Evaluating Sensor Data Quality in Internet of Things Smart Agriculture Applications

Fizza, Kaneez; Jayaraman, Prem Prakash; Banerjee, Abhik; Georgakopoulos, Dimitrios; Ranjan, Rajiv

doi:10.1109/mm.2021.3137401

Cited by 15 publications

(5 citation statements)

References 27 publications

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“…Evaluation strategy Domain expert knowledge Unique process General evaluation A product perspective on total data quality management [26] × × Aimq: a methodology for information quality assessment [32] × × Automated sensor verification using outlier detection in the Internet of thing [36] × × Data filtering system to avoid total data distortion in IoT networking [37] × × The daquincis architecture: a platform for exchanging and improving data quality in cooperative information systems [34] × [27] has been widely accepted within database management systems [28], [29], and within big data and IoT systems [30]- [32]. One of its core advantages is that it emphasizes an iterative approach to data quality management.…”

Section: Research Workmentioning

confidence: 99%

End-to-End Data Quality Assessment Using Trust for Data Shared IoT Deployments

2022

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Continued development of communication technologies has led to the widespread Internet of Things (IoT) integration into various domains, including health, manufacturing, automotive and precision agriculture. This has further led to the increased sharing of data amongst such domains to abet innovation. Most of these IoT deployments, however, are based on heterogeneous, pervasive sensors, which can lead to quality issues in the recorded data. This can lead to sharing of inaccurate or inconsistent data. There is a significant need to assess the quality of the collected data, should it be shared with multiple application domains, as inconsistencies in the data could have financial or health ramifications. This paper builds on the recent research on trust metrics and presents a framework to integrate such metrics into the IoT data cycle for real-time data quality assessment. Critically, this paper adopts a mechanism to facilitate end user parameterisation of a trust metric tailoring it's use in the framework. Trust is a well-established metric that has been used to determine the validity of a piece or source of data in crowd-sourced or other unreliable data collection techniques such as that in IoT.The paper further discusses how the trust based framework eliminates the requirement for a gold standard and provides visibility into data quality assessment throughout the big data model.To qualify the use of trust as a measure of quality, an experiment is conducted using data collected from an IoT deployment of sensors to measure air quality in which low cost sensors were co-located with a gold standard reference sensor. The calculated trust metric is compared with two well understood metrics for data quality, Root Mean Squares Error (RMSE) and Mean Absolute Error (MAE). A strong correlation between the trust metric and the comparison metrics shows that trust may be used as an indicative quality metric for data quality. The metric incorporates the additional benefit of its ability for use in low context scenarios, as opposed to RMSE and MAE, which require a reference for comparison.

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Section: Research Workmentioning

confidence: 99%

End-to-End Data Quality Assessment Using Trust for Data Shared IoT Deployments

2022

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“…The authors employ machine learning techniques to validate the resulting data quality, relying on data obtained from neighboring sensors. However, a significant challenge faced by this and other data quality solutions [9,27] is the requirement for a gold standard for validation.…”

Section: Data Qualitymentioning

confidence: 99%

“…Yang et al [8] proposed data quality assessment data based on deep learning for effective pest identification in smart agriculture. Fizza et al [9] proposed a conceptual data quality assessment framework that is applied to monitor milk conditions for dairy farms.…”

Section: Introductionmentioning

confidence: 99%

IoT Data Quality Assessment Framework Using Adaptive Weighted Estimation Fusion

Byabazaire

O’Hare

Collier

et al. 2023

Sensors

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Timely data quality assessment has been shown to be crucial for the development of IoT-based applications. Different IoT applications’ varying data quality requirements pose a challenge, as each application requires a unique data quality process. This creates scalability issues as the number of applications increases, and it also has financial implications, as it would require a separate data pipeline for each application. To address this challenge, this paper proposes a novel approach integrating fusion methods into end-to-end data quality assessment to cater to different applications within a single data pipeline. By using real-time and historical analytics, the study investigates the effects of each fusion method on the resulting data quality score and how this can be used to support different applications. The study results, based on two real-world datasets, indicate that Kalman fusion had a higher overall mean quality score than Adaptive weighted fusion and Naïve fusion. However, Kalman fusion also had a higher computational burden on the system. The proposed solution offers a flexible and efficient approach to addressing IoT applications’ diverse data quality needs within a single data pipeline.

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“…In 2022 Fizza et al published a paper on evaluating data quality in IoT smart agriculture applications [41]. They proposed and defined three sensor data quality metrics: suitability, accuracy, and completeness [41].…”

Section: Related Workmentioning

confidence: 99%

Selected Code-Quality Characteristics and Metrics for Internet of Things Systems

et al. 2022

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Software code is present on multiple levels within current Internet of Things (IoT) systems.The quality of this code impacts system reliability, safety, maintainability, and other quality aspects. In this paper, we provide a comprehensive overview of code quality-related metrics, specifically revised for the context of IoT systems. These metrics are divided into main code quality categories: Size, redundancy, complexity, coupling, unit test coverage and effectiveness, cohesion, code readability, security, and code heterogeneity. The metrics are then linked to selected general quality characteristics from the ISO/IEC 25010:2011 standard by their possible impact on the quality and reliability of an IoT system, the principal layer of the system, the code levels and the main phases of the projectto which they are relevant. This analysis is followed by a discussion of code smells and their relation to the presented metrics. The overview presented in the paper is the result of a thorough analysis and discussion of the author's team with the involvement of external subject-matter experts in which a defined decision algorithm was followed. The primary result of the paper is an overview of the metrics accompanied by applicability notes related to the quality characteristics, the system layer, the level of the code, and the phase of the IoT project.

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Evaluating Sensor Data Quality in Internet of Things Smart Agriculture Applications

Cited by 15 publications

References 27 publications

End-to-End Data Quality Assessment Using Trust for Data Shared IoT Deployments

End-to-End Data Quality Assessment Using Trust for Data Shared IoT Deployments

IoT Data Quality Assessment Framework Using Adaptive Weighted Estimation Fusion

Selected Code-Quality Characteristics and Metrics for Internet of Things Systems

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