Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena

Sens-BERT: Enabling Transferability and Re-calibration of Calibration Models for Low-cost Sensors under Reference Measurements Scarcity

Low-cost sensors measurements are noisy, which limits large-scale adaptability in airquality monitoirng. Calibration is generally used to get good estimates of air quality measurements out from LCS. In order to do this, LCS sensors are typically co-located with reference stations for some duration. A calibration model is then developed to transfer the LCS sensor measurements to the reference station measurements. Existing works implement the calibration of LCS as an optimization problem in which a model is trained with the data obtained from real-time deployments; later, the trained model is employed to estimate the air quality measurements of that location. However, this approach is sensor-specific and location-specific and needs frequent re-calibration. The re-calibration also needs massive data like initial calibration, which is a cumbersome process in practical scenarios. To overcome these limitations, in this work, we propose Sens-BERT, a BERT-inspired learning approach to calibrate LCS, and it achieves the calibration in two phases: self-supervised pre-training and supervised fine-tuning. In the pre-training phase, we train Sens-BERT with only LCS data (without reference station observations) to learn the data distributional features and produce corresponding embeddings. We then use the Sens-BERT embeddings to learn a calibration model in the fine-tuning phase. Our proposed approach has many advantages over the previous works. Since the Sens-BERT learns the behaviour of the LCS, it can be transferable to any sensor of the same sensing principle without explicitly training on that sensor. It requires only LCS measurements in pre-training to learn the characters of LCS, thus enabling calibration even with a tiny amount of paired data in fine-tuning. We have exhaustively tested our approach with the Community Air Sensor Network (CAIRSENSE) data set, an open repository for LCS.Comment: 1

Toward Massive Scale Air Quality Monitoring

Dangers associated with poor air quality are driving deployments of air quality monitoring technology. These deployments rely either on professional-grade measurement stations or a small number of low-cost sensors integrated into urban infrastructure. In this article, we present a research vision of real-time massive scale air quality sensing that integrates tens of thousands or even millions of air quality sensors to monitor air quality at fine spatial and temporal resolution. We highlight opportunities and challenges of our vision by discussing use cases, key requirements and reference technologies in order to establish a roadmap on how to realize this vision. We address the feasibility of our vision, introducing a testbed deployment in Helsinki, Finland, and carrying out controlled experiments that address collaborative and opportunistic sensor calibration, a key research challenge for our vision.Peer reviewe

Distributed multi-scale calibration of low-cost ozone sensors in wireless sensor networks

New advances in sensor technologies and communications in wireless sensor networks have favored the introduction of low-cost sensors for monitoring air quality applications. In this article, we present the results of the European project H2020 CAPTOR, where three testbeds with sensors were deployed to capture tropospheric ozone concentrations. One of the biggest challenges was the calibration of the sensors, as the manufacturer provides them without calibrating. Throughout the paper, we show how short-term calibration using multiple linear regression produces good calibrated data, but instead produces biases in the calculated long-term concentrations. To mitigate the bias, we propose a linear correction based on Kriging estimation of the mean and standard deviation of the long-term ozone concentrations, thus correcting the bias presented by the sensors.Peer ReviewedPostprint (published version

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis

arXiv:1912.06384 [eess.SP]The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.Peer reviewe

Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments

The use of low-cost air quality sensors for air pollution research has outpaced our understanding of their capabilities and limitations under real-world conditions, and there is thus a critical need for understanding and optimizing the performance of such sensors in the field. Here we describe the deployment, calibration, and evaluation of electrochemical sensors on the island of Hawai'i, which is an ideal test bed for characterizing such sensors due to its large and variable sulfur dioxide (SO 2 ) levels and lack of other co-pollutants. Nine custom-built SO 2 sensors were co-located with two Hawaii Department of Health Air Quality stations over the course of 5 months, enabling comparison of sensor output with regulatory-grade instruments under a range of realistic environmental conditions. Calibration using a nonparametric algorithm (k nearest neighbors) was found to have excellent performance (RMSE 0.997) across a wide dynamic range in SO 2 ( 2ppm). However, since nonparametric algorithms generally cannot extrapolate to conditions beyond those outside the training set, we introduce a new hybrid linear-nonparametric algorithm, enabling accurate measurements even when pollutant levels are higher than encountered during calibration. We find no significant change in instrument sensitivity toward SO 2 after 18 weeks and demonstrate that calibration accuracy remains high when a sensor is calibrated at one location and then moved to another. The performance of electrochemical SO 2 sensors is also strong at lower SO 2 mixing ratios ( < 25ppb), for which they exhibit an error of less than 2.5ppb. While some specific results of this study (calibration accuracy, performance of the various algorithms, etc.) may differ for measurements of other pollutant species in other areas (e.g., polluted urban regions), the calibration and validation approaches described here should be widely applicable to a range of pollutants, sensors, and environments.United States. Environmental Protection Agency (Grant RD-83618301

Multisensor data fusion calibration in IoT air pollution platforms

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This article investigates the calibration of low-cost sensors for air pollution. The sensors were deployed on three Internet of Things (IoT) platforms in Spain, Austria, and Italy during the summers of 2017, 2018, and 2019. One of the biggest challenges in the operation of an IoT platform, which has a great impact on the quality of the reported pollution values, is the calibration of the sensors in an uncontrolled environment. This calibration is performed using arrays of sensors that measure cross sensitivities and therefore compensate for both interfering contaminants and environmental conditions. This article investigates how the fusion of data taken by sensor arrays can improve the calibration process. In particular, calibration with sensor arrays, multisensor data fusion calibration with weighted averages, and multisensor data fusion calibration with machine learning models are compared. Calibration is evaluated by combining data from various sensors with linear and nonlinear regression models.This work was supported in part by the NationalSpanish funding under Grant TIN2016-78473-C3-1-R, in part by RegionalProject under Grant 2017SGR-990, in part by EU H2020 CAPTOR Project, inpart by AGAUR SGR44, in part by the Agencia Estatal de Investigación underGrant CGL2017-82093-ERC, and in part by the Spanish Ministry of Economy,Industry and Competitiveness under Grant EUIN2017-85799Peer ReviewedPostprint (author's final draft