Detection outliers on internet of things using big data technology

Ghallab, Haitham; Fahmy, Hanan; Nasr, Mona

doi:10.1016/j.eij.2019.12.001

Cited by 41 publications

(19 citation statements)

References 4 publications

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“…The Internet of Things (IoT) allows the applications to be equipped with sensors and processors that communicate with one another through internet [6]. Outlier detection is an open issue in IoT based applications.…”

Section: Outlier Detection In Iotmentioning

confidence: 99%

Anomaly detection techniques for streaming data–An overview

Kunasekaran¹,

Suriyanarayanan²

2020

Malaya J. Mat

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With the advent of smart devices and the Internet, data is being generated from various sources including mobile phones, sensor networks, telecommunications, satellites, log data, business, health care and many government sectors where the data is likely to arrive with speed. Data which flows continuously with respect to time is called streaming data and detection anomaly in such data in real time is an open challenge. Detecting anomaly in right time facilitates the appropriate control actions for the anomaly in right time. There are several techniques for detecting anomaly. In this paper, an overview of different techniques for detection of anomaly is presented.

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Section: Outlier Detection In Iotmentioning

confidence: 99%

Anomaly detection techniques for streaming data–An overview

Kunasekaran¹,

Suriyanarayanan²

2020

Malaya J. Mat

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“…There are a variety of definitions for IoT from different points of view. It was represented as a group of smart things linked through Radio Frequency Identification (RFID) (Ghallab, Fahmy, & Nasr, 2020). From a connection point of view, the IoT permits individuals to be connected anywhere and anytime with everything and everyone (Côrte-Real, Ruivo, & Oliveira, 2020).…”

Section: Introductionmentioning

confidence: 99%

IoT Data Quality Issues and Potential Solutions: A Literature Review

Mansouri

Moghadam

Monshizadeh

et al. 2021

The Computer Journal

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In the Internet of Things (IoT), data gathered from dozens of devices are the base for creating business value and developing new products and services. If data are of poor quality, decisions are likely to be non-sense. Data quality is crucial to gain business value of the IoT initiatives. This paper presents a systematic literature review regarding IoT data quality from 2000 to 2020. We analyzed 58 articles to identify IoT data quality dimensions and issues and their categorizations. According to this analysis, we offer a classification of IoT data characterizations using the focus group method and clarify the link between dimensions and issues in each category. Manifesting a link between dimensions and issues in each category is incumbent, while this critical affair in extant categorizations is ignored. We also examine data security as an important data quality issue and suggest potential solutions to overcome IoT’s security issues. The finding of this study proposes a new research discipline for additional examination for researchers and practitioners in determining data quality in the context of IoT.

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“…Local-DBSCAN (LDBSCAN) was proposed to distinguish the false targets (FTs) from the physical targets (PTs) after compensating the FTs time delays, while PTs possess small distribution [5]. Different from the other work which focused on estimating the density of each sample using different kinds of density estimators, a clustering algorithm named adapative DBSCAN was developed based on inherent properties of the nearest neighbor graph [6]. A new algorithm NRDD-DBSCAN based on the DBSCAN algorithm was presented using resilient-distributed datasets (RDDs) to explore the outliers which influence the data quality of IoT [7].…”

Section: Introductionmentioning

confidence: 99%