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Sensor Network Applications for Environmental Monitoring

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

Deadline for manuscript submissions: 10 April 2025 | Viewed by 5066

Special Issue Editors


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Guest Editor
1. Centro ALGORITMI, University of Minho, 4804-533 Guimarães, Portugal
2. Department of Industrial Electronics, University of Minho, 4804-533 Guimarães, Portugal
Interests: software design and reuse; middleware frameworks; object-oriented techniques; reusability; mobile applications; monitoring and automation systems; embedded systems based on microcontrollers
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. CMEMS-University of Minho, 4804-533 Guimarães, Portugal
2. Department of Industrial Electronics, University of Minho, 4804-533 Guimarães, Portugal
Interests: sensors; actuators; electronics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. CMEMS—UMinho, University of Minho, 4800-058 Guimarães, Portugal
2. LABBELS—Associate Laboratory, University of Minho, 4710-057 Braga, Portugal
3. Department of Industrial Electronics, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal
Interests: internet of things; wireless sensor networks; body sensor network; mobile phone sensing; quality of service; medium access control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human activity has a significant impact in nature and living conditions. In this sense, monitoring is crucial to provide data that enable the maintenance of healthy environments, whether on land, in the water or in the air, and control systems may also be implemented based on readings of relevant parameters. Sensor networks allow the monitoring of variables that characterize environmental conditions over large areas or during extended periods, at a much lower cost than approaches based, for example, on data loggers. IoT (Internet of Things) offers a practical way of storing and accessing all data and is the most probable model for environment sensing applications.

Recent advances in communications technology, such as in LPWANs (low-power wide-area networks), offer much more flexible and wider monitoring solutions. Depending on the application, monitoring systems can use short-range sensor networks, such as ZigBee or BLE (Bluetooth low-energy), wide-area sensor networks, such as LoRa, or a direct connection to the Internet, such as by using NB-IoT (narrowband IoT). Processing platforms, such as microcontrollers, have also evolved and offer advanced as well as flexible control over power consumption, which enable the achievement of higher energy autonomy or the use of batteries with a lower capacity. Additionally, sensors are systems’ interfaces to the real world and range from transducers that need to be calibrated and linearized to advanced units with digital interfaces.

This Special Issue addresses studies whose objective is the monitoring and/or characterization of environments through the measurement of relevant parameters. Thus, the focus is on applications of sensor network technologies to monitor, for example, water resources/availability, water pollution, air quality, forest fires, soil conditions for agriculture, disposal of waste/garbage, state of natural heritage and ecosystems for protection/preservation, etc.

Dr. Sérgio F. Lopes
Prof. Dr. Luis Gonçalves
Dr. José A. Afonso
Guest Editors

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Keywords

  • sensors for environmental monitoring
  • energy harvesting
  • energy efficiency
  • network technologies and applications
  • application protocols
  • processing platforms/devices
  • data management
  • Internet of Things

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Published Papers (3 papers)

