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23 pages, 17070 KiB

Open AccessArticle

A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania

by Lawrence Mdegela, Yorick De Bock, Esteban Municio, Edith Luhanga, Judith Leo and Erik Mannens

Sensors 2023, 23(8), 4055; https://doi.org/10.3390/s23084055 - 17 Apr 2023

Cited by 6 | Viewed by 3308

Abstract

Reliable and accurate flood prediction in poorly gauged basins is challenging due to data scarcity, especially in developing countries where many rivers remain insufficiently monitored. This hinders the design and development of advanced flood prediction models and early warning systems. This paper introduces [...] Read more.

Reliable and accurate flood prediction in poorly gauged basins is challenging due to data scarcity, especially in developing countries where many rivers remain insufficiently monitored. This hinders the design and development of advanced flood prediction models and early warning systems. This paper introduces a multi-modal, sensor-based, near-real-time river monitoring system that produces a multi-feature data set for the Kikuletwa River in Northern Tanzania, an area frequently affected by floods. The system improves upon existing literature by collecting six parameters relevant to weather and river flood detection: current hour rainfall (mm), previous hour rainfall (mm/h), previous day rainfall (mm/day), river level (cm), wind speed (km/h), and wind direction. These data complement the existing local weather station functionalities and can be used for river monitoring and extreme weather prediction. Tanzanian river basins currently lack reliable mechanisms for accurately establishing river thresholds for anomaly detection, which is essential for flood prediction models. The proposed monitoring system addresses this issue by gathering information about river depth levels and weather conditions at multiple locations. This broadens the ground truth of river characteristics, ultimately improving the accuracy of flood predictions. We provide details on the monitoring system used to gather the data, as well as report on the methodology and the nature of the data. The discussion then focuses on the relevance of the data set in the context of flood prediction, the most suitable AI/ML-based forecasting approaches, and highlights potential applications beyond flood warning systems. Full article

(This article belongs to the Special Issue Application of Wireless Sensor Networks in Environmental Monitoring)

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20 pages, 8193 KiB

Open AccessArticle

Characterizing Ambient Seismic Noise in an Urban Park Environment

by Benjamin Saadia and Georgia Fotopoulos

Sensors 2023, 23(5), 2446; https://doi.org/10.3390/s23052446 - 22 Feb 2023

Cited by 4 | Viewed by 2261

Abstract

In this study, a method for characterizing ambient seismic noise in an urban park using a pair of Tromino3G+ seismographs simultaneously recording high-gain velocity along two axes (north-south and east-west) is presented. The motivation for this study is to provide design parameters for [...] Read more.

In this study, a method for characterizing ambient seismic noise in an urban park using a pair of Tromino3G+ seismographs simultaneously recording high-gain velocity along two axes (north-south and east-west) is presented. The motivation for this study is to provide design parameters for seismic surveys conducted at a site prior to the installation of long-term permanent seismographs. Ambient seismic noise refers to the coherent component of the measured signal that comes from uncontrolled, or passive sources (natural and anthropogenic). Applications of interest include geotechnical studies, modeling the seismic response of infrastructure, surface monitoring, noise mitigation, and urban activity monitoring, which may exploit the use of well-distributed seismograph stations within an area of interest, recording on a days-to-years scale. An ideal well-distributed array of seismographs may not be feasible for all sites and therefore, it is important to identify means for characterizing the ambient seismic noise in urban environments and limitations imposed with a reduced spatial distribution of stations, herein two stations. The developed workflow involves a continuous wavelet transform, peak detection, and event characterization. Events are classified by amplitude, frequency, occurrence time, source azimuth relative to the seismograph, duration, and bandwidth. Depending on the applications, results can guide seismograph selection (sampling frequency and sensitivity) and seismograph placement within the area of interest. Full article

(This article belongs to the Special Issue Application of Wireless Sensor Networks in Environmental Monitoring)

