Research on real-time reliability evaluation of CPS system based on machine learning

Wang, Hechuang

doi:10.1016/j.comcom.2020.04.039

Cited by 12 publications

(3 citation statements)

References 15 publications

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“…They study 16 software life cycle databases empirically and evaluate their predictions using mean absolute error, root mean squared error, correlation coefficient and precisions. A related research is performed by Wang [40] where the author studies the reliability of cyber-physical systems using different machine-learning techniques. Didona et al [15] combines analytical models with machine learning approaches to predict the performance of software systems.…”

Section: B Machine-learning Approachesmentioning

confidence: 99%

Smart and Adaptive Routing Architecture: An Internet-of-Things Traffic Manager Based on Artificial Neural Networks

Amiri,

Zdun

2023

2023 IEEE International Conference on Software Services Engineering (SSE)

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Many studies have been performed on integrating the Internet of Things (IoT) with cloud services. As these systems become widely used, quality metrics are of concern. For example, users might specify access control to restrict their sensitive data being processed in the cloud. Routers, e.g., API gateways, message brokers, or sidecars, can provide this access control by blocking or routing device data to a specific cloud service. However, a static routing application might not suit the dynamic behavior of IoT applications well. For example, in a centralized schema, where all device data is routed to a component for control checking, performance can be an issue. On the other hand, distributed routing can harm the reliability of a system, as device data might be lost due to an unresponsive service. We present the Smart and Adaptive Routing (SAR) architecture that creates an optimal reconfiguration solution using a deep neural network based on the quality metrics of an IoT application. To design our architecture, we give a background of the published studies and a review of the gray literature, e.g., practitioner blogs, to categorize the knowledge in the domain of IoT-cloud traffic management. We systematically evaluate our approach in an extensive evaluation of 4500 cases and compare SAR with an empirical data set of 1200 hours. The results show that our approach significantly improves quality-of-service measures by adapting the IoT-cloud system at runtime.

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Section: B Machine-learning Approachesmentioning

confidence: 99%

Smart and Adaptive Routing Architecture: An Internet-of-Things Traffic Manager Based on Artificial Neural Networks

Amiri,

Zdun

2023

2023 IEEE International Conference on Software Services Engineering (SSE)

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“…The new system can overcome password guessing or smartcard lost attacks on traditional authentication schemes, such as password or smartcard, to validate users. Wang ( 2020 ) proposed a system to evaluate the reliability of CPS based on different analytic methods. First, clustering methods are used to classify the importance of nodes in complex networks.…”

Section: Current Interdisciplinary Research Between Data Analytics and Industry 40mentioning

confidence: 99%

Data Analytics in Industry 4.0: A Survey

Duan

2021

Inf Syst Front

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Industry 4.0 is the fourth industrial revolution for decentralized production through shared facilities to achieve on-demand manufacturing and resource efficiency. It evolves from Industry 3.0 which focuses on routine operation. Data analytics is the set of techniques focus on gain actionable insight to make smart decisions from a massive amount of data. As the performance of routine operation can be improved by smart decisions and smart decisions need the support from routine operation to collect relevant data, there is an increasing amount of research effort in the merge between Industry 4.0 and data analytics. To better understand current research efforts, hot topics, and tending topics on this critical intersection, the basic concepts in Industry 4.0 and data analytics are introduced first. Then the merge between them is decomposed into three components: industry sectors, cyber-physical systems, and analytic methods. Joint research efforts on different intersections with different components are studied and discussed. Finally, a systematic literature review on the interaction between Industry 4.0 and data analytics is conducted to understand the existing research focus and trend.

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“…In terms of machine learning capabilities in CPS, clustering data streams from high throughput machining cycle conditions [29], real-time reliability evaluation of CPS system [30], an IoT-based wearable system for fetal movement monitoring [31], detecting time synchronization attacks on CPS [32], and behaviour-based attack detection and classification [33] are some of the leading instances of direct value generation from machine learning. In contrast, the number of studies focusing on concept drift detection in CPS is limited.…”

Section: Related Workmentioning

confidence: 99%

Continuous detection of concept drift in industrial cyber-physical systems using closed loop incremental machine learning

Jayaratne

Silva

Alahakoon

et al. 2021

Discov Artif Intell

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The embedded, computational and cloud elements of industrial cyber physical systems (CPS) generate large volumes of data at high velocity to support the operations and functions of corresponding time-critical and mission-critical physical entities. Given the non-deterministic nature of these entities, the generated data streams are susceptible to dynamic and abrupt changes. Such changes, which are formally defined as concept drifts, leads to a decline in the accuracy and robustness of predicted CPS behaviors. Most existing work in concept drift detection are classifier dependent and require labeled data. However, CPS data streams are unlabeled, unstructured and change over time. In this paper, we propose an unsupervised machine learning algorithm for continuous concept drift detection in industrial CPS. This algorithm demonstrates three types of unsupervised learning, online, incremental and decremental. Furthermore, it distinguishes between abrupt and reoccurring drifts. We conducted experiments on SEA, a widely cited synthetic dataset of concept drift detection, and two industrial applications of CPS, task tracking in factory settings and smart energy consumption. The results of these experiments successfully validate the key features of the proposed algorithm and its utility of detecting change in non-deterministic CPS environments.

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Research on real-time reliability evaluation of CPS system based on machine learning

Cited by 12 publications

References 15 publications

Smart and Adaptive Routing Architecture: An Internet-of-Things Traffic Manager Based on Artificial Neural Networks

Smart and Adaptive Routing Architecture: An Internet-of-Things Traffic Manager Based on Artificial Neural Networks

Data Analytics in Industry 4.0: A Survey

Continuous detection of concept drift in industrial cyber-physical systems using closed loop incremental machine learning

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