Sign ◽  
Conformal Prediction ◽  
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.

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.

Ananke

2020 ◽  
Vol 14 (3) ◽  
pp. 391-403
Author(s):  
Dimitris Palyvos-Giannas ◽  
Bastian Havers ◽  
Marina Papatriantafilou ◽  
Vincenzo Gulisano
Keyword(s):  
Bipartite Graph ◽  
Online Monitoring ◽  
Cyber Physical Systems ◽  
Parallel Analysis ◽  
Data Streaming ◽  
Physical Systems ◽  
Fine Grained ◽  
Provenance Data ◽  
Source Data

Data streaming enables online monitoring of large and continuous event streams in Cyber-Physical Systems (CPSs). In such scenarios, fine-grained backward provenance tools can connect streaming query results to the source data producing them, allowing analysts to study the dependency/causality of CPS events. While CPS monitoring commonly produces many events, backward provenance does not help prioritize event inspection since it does not specify if an event's provenance could still contribute to future results. To cover this gap, we introduce Ananke , a framework to extend any fine-grained backward provenance tool and deliver a live bipartite graph of fine-grained forward provenance. With Ananke , analysts can prioritize the analysis of provenance data based on whether such data is still potentially being processed by the monitoring queries. We prove our solution is correct, discuss multiple implementations, including one leveraging streaming APIs for parallel analysis, and show Ananke results in small overheads, close to those of existing tools for fine-grained backward provenance.

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