Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring
With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task....
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/3/1093 |
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| author | Rameez Raja Kureshi Bhupesh Kumar Mishra Dhavalkumar Thakker Reena John Adrian Walker Sydney Simpson Neel Thakkar Agot Kirsten Wante |
| author_facet | Rameez Raja Kureshi Bhupesh Kumar Mishra Dhavalkumar Thakker Reena John Adrian Walker Sydney Simpson Neel Thakkar Agot Kirsten Wante |
| author_sort | Rameez Raja Kureshi |
| collection | DOAJ |
| description | With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (<i>AH</i>) and Relative Humidity (<i>RH</i>) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (<i>R</i><sup>2</sup>), Root Mean Square Error (<i>RMSE</i>), and Mean Absolute Error (<i>MAE</i>) were compared for both <i>AH</i> and <i>RH</i>. The experimental results showed that calibration with <i>AH</i> has better performance as compared with <i>RH</i>. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems. |
| first_indexed | 2024-03-09T23:07:29Z |
| format | Article |
| id | doaj.art-08dac04892a2401c89c135066aba96b2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:07:29Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-08dac04892a2401c89c135066aba96b22023-11-23T17:50:22ZengMDPI AGSensors1424-82202022-01-01223109310.3390/s22031093Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality MonitoringRameez Raja Kureshi0Bhupesh Kumar Mishra1Dhavalkumar Thakker2Reena John3Adrian Walker4Sydney Simpson5Neel Thakkar6Agot Kirsten Wante7Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UKFaculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UKFaculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UKFaculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UKCity of Bradford Metropolitan District Council, Bradford BD1 1HX, UKCity of Bradford Metropolitan District Council, Bradford BD1 1HX, UKIBM India Pvt Ltd., Kolkata 700156, IndiaIVL Swedish Environmental Research Institute, 114 28 Stockholm, SwedenWith the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (<i>AH</i>) and Relative Humidity (<i>RH</i>) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (<i>R</i><sup>2</sup>), Root Mean Square Error (<i>RMSE</i>), and Mean Absolute Error (<i>MAE</i>) were compared for both <i>AH</i> and <i>RH</i>. The experimental results showed that calibration with <i>AH</i> has better performance as compared with <i>RH</i>. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.https://www.mdpi.com/1424-8220/22/3/1093Low-Cost Sensor (LCS)calibrationdata-driven techniquesdrift analysisair quality |
| spellingShingle | Rameez Raja Kureshi Bhupesh Kumar Mishra Dhavalkumar Thakker Reena John Adrian Walker Sydney Simpson Neel Thakkar Agot Kirsten Wante Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring Sensors Low-Cost Sensor (LCS) calibration data-driven techniques drift analysis air quality |
| title | Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring |
| title_full | Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring |
| title_fullStr | Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring |
| title_full_unstemmed | Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring |
| title_short | Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring |
| title_sort | data driven techniques for low cost sensor selection and calibration for the use case of air quality monitoring |
| topic | Low-Cost Sensor (LCS) calibration data-driven techniques drift analysis air quality |
| url | https://www.mdpi.com/1424-8220/22/3/1093 |
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