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12 pages, 2569 KiB

Open AccessArticle

Environmental Sensing in High-Altitude Mountain Ecosystems Powered by Sedimentary Microbial Fuel Cells

by Celso Recalde, Denys López, Diana Aguay and Víctor J. García

Sensors 2023, 23(4), 2101; https://doi.org/10.3390/s23042101 - 13 Feb 2023

Cited by 3 | Viewed by 2055

Abstract

The increasing need for fresh water in a climate change scenario requires remote monitoring of water bodies in high-altitude mountain areas. This study aimed to explore the feasibility of SMFC operation in the presence of low dissolved oxygen concentrations for remote, on-site monitoring [...] Read more.

The increasing need for fresh water in a climate change scenario requires remote monitoring of water bodies in high-altitude mountain areas. This study aimed to explore the feasibility of SMFC operation in the presence of low dissolved oxygen concentrations for remote, on-site monitoring of physical environmental parameters in high-altitude mountainous areas. The implemented power management system (PMS) uses a reference SMFC (SMFC_Ref) to implement a quasi-maximum power point tracking (quasi-MPPT) algorithm to harvest energy stably. As a result, while transmitting in a point-to-point wireless sensor network topology, the system achieves an overall efficiency of 59.6%. Furthermore, the control mechanisms prevent energy waste and maintain a stable voltage despite the microbial fuel cell (MFC)’s high impedance, low time response, and low energy production. Moreover, our system enables a fundamental understanding of environmental systems and their resilience of adaptation strategies by being a low-cost, ecological, and environmentally friendly alternative to power-distributed and dynamic environmental sensing networks in high-altitude mountain ecosystems with anoxic environmental conditions. Full article

(This article belongs to the Special Issue Application of Wireless Sensor Networks in Environmental Monitoring)

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23 pages, 9299 KiB

Open AccessArticle

A Compact IIoT System for Remote Monitoring and Control of a Micro Hydropower Plant

by Anca Albița and Dan Selișteanu

Sensors 2023, 23(4), 1784; https://doi.org/10.3390/s23041784 - 5 Feb 2023

Cited by 6 | Viewed by 3361

Abstract

Remote monitoring and operation evaluation applications for industrial environments are modern and easy means of exploiting the provided resources of specific systems. Targeted micro hydropower plant functionalities (such as tracking and adjusting the values of functional parameters, real-time fault and cause signalizing, condition [...] Read more.

Remote monitoring and operation evaluation applications for industrial environments are modern and easy means of exploiting the provided resources of specific systems. Targeted micro hydropower plant functionalities (such as tracking and adjusting the values of functional parameters, real-time fault and cause signalizing, condition monitoring assurance, and assessments of the need for maintenance activities) require the design of reliable and efficient devices or systems. The present work describes the design and implementation procedure of an Industrial Internet of Things (IIoT) system configured for a basic micro hydropower plant architecture and assuring simple means of customization for plant differences in structure and operation. The designed system features a set of commonly used functions specific to micro hydropower exploitation, providing maximum performance and efficiency. Full article

(This article belongs to the Special Issue Application of Wireless Sensor Networks in Environmental Monitoring)

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20 pages, 9260 KiB

Open AccessArticle

Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN

by Mohammad Siami, Tomasz Barszcz, Jacek Wodecki and Radoslaw Zimroz

Sensors 2022, 22(24), 10004; https://doi.org/10.3390/s222410004 - 19 Dec 2022

Cited by 6 | Viewed by 3217

Abstract

Mechanical industrial infrastructures in mining sites must be monitored regularly. Conveyor systems are mechanical systems that are commonly used for safe and efficient transportation of bulk goods in mines. Regular inspection of conveyor systems is a challenging task for mining enterprises, as conveyor [...] Read more.

