Towards Reliability-Based Decision Making in Cyber-Physical Systems

Nannapaneni, Saideep; Mahadevan, Sankaran; Pradhan, Subhav; Dubey, Abhishek

doi:10.1109/smartcomp.2016.7501724

Cited by 24 publications

(15 citation statements)

References 15 publications

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“…Nannapaneni et al addressed the topic of reliability evaluation of CPSs. They noted that it is difficult to achieve because of the problems derived from CPS components dependencies/interactions.…”

Section: Reliability In Cpssmentioning

confidence: 99%

“…The proposed reliability analysis aims at supporting the design phase of the CPS, especially for component selection and runtime reconfiguration. The aforementioned authors also proposed a model based on an optimization problem capable of reconfiguring the CPS in order for it to satisfy a maximum ratio of reliability to cost constraints. The proposed reliability analysis and reconfiguration model are demonstrated on a smart parking CPS.…”

Section: Reliability In Cpssmentioning

confidence: 99%

“…Nannapaneni et al 24 Zhang et al 26 addressed the challenge of assessing vulnerabilities, which can be found in the cyber networks of a power CPS. These vulnerabilities can be exploited by cyberattacks, which decrease the reliability and availability of information.…”

Section: Reliability Analysis/evaluationmentioning

confidence: 99%

“…as well as the definition of new reliability metrics. Several early attempts were presented in other works, [23][24][25][26][27][28][29][30] in which new models based on Markov analysis, Bayesian graphs, and on the optimization problem, and simulation frameworks were used to evaluate CPSs' reliability.…”

Section: Techniques For the Analysis/evaluation Of Reliabilitymentioning

confidence: 99%

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Reliability, failure detection and prevention in cyber‐physical systems (CPSs) with agents

Sanislav

Zeadally

Moiş

et al. 2018

Concurrency and Computation

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Summary The complexity of cyber‐physical systems (CPSs) opens up several challenges regarding the assurance of high levels of reliability. Therefore, we need to develop a new data quality analysis and new CPS components/subsystems for the detection, isolation, recovery, and prevention of failures and intrusion events. Furthermore, we have to develop new reliability assessment models and metrics in order to properly evaluate CPSs. Agent technologies, through their characteristics such as autonomy, social ability, reactivity, and proactivity, have the potential to address these challenges and requirements. We propose a multi‐agent based solution, namely ReliaCPS, for detecting and preventing the failures of the system components of CPSs. We also use a case study of a CPS for monitoring ambient parameters to evaluate the performance of the proposed approach. Our results indicate improvements in terms of reliability metrics (32.69% for the mean time between failures and mean time to failure, 50% for the mean time to repair and 0.94% for reliability function over time).

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Section: Reliability In Cpssmentioning

confidence: 99%

Section: Reliability In Cpssmentioning

confidence: 99%

Section: Reliability Analysis/evaluationmentioning

confidence: 99%

Section: Techniques For the Analysis/evaluation Of Reliabilitymentioning

confidence: 99%

See 2 more Smart Citations

Reliability, failure detection and prevention in cyber‐physical systems (CPSs) with agents

Sanislav

Zeadally

Moiş

et al. 2018

Concurrency and Computation

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“…In the era of the fourth industrial revolution, a new paradigm of an engineering approach has emerged, the Cyber-Physical System (CPS), which handles complex tasks in various fields of information technology, such as in smart power systems, healthcare, smart buildings, self-driving vehicles, avionics, and smart manufacturing systems [1,2]. CPSs have penetrated almost every aspect of life.…”

Section: Introductionmentioning

confidence: 99%

Failure Detection and Prevention for Cyber-Physical Systems Using Ontology-Based Knowledge Base

Ali

Hong

2018

Computers

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Cyber-physical systems have emerged as a new engineering paradigm, which combine the cyber and physical world with comprehensive computational and analytical tools to solve complex tasks. In cyber-physical systems, components are developed to detect failures, prevent failures, or mitigate the failures of a system. Sensors gather real-time data as an input to the system for further processing. Therefore, the whole cyber-physical system depends on sensors to accomplish their tasks and the failure of one sensor may lead to the failure of the whole system. To address this issue, we present an approach that utilizes the Failure Modes, Effects, and Criticality Analysis, which is a prominent hazard analysis technique to increase the understanding of risk and failure prevention. In our approach, we transform the Failure Modes, Effects, and Criticality Analysis model into a UML(Unified Modeling Language) class diagram, and then a knowledge base is constructed based on the derived UML class diagram. Finally, the UML class diagram is used to build an ontology. The proposed approach employs a 5C architecture for smart industries for its systematic application. Lastly, we use a smart home case study to validate our approach.

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Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System

Yong

Brintrup

2019

Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future

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Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. While accuracy is important, focusing primarily on it poses an overfitting danger, exposing manufacturers to risk, ultimately hindering the adoption of these techniques. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty in a cyber-physical manufacturing system (CPMS) scenario. Then, we propose a multi-agent system architecture which leverages probabilistic machine learning as a means of achieving such criteria. We propose possible scenarios for which our proposed architecture is useful and discuss future work. Experimentally, we implement Bayesian Neural Networks for multi-tasks classification on a public dataset for the real-time condition monitoring of a hydraulic system and demonstrate the usefulness of the system by evaluating the probability of a prediction being accurate given its uncertainty. We deploy these models using our proposed agent-based framework and integrate web visualisation to demonstrate its real-time feasibility.

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Towards Reliability-Based Decision Making in Cyber-Physical Systems

Cited by 24 publications

References 15 publications

Reliability, failure detection and prevention in cyber‐physical systems (CPSs) with agents

Reliability, failure detection and prevention in cyber‐physical systems (CPSs) with agents

Failure Detection and Prevention for Cyber-Physical Systems Using Ontology-Based Knowledge Base

Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System

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