A Hopping Umbrella for Fuzzy Joining Data Streams From IoT Devices in the Cloud and on the Edge

Mrozek, Dariusz; Tokarz, Krzysztof; Pankowski, Daniel

doi:10.1109/tfuzz.2019.2955056

Cited by 15 publications

(6 citation statements)

References 60 publications

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“…With F1-F6, we mark the detected (predicted) failures of equipment that really occurred in the monitoring period. Two vertical lines represent the first and the second alert level, as described in Formulas ( 6) and (7). All detected failures are described in more detail in Table 4.…”

Section: Results and Visualizationmentioning

confidence: 99%

“…In reference to the proposal presented in the article [2], treating the existing SCADA metering and its data repository as an industrial Internet of Things environment, we will create an IoT service to increase the availability of the devices by using predictive maintenance techniques. By using cloud computing [5][6][7] and direct access to operational data, we can create new value at a low cost for operators and engineers managing the production process.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Maintenance of Boiler Feed Water Pumps Using SCADA Data

Moleda

Momot

Mrozek

2020

Sensors

Self Cite

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IoT enabled predictive maintenance allows companies in the energy sector to identify potential problems in the production devices far before the failure occurs. In this paper, we propose a method for early detection of faults in boiler feed pumps using existing measurements currently captured by control devices. In the experimental part, we work on real measurement data and events from a coal fired power plant. The main research objective is to implement a model that detects deviations from the normal operation state based on regression and to check which events or failures can be detected by it. The presented technique allows the creation of a predictive system working on the basis of the available data with a minimal requirement of expert knowledge, in particular the knowledge related to the categorization of failures and the exact time of their occurrence, which is sometimes difficult to identify. The paper shows that with modern technologies, such as the Internet of Things, big data, and cloud computing, it is possible to integrate automation systems, designed in the past only to control the production process, with IT systems that make all processes more efficient through the use of advanced analytic tools.

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Section: Results and Visualizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive Maintenance of Boiler Feed Water Pumps Using SCADA Data

Moleda

Momot

Mrozek

2020

Sensors

Self Cite

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“…When sensor readings are trasferred from IoT devices to the Cloud, a reduction in data volume is needed. Dynamic clustering using centroids and fuzzy join on data streams can address data motion and online changes in data streams [13]. The volatility in data streams with data drifts are some of the challenges that impact on the performances of machine learning.…”

Section: A Backgroundmentioning

confidence: 99%

Resource-Aware Edge-Based Stream Analytics

Petri

Chirilă

Gomes

et al. 2022

IEEE Internet Comput.

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“…Traditional cybersecurity measures often fall short of addressing the unique vulnerabilities presented by the interconnected and open nature of data lakes and IoT ecosystems. The dynamic landscape of digital threats necessitates innovative approaches to data security, ones that can adeptly safeguard privacy without sacrificing the utility and benefits of these technological advancements [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL

Valera-Rodriguez,

Manzanares-Lopez,

Cano

2024

Applied Sciences

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In the context of the increasing integration of Internet of Things technologies and the growing importance of data lakes, the need for robust cybersecurity measures to protect privacy without compromising data utility becomes key. Aiming to address the privacy–security challenge in such digital ecosystems, this study explores the application of Fully Homomorphic Encryption (FHE) using the Microsoft SEAL library. FHE allows for operations on encrypted data, offering a promising opportunity for maintaining data confidentiality during processing. Our research employs systematic experimental tests on datasets to evaluate the performance of homomorphic encryption in terms of CPU usage and execution time, executed across traditional PC configurations and a NVIDIA Jetson Nano device to assess the scalability and practicality of FHE in edge computing. The results reveal a performance disparity between computing environments, with the PC showing stable performance and the Jetson Nano revealing the limitations of edge devices in handling encryption tasks due to computational and memory constraints.

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A Hopping Umbrella for Fuzzy Joining Data Streams From IoT Devices in the Cloud and on the Edge

Cited by 15 publications

References 60 publications

Predictive Maintenance of Boiler Feed Water Pumps Using SCADA Data

Predictive Maintenance of Boiler Feed Water Pumps Using SCADA Data

Resource-Aware Edge-Based Stream Analytics

Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL

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