Mechanical industrial infrastructures in mining sites must be monitored regularly. Conveyor systems are mechanical systems that are commonly used for safe and efficient transportation of bulk goods in mines. Regular inspection of conveyor systems is a challenging task for mining enterprises, as conveyor systems’ lengths can reach tens of kilometers, where several thousand idlers need to be monitored. Considering the harsh environmental conditions that can affect human health, manual inspection of conveyor systems can be extremely difficult. Hence, the authors proposed an automatic robotics-based inspection for condition monitoring of belt conveyor idlers using infrared images, instead of vibrations and acoustic signals that are commonly used for condition monitoring applications. The first step in the whole process is to segment the overheated idlers from the complex background. However, classical image segmentation techniques do not always deliver accurate results in the detection of target in infrared images with complex backgrounds. For improving the quality of captured infrared images, preprocessing stages are introduced. Afterward, an anomaly detection method based on an outlier detection technique is applied to the preprocessed image for the segmentation of hotspots. Due to the presence of different thermal sources in mining sites that can be captured and wrongly identified as overheated idlers, in this research, we address the overheated idler detection process as an image binary classification task. For this reason, a Convolutional Neural Network (CNN) was used for the binary classification of the segmented thermal images. The accuracy of the proposed condition monitoring technique was compared with our previous research. The metrics for the previous methodology reach a precision of 0.4590 and an F1 score of 0.6292. The metrics for the proposed method reach a precision of 0.9740 and an F1 score of 0.9782. The proposed classification method considerably improved our previous results in terms of the true identification of overheated idlers in the presence of complex backgrounds. Full article

(This article belongs to the Special Issue Application of Wireless Sensor Networks in Environmental Monitoring)

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30 pages, 7786 KiB

Open AccessArticle

Possibilities of Real Time Monitoring of Micropollutants in Wastewater Using Laser-Induced Raman & Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI)

by Claudia Post, Niklas Heyden, André Reinartz, Aaron Foerderer, Simon Bruelisauer, Volker Linnemann, William Hug and Florian Amann

Sensors 2022, 22(13), 4668; https://doi.org/10.3390/s22134668 - 21 Jun 2022

Cited by 14 | Viewed by 3157

Abstract

The entire water cycle is contaminated with largely undetected micropollutants, thus jeopardizing wastewater treatment. Currently, monitoring methods that are used by wastewater treatment plants (WWTP) are not able to detect these micropollutants, causing negative effects on aquatic ecosystems and human health. In our [...] Read more.

The entire water cycle is contaminated with largely undetected micropollutants, thus jeopardizing wastewater treatment. Currently, monitoring methods that are used by wastewater treatment plants (WWTP) are not able to detect these micropollutants, causing negative effects on aquatic ecosystems and human health. In our case study, we took collective samples around different treatment stages (aeration tank, membrane bioreactor, ozonation) of a WWTP and analyzed them via Deep-UV laser-induced Raman and fluorescence spectroscopy (LIRFS) in combination with a CNN-based AI support. This process allowed us to perform the spectra recognition of selected micropollutants and thus analyze their reliability. The results indicated that the combination of sensitive fluorescence measurements with very specific Raman measurements, supplemented with an artificial intelligence, lead to a high information gain for utilizing it as a monitoring purpose. Laser-induced Raman spectroscopy reaches detections limits of alert pharmaceuticals (carbamazepine, naproxen, tryptophan) in the range of a few µg/L; naproxen is detectable down to 1 × 10⁻⁴ mg/g. Furthermore, the monitoring of nitrate after biological treatment using Raman measurements and AI support showed a reliable assignment rate of over 95%. Applying the fluorescence technique seems to be a promising method in observing DOC changes in wastewater, leading to a correlation coefficient of R² = 0.74 for all samples throughout the purification processes. The results also showed the influence of different extraction points in a cleaning stage; therefore, it would not be sensible to investigate them separately. Nevertheless, the interpretation suffers when many substances interact with one another and influence their optical behavior. In conclusion, the results that are presented in our paper elucidate the use of LIRFS in combination with AI support for online monitoring. Full article

(This article belongs to the Special Issue Application of Wireless Sensor Networks in Environmental Monitoring)

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23 pages, 4711 KiB

Open AccessArticle

An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate Measurements

by Alexander Rusch and Thomas Rösgen

Sensors 2022, 22(12), 4377; https://doi.org/10.3390/s22124377 - 9 Jun 2022

Cited by 1 | Viewed by 2376

Abstract

The COVID-19 pandemic has emphasized the need for infection risk analysis and assessment of ventilation systems in indoor environments based on air quality criteria. In this context, simulations and direct measurements of CO₂ concentrations as a proxy for exhaled air can help [...] Read more.

