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

Abstract

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.

IoT Data Quality Assessment Framework Using Adaptive Weighted Estimation Fusion

“…Each has unique data quality dimensions: accuracy, completeness, consistency, and timeliness. Previous research [16] describes this in more detail. The second core component of the implementation is the data fusion engine.…”

“…This methodology must include a comprehensive view of quality metrics from the start of data acquisition to the implementation [18]. To this end, an end-to-end data quality assessment framework has been defined [16].…”

“…The DQA is responsible for assessing the quality of the data from data streams before they are shared in real-time. This is based on previous research [16] that uses trust to evaluate data quality. The previous study has built and tested an end-to-end DQ assessment framework that integrates DQ assessment into the big data cycle for data-shared IoT applications [4,16].…”

“…This is based on previous research [16] that uses trust to evaluate data quality. The previous study has built and tested an end-to-end DQ assessment framework that integrates DQ assessment into the big data cycle for data-shared IoT applications [4,16]. Trust is a well-established metric that has been used to determine the validity of a piece or source of data in crowdsourced or other unreliable data collection techniques.…”

“…This builds on previous research that uses trust to evaluate data quality [4,16]. This paper, therefore, uses data fusion strategies to combine quality scores at each stage into a single score, while maintaining the contribution of each stage.…”