An Approach towards Missing Data Recovery within IoT Smart System

Izonin, Ivan; Kryvinska, Natalia; Tkachenko, Roman; Zub, Khrystyna

doi:10.1016/j.procs.2019.08.006

Cited by 26 publications

(19 citation statements)

References 23 publications

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“…Considering the significant practical and theoretical results of research in related fields, web projects should be analyzed as heterogeneous data environments [1][2][3][4][5][6] and as content sources [7][8][9][10]. As conventional messaging and news-distribution-oriented web projects are gradually being transformed into video hosting with the ability to stream video online in real time [11][12][13][14], the speed of information is measured in seconds.…”

Section: Introductionmentioning

confidence: 99%

Model of User Data Analysis Complex for the Management of Diverse Web Projects during Crises

et al. 2020

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This article discusses the relevant task of analyzing user data in the process of managing various web projects. The results of this analysis will help to improve the management of diverse web projects during crises. The authors explore the concept of data heterogeneity in web projects, classify web projects by function and purpose, and analyze the search models and data display in web projects. The proposed algorithms for analyzing user data in the process of managing diverse web projects will improve the structuring and presentation of data on the web project platform. The model user data analysis complex developed by the authors will simplify the process of managing various web projects during crises.

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Section: Introductionmentioning

confidence: 99%

Model of User Data Analysis Complex for the Management of Diverse Web Projects during Crises

et al. 2020

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“…To provide a reasonable sensor value in case of faults, different imputation techniques are defined in the literature. Izonin et al [ 36 ] developed a missing data recovery method by using Adaboost regression on transformed sensor data through Itô decomposition and compared the results with other algorithms like Support Vector Regression (SVR), Stochastic Gradient Descent (SGD) regressor, etc. Liu et al [ 37 ] defined a procedure to deal with large patches of faulty data in uni-variate time-series data.…”

Section: Related Workmentioning

confidence: 99%

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

Iggena¹,

Ilyas²,

Fischer³

et al. 2021

Sensors

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Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

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“…Linear regression yielded better accuracy when there was a linear relationship between the outcome and the predictors, whereas the implementation of SVM was suggested when the relation between features was nonlinear. Also, Izonin et al (2019) introduced an imputation method based on the utilisation of the Ito decomposition and the AdaBoost algorithm. It was concluded that the proposed approach led to more accurate results than other analysed techniques, such as SVR and SGD regressor.…”

Section: Literature Reviewmentioning

confidence: 99%

“…IIoT has demonstrated the enabling of efficient predictive maintenance (Aheleroff et al 2020), and thus determines the current and future health of machinery to assist the decision-making processes that optimise the maintenance and inspection tasks, crew management, and spare parts stocks, among other aspects. However, incomplete values, which are derived from device failure, network collapse, and human error (Noor et al 2014;Balakrishnan and Sangaiah 2018;Izonin et al 2019), may be recorded due to the utilisation of IIoT, and thus, if not addressed, data analysis may be unreliable and inaccurate, promoting bias in data-driven decisionmaking models. To that end, the implementation of data imputation is indispensable, which is a crucial step in sensor data preparation to deal with missing values.…”

Section: Introductionmentioning

confidence: 99%

A novel framework for imputing large gaps of missing values from time series sensor data of marine machinery systems

Velasco-Gallego

Lazakis

2021

Ships and Offshore Structures

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Condition-based maintenance is a maintenance strategy that implements Industrial Internet of Things to monitor the assets' condition. Despite its undeniable benefits, several challenges are encountered, such as the incompleteness of sensor data. Hence, while data imputation is an important practise, there is a lack of analysis and formalisation of data imputation in the maritime industry. Accordingly, a novel framework is introduced by implementing the first-order Markov chain in tandem with a multivariate imputation approach based on a comparative methodology of 16 machine learning and time series forecasting models. To highlight its performance efficiency, a comparative study is conducted between the proposed framework and the MICE approach by the implementation of a case study on a total of 4 parameters, obtained from sensors installed on the marine machinery systems of a cargo vessel. The results demonstrated an improvement of 21-77%, indicating its performance efficiency as a data imputation technique.

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An Approach towards Missing Data Recovery within IoT Smart System

Cited by 26 publications

References 23 publications

Model of User Data Analysis Complex for the Management of Diverse Web Projects during Crises

Model of User Data Analysis Complex for the Management of Diverse Web Projects during Crises

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

A novel framework for imputing large gaps of missing values from time series sensor data of marine machinery systems

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