The COVID-19 pandemic has emphasized the need for infection risk analysis and assessment of ventilation systems in indoor environments based on air quality criteria. In this context, simulations and direct measurements of CO₂ concentrations as a proxy for exhaled air can help to shed light on potential aerosol pathways. While the former typically lack accurate boundary conditions as well as spatially and temporally resolved validation data, currently existing measurement systems often probe rooms in non-ideal, single locations. Addressing both of these issues, a large and flexible wireless array of 50 embedded sensor units is presented that provides indoor climate metrics with configurable spatial and temporal resolutions at a sensor response time of 20 s. Augmented by an anchorless self-localization capability, three-dimensional air quality maps are reconstructed up to a mean 3D Euclidean error of 0.21 m. Driven by resolution, ease of use, and fault tolerance requirements, the system has proven itself in day-to-day use at ETH Zurich, where topologically differing auditoria (at-grade, sloped) were investigated under real occupancy conditions. The corresponding results indicate significant spatial and temporal variations in the indoor climate rendering large sensor arrays essential for accurate room assessments. Even in well-ventilated auditoria, cleanout time constants exceeded 30 min. Full article

(This article belongs to the Special Issue Application of Wireless Sensor Networks in Environmental Monitoring)

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25 pages, 3971 KiB

Open AccessArticle

A Low-Cost Sensor Network for Real-Time Thermal Stress Monitoring and Communication in Occupational Contexts

by Markus Sulzer, Andreas Christen and Andreas Matzarakis

Sensors 2022, 22(5), 1828; https://doi.org/10.3390/s22051828 - 25 Feb 2022

Cited by 27 | Viewed by 5779

Abstract

The MoBiMet (Mobile Biometeorology System) is a low-cost device for thermal comfort monitoring, designed for long-term deployment in indoor or semi-outdoor occupational contexts. It measures air temperature, humidity, globe temperature, brightness temperature, light intensity, and wind, and is capable of calculating thermal indices [...] Read more.

The MoBiMet (Mobile Biometeorology System) is a low-cost device for thermal comfort monitoring, designed for long-term deployment in indoor or semi-outdoor occupational contexts. It measures air temperature, humidity, globe temperature, brightness temperature, light intensity, and wind, and is capable of calculating thermal indices (e.g., physiologically equivalent temperature (PET)) on site. It visualizes its data on an integrated display and sends them continuously to a server, where web-based visualizations are available in real-time. Data from many MoBiMets deployed in real occupational settings were used to demonstrate their suitability for large-scale and continued monitoring of thermal comfort in various contexts (industrial, commercial, offices, agricultural). This article describes the design and the performance of the MoBiMet. Alternative methods to determine mean radiant temperature (T_mrt) using a light intensity sensor and a contactless infrared thermopile were tested next to a custom-made black globe thermometer. Performance was assessed by comparing the MoBiMet to an independent mid-cost thermal comfort sensor. It was demonstrated that networked MoBiMets can detect differences of thermal comfort at different workplaces within the same building, and between workplaces in different companies in the same city. The MoBiMets can capture spatial and temporal differences of thermal comfort over the diurnal cycle, as demonstrated in offices with different stories and with different solar irradiances in a single high-rise building. The strongest sustained heat stress was recorded at industrial workplaces with heavy machinery. Full article

(This article belongs to the Special Issue Application of Wireless Sensor Networks in Environmental Monitoring)

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