Blind Calibration of Air Quality Wireless Sensor Networks Using Deep Neural Networks

Veiga, Tiago S.; Ljunggren, Erling; Bach, Kerstin; Akselsen, Sigmund

doi:10.1109/coins51742.2021.9524276

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Temperature, PM10, and humidity are used as the features of the deep learning models for predicting the PM2.5 values. The results show that both proposed models in [23,24] reduce the calibration error. However, they were not validated against the real data.…”

Section: Sensor Calibrationmentioning

confidence: 87%

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An Edge Transfer Learning Approach for Calibrating Soil Electrical Conductivity Sensors

Lin,

Chang

et al. 2023

Sensors

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Smart agriculture utilizes Internet of Things (IoT) technologies to enable low-cost electrical conductivity (EC) sensors to support farming intelligence. Due to aging and changes in weather and soil conditions, EC sensors are prone to long-term drift over years of operation. Therefore, regular recalibration is necessary to ensure data accuracy. In most existing solutions, an EC sensor is calibrated by using the standard sensor to build the calibration table. This paper proposes SensorTalk3, an ensemble approach of machine learning models including XGBOOST and Random Forest, which can be executed at an edge device (e.g., Raspberry Pi) without GPU acceleration. Our study indicates that the soil information (both temperature and moisture sensor data) plays an important role in SensorTalk3, which significantly outperforms the existing calibration approaches. The MAPE of SensorTalk3 can be as low as 1.738%, compared to the 7.792% error of the original sensor. Our study indicates that when the errors of uncalibrated moisture and temperature sensors are not larger than 8.3%, SensorTalk3 can accurately calibrate EC. SensorTalk3 can perform model training during data collection at the edge node. When all training data are collected, AI training is also finished at the edge node. Such an AI training approach has not been found in existing edge AI approaches. We also proposed the dual-sensor detection solution to determine when to conduct recalibration. The overhead of this solution is less than twice the optimal detection scenario (which cannot be achieved practically). If the two non-standard sensors are homogeneous and stable, then the optimal detection scenario can be approached. Conventional methods require training calibration AI models in the cloud. However, SensorTalk3 introduces a significant advancement by enabling on-site transfer learning in the edge node. Given the abundance of farming sensors deployed in the fields, performing local transfer learning using low-cost edge nodes proves to be a more cost-effective solution for farmers.

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Section: Sensor Calibrationmentioning

confidence: 87%

“…The study in [23] utilized deep learning to investigate the effects of weather in both drifting and sensor measurements. A procedure was designed to generate simulated emission and dispersion of PM 2.5 and PM 10.…”

Section: Sensor Calibrationmentioning

confidence: 99%

An Edge Transfer Learning Approach for Calibrating Soil Electrical Conductivity Sensors

Lin,

Chang

et al. 2023

Sensors

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Deep learning on multi-view sequential data: a survey

et al. 2022

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With the progress of human daily interaction activities and the development of industrial society, a large amount of media data and sensor data become accessible. Humans collect these multi-source data in chronological order, called multi-view sequential data (MvSD). MvSD has numerous potential application domains, including intelligent transportation, climate science, health care, public safety and multimedia, etc. However, as the volume and scale of MvSD increases, the traditional machine learning methods become difficult to withstand such large-scale data, and it is no longer appropriate to use hand-craft features to represent these complex data. In addition, there is no general framework in the process of mining multi-view relationships and integrating multi-view information. In this paper, We first introduce four common data types that constitute MvSD, including point data, sequence data, graph data, and raster data. Then, we summarize the technical challenges of MvSD. Subsequently, we review the recent progress in deep learning technology applied to MvSD. Meanwhile, we discuss how the network represents and learns features of MvSD. Finally, we summarize the applications of MvSD in different domains and give potential research directions.

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Extreme Learning Machines for Calibration and Prediction in Wireless Sensor Networks: Advancing Environmental Monitoring Efficiency

Yar,

Henna,

McAfee

et al. 2023

2023 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS)

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Blind Calibration of Air Quality Wireless Sensor Networks Using Deep Neural Networks

Cited by 3 publications

References 12 publications

An Edge Transfer Learning Approach for Calibrating Soil Electrical Conductivity Sensors

An Edge Transfer Learning Approach for Calibrating Soil Electrical Conductivity Sensors

Deep learning on multi-view sequential data: a survey

Extreme Learning Machines for Calibration and Prediction in Wireless Sensor Networks: Advancing Environmental Monitoring Efficiency

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