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Research

15 pages, 5706 KiB  
Article
Algorithms and Resources for the Monitoring of Very-Low-Frequency Signal Deviations Due to Solar Activity Using a Web-Based Software-Defined Radio-Distributed Network
by Ilia Iliev, Kostadin Tudjarov, Ivaylo Nachev, Peter Z. Petkov, Yuliyan Velchev and Ana Ilieva
Sensors 2024, 24(14), 4596; https://doi.org/10.3390/s24144596 - 16 Jul 2024
Viewed by 862
Abstract
This work presents the development and testing of an experimental web-based SDR (software-defined radio) monitoring system for indirect solar activity detection, which has the ability to estimate and potentially predict various events in space and on earth, including solar flares, coronal mass ejections, [...] Read more.
This work presents the development and testing of an experimental web-based SDR (software-defined radio) monitoring system for indirect solar activity detection, which has the ability to estimate and potentially predict various events in space and on earth, including solar flares, coronal mass ejections, and geomagnetic storms. The proposed system can be used to investigate the effect of solar activity on the propagation of very-low-frequency (VLF) signals. The advantages and benefits of the given approach are as follows: increasing measurement accuracy and eventual solar activity identification by combining measurements from multiple spatially distributed SDRs. The verification process involves carrying out several experiments comparing data from the GOES satellite system and the Dunksin SuperSID system with information received by the SDR monitoring system. Then, utilizing Pearson correlation coefficients, the measured data from the SDRs, along with those from the GOES satellite system and the Dunsing monitoring station, are investigated. At the time of a solar flare, the correlation value is above 90% for most of the stations used. Combining the signal-to-noise ratio via summation also shows an improvement in the results, with a correlation above 98%. Full article
(This article belongs to the Special Issue Sensor Network Applications for Environmental Monitoring)
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16 pages, 10976 KiB  
Article
Developing a Cloud-Based Air Quality Monitoring Platform Using Low-Cost Sensors
by Abdul Samad, Joschka Kieser, Ioannis Chourdakis and Ulrich Vogt
Sensors 2024, 24(3), 945; https://doi.org/10.3390/s24030945 - 1 Feb 2024
Cited by 4 | Viewed by 2021
Abstract
Conventional air quality monitoring has been traditionally carried out in a few fixed places with expensive measuring equipment. This results in sparse spatial air quality data, which do not represent the real air quality of an entire area, e.g., when hot spots are [...] Read more.
Conventional air quality monitoring has been traditionally carried out in a few fixed places with expensive measuring equipment. This results in sparse spatial air quality data, which do not represent the real air quality of an entire area, e.g., when hot spots are missing. To obtain air quality data with higher spatial and temporal resolution, this research focused on developing a low-cost network of cloud-based air quality measurement platforms. These platforms should be able to measure air quality parameters including particulate matter (PM10, PM2.5, PM1) as well as gases like NO, NO2, O3, and CO, air temperature, and relative humidity. These parameters were measured every second and transmitted to a cloud server every minute on average. The platform developed during this research used one main computer to read the sensor data, process it, and store it in the cloud. Three prototypes were tested in the field: two of them at a busy traffic site in Stuttgart, Marienplatz and one at a remote site, Ötisheim, where measurements were performed near busy railroad tracks. The developed platform had around 1500 € in materials costs for one Air Quality Sensor Node and proved to be robust during the measurement phase. The notion of employing a Proportional–Integral–Derivative (PID) controller for the efficient working of a dryer that is used to reduce the negative effect of meteorological parameters such as air temperature and relative humidity on the measurement results was also pursued. This is seen as one way to improve the quality of data captured by low-cost sensors. Full article
(This article belongs to the Special Issue Sensor Network Applications for Environmental Monitoring)
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21 pages, 3437 KiB  
Article
Field Measurement of the Dynamic Interaction between Urban Surfaces and Microclimates in Humid Subtropical Climates with Multiple Sensors
by Min-cheng Tu and Wei-jen Chen
Sensors 2023, 23(24), 9835; https://doi.org/10.3390/s23249835 - 14 Dec 2023
Cited by 1 | Viewed by 1193
Abstract
Forcing pathways between urban surfaces (impervious and pervious pavers) and near-surface air temperature were measured and investigated with a network of multiple sensors. Utilizing field data measured between April 2021 and May 2022, and assuming that the influential variables follow the basic heat-transfer [...] Read more.
Forcing pathways between urban surfaces (impervious and pervious pavers) and near-surface air temperature were measured and investigated with a network of multiple sensors. Utilizing field data measured between April 2021 and May 2022, and assuming that the influential variables follow the basic heat-transfer energy-balance equations, multiple regression-based statistical models were built to predict the surface temperature and near-surface air temperature (0.05 m, 0.5 m, 1 m, 2 m, and 3 m) of one impervious paver site and one pervious paver site in Taipei City, Taiwan. Evaporative cooling was found to be more influential on the pervious paver with a statistically significant influence on the microclimate up to 1.8 m (and up to 0.7 m for the impervious paver), using in situ data with an ambient air temperature higher than 24 °C. The surface temperature is mainly affected by solar shortwave radiation and ambient air temperature. As for near-surface air temperature, ambient air temperature is the most influential factor, followed by surface temperature. The importance of surface temperature indicates the influence of upwelling longwave radiation on the microclimate. The predictive equations show that pervious surfaces can help cities with hot and humid climates fight the changing climate in the future. Full article
(This article belongs to the Special Issue Sensor Network Applications for Environmental Monitoring)
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