Data quality in internet of things: A state-of-the-art survey

Karkouch, Aimad; Mousannif, Hajar; Moatassime, Hassan Al; Noël, Thomas

doi:10.1016/j.jnca.2016.08.002

Cited by 277 publications

(175 citation statements)

References 84 publications

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“…By utilizing the relationship among different indicators, we study the final result of different order of cleaning strategy by theoretical analysis. (3). An efficient data cleaning strategy is proposed to solve the multiple mixed errors in wireless sensor networks, and its effect is verified by experiments.…”

Section: Introductionmentioning

confidence: 93%

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Data quality analysis and cleaning strategy for wireless sensor networks

Cheng

Feng

Shi

et al. 2018

J Wireless Com Network

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The quality of data in wireless sensor networks has a significant impact on decision support, and data cleaning is an effective way to improve data quality. However, if the data cleaning strategies are not correctly designed, it might result in an unsatisfactory cleaning effect with increased system cleaning costs. Initially, data quality evaluation indicators and their measurement methods in wireless sensor networks were introduced. We then explored the impact of relationship between different indicators which are used in the quality assessment. Finally, data cleaning strategy for wireless sensor networks based on the relationship between data quality indicators was proposed by comparing and analyzing data cleaning schemes with different orders. The experimental results showed that the proposed data cleaning strategy can effectively improve data availability and have a better cleaning effect in wireless sensor networks for the same cleaning cost.

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Section: Introductionmentioning

confidence: 93%

“…However, the dataset is not reliable due to numerous data errors in the network. It is necessary to improve the data quality to support various applications [3].…”

Section: Introductionmentioning

confidence: 99%

Data quality analysis and cleaning strategy for wireless sensor networks

Cheng

Feng

Shi

et al. 2018

J Wireless Com Network

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“…In IoT systems, data is streaming continuously from a variety of sources which led the companies to the need to rapidly collect, store and analyze large volumes of heterogeneous data [12]. Big data technologies, such as NoSQL databases and Hadoop, can resolve this kind of problems listed above.…”

Section: Types Of Data Generated By Iotmentioning

confidence: 99%

Internet of Things, Challenges for Demand Side Management

Oprea¹,

Tudorica²,

Belciu³

et al. 2017

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“…Sebastian-Coleman [17] defines DQ by two factors: how well does the data meet the expectations of data consumers, and how well does the data represent the objects, events and concepts it is meant to represent. In the field of Internet of Things (IoT), DQ means how suitable the data collected from the smart "things" are for providing ubiquitous services for IoT users [9].…”

Section: What Is Data Quality?mentioning

confidence: 99%

“…Commonly, the DQ dimensions are used to derive more specific DQ metrics such as outlier [9,23] and anomaly detection [3]. The problem with the existing metrics is, however, that they are often developed on an ad-hoc basis to solve specific problems, and fundamental principles necessary for developing general DQ metrics in practice have not been defined [13].…”

Section: Data Quality Assessmentmentioning

confidence: 99%

Towards altruistic data quality assessment for mobile sensing

Ferreira

2017

Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 AC

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High-quality data is a necessity for successful research and development endeavors. In this article, we review relevant literature for data quality (DQ) assessment methods in different domains and discuss the possibilities, challenges and constraints of applying them to mobile sensing. We identify DQ dimensions directly applicable to sensor data: believability (comparison with the correct operating bounds), completeness (missing values), free-of-error (erroneous values), consistency (over time), timeliness (delay), accuracy (deviation from true value) and precision (granularity of readings) are core aspects of high-quality sensor data. We also emphasize that sensor data must be representative of the originating type of sensor. We propose an altruistic approach to DQ assessment for sensor data that facilitates aggregating and sharing of domain knowledge through a community-contributed library of DQ assessment methods organized by sensor type.

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Data quality in internet of things: A state-of-the-art survey

Cited by 277 publications

References 84 publications

Data quality analysis and cleaning strategy for wireless sensor networks

Data quality analysis and cleaning strategy for wireless sensor networks

Internet of Things, Challenges for Demand Side Management

Towards altruistic data quality assessment for mobile sensing

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