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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 May 2020) | Viewed by 24805

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Special Issue Editor

Prof. Dr. Alexander Bergmann

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Guest Editor

Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Inffeldgasse 33/I, 8010 Graz, Austria
Interests: environmental sensors; sensor networks; sensor effects; aerosol sensors; photonic sensors; electronic sensor systems; silicon photonics; biosensors
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Special Issue Information

Dear Colleagues,

New sensor technologies allow trace gas and particulate matter detection and classification at an unprecedented resolution, enabling us to better understand our environment. At the same time, inexpensive sensors are appearing on the market that measure a wide range of air quality parameters. In combination with wireless sensor network technologies, these technologies offer the potential to map air quality in cities, control low-emission zones, or identify pollution sources in- and outdoors. On the other hand, such low-cost sensors suffer from cross-sensitivity to other pollutants and stability issues. Therefore, performance metrics for low-cost standards are of the utmost importance to supplant or supplement conventional regulatory air quality monitors.

The Special Issue “Air Quality and Sensor Networks” aims to collect a comprehensive set of research advances and use cases in the field.

This Special Issue includes but is not limited to the following topics:

Performance standards of low-cost sensors;
Reliability, cross-sensitivity, and stability of environmental/air quality sensors;
Gas and vapor sensors;
Soot and particulate matter sensors;
Exhaust gases and secondary particles;
Applications of wireless sensor networks and AQ monitoring such as traffic control, city toll, pollution poor route;
Indoor air quality;
Source appointment and characterization using sensor networks and novel data analytics;
Ambient stationary and indoor deployment, sensor siting;
Data visualization, data analytics;
Field campaigns;
Regulations.

Prof. Alexander Bergmann
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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20 pages, 1802 KiB

Open AccessArticle

Internet of Things and Enhanced Living Environments: Measuring and Mapping Air Quality Using Cyber-physical Systems and Mobile Computing Technologies

by Gonçalo Marques, Nuno Miranda, Akash Kumar Bhoi, Begonya Garcia-Zapirain, Sofiane Hamrioui and Isabel de la Torre Díez

Sensors 2020, 20(3), 720; https://doi.org/10.3390/s20030720 - 28 Jan 2020

Cited by 47 | Viewed by 6606

Abstract

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality. Full article

(This article belongs to the Special Issue Air Quality and Sensor Networks)

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17 pages, 6242 KiB

Open AccessArticle

Developing of Low-Cost Air Pollution Sensor—Measurements with the Unmanned Aerial Vehicles in Poland

by Sławomir Pochwała, Arkadiusz Gardecki, Piotr Lewandowski, Viola Somogyi and Stanisław Anweiler

Sensors 2020, 20(12), 3582; https://doi.org/10.3390/s20123582 - 24 Jun 2020

Cited by 30 | Viewed by 5680

Abstract

This article presents the capabilities and selected measurement results from the newly developed low-cost air pollution measurement system mounted on an unmanned aerial vehicle (UAV). The system is designed and manufactured by the authors and is intended to facilitate, accelerate, and ensure the safety of operators when measuring air pollutants. It allows the creation of three-dimensional models and measurement visualizations, thanks to which it is possible to observe the location of leakage of substances and the direction of air pollution spread by various types of substances. Based on these models, it is possible to create area audits and strategies for the elimination of pollution sources. Thanks to the usage of a multi-socket microprocessor system, the combination of nine different air quality sensors can be installed in a very small device. The possibility of simultaneously measuring several different substances has been achieved at a very low cost for building the sensor unit: 70 EUR. The very small size of this device makes it easy and safe to mount it on a small drone (UAV). Because of this device, many harmful chemical compounds such as ammonia, hexane, benzene, carbon monoxide, and carbon dioxide, as well as flammable substances such as hydrogen and methane, can be detected. Additionally, a very important function is the ability to perform measurements of PM_2.5 and PM₁₀ suspended particulates. Thanks to the use of UAV, the measurement is carried out remotely by the operator, which allows us to avoid the direct exposure of humans to harmful factors. A big advantage is the quick measurement of large spaces, at different heights above the ground, in different weather conditions. Because of the three-dimensional positioning from GPS receiver, users can plot points and use colors reflecting a concentration of measured features to better visualize the air pollution. A human-friendly data output can be used to determine the mostly hazardous regions of the sampled area. Full article

(This article belongs to the Special Issue Air Quality and Sensor Networks)

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25 pages, 3451 KiB

Open AccessArticle

Laboratory Evaluations of Correction Equations with Multiple Choices for Seed Low-Cost Particle Sensing Devices in Sensor Networks

by Wen-Cheng Vincent Wang, Shih-Chun Candice Lung, Chun Hu Liu and Chen-Kai Shui

Sensors 2020, 20(13), 3661; https://doi.org/10.3390/s20133661 - 30 Jun 2020

Cited by 84 | Viewed by 3373

Abstract

To tackle the challenge of the data accuracy issues of low-cost sensors (LCSs), the objective of this work was to obtain robust correction equations to convert LCS signals into data comparable to that of research-grade instruments using side-by-side comparisons. Limited sets of seed LCS devices, after laboratory evaluations, can be installed strategically in areas of interest without official monitoring stations to enable reading adjustments of other uncalibrated LCS devices to enhance the data quality of sensor networks. The robustness of these equations for LCS devices (AS-LUNG with PMS3003 sensor) under a hood and a chamber with two different burnt materials and before and after 1.5 years of field campaigns were evaluated. Correction equations with incense or mosquito coils burning inside a chamber with segmented regressions had a high R² of 0.999, less than 6.0% variability in the slopes, and a mean RMSE of 1.18 µg/m³ for 0.1–200 µg/m³ of PM_2.5, with a slightly higher RMSE for 0.1–400 µg/m³ compared to EDM-180. Similar results were obtained for PM₁, with an upper limit of 200 µg/m³. Sensor signals drifted 19–24% after 1.5 years in the field. Practical recommendations are given to obtain equations for Federal-Equivalent-Method-comparable measurements considering variability and cost. Full article

(This article belongs to the Special Issue Air Quality and Sensor Networks)

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33 pages, 18095 KiB

Open AccessArticle

Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

by Leandro Ordonez-Ante, Gregory Van Seghbroeck, Tim Wauters, Bruno Volckaert and Filip De Turck

Sensors 2020, 20(9), 2737; https://doi.org/10.3390/s20092737 - 11 May 2020

Cited by 8 | Viewed by 3062

Abstract

Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving—on ingestion time—synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach. Full article

(This article belongs to the Special Issue Air Quality and Sensor Networks)

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25 pages, 7550 KiB

Open AccessArticle

Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor

by Hoochang Lee, Jiseock Kang, Sungjung Kim, Yunseok Im, Seungsung Yoo and Dongjun Lee

Sensors 2020, 20(13), 3617; https://doi.org/10.3390/s20133617 - 27 Jun 2020

Cited by 36 | Viewed by 5310

Abstract

Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 → 4.70

μ

g/m

^{3}

) and increases the correlation (e.g., R

^{2}

: 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network. Full article

(This article belongs to the Special Issue Air Quality and Sensor Networks)

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