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Advanced Sensing Technology for Environment Monitoring
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Advanced Sensing Technology for Environment Monitoring

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

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 39696

Special Issue Editors

Dr. Waheb Abdullah

E-Mail Website
Guest Editor
College of Engineering, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham B4 7XG, United Kingdom
Interests: internet of things; cyber-physical systems; wireless networks; smart city
Special Issues, Collections and Topics in MDPI journals
Dr. AbdulRahman Alsewari

E-Mail Website
Guest Editor
School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham B4 7XG, UK
Interests: artificial intelligence; combinatorial testing; optimisation algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Mario De Oliveira

E-Mail Website
Guest Editor
College of Engineering, Birmingham City University, Birmingham B4 7XG, UK
Interests: signal processing; machine learning; structural health monitoring; electronics systems; intelligent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the advancements in smart sensing technologies have dramatically altered environmental monitoring approaches. The emergence of the Internet of Things (IoT), Artificial Intelligence (AI), 5G, and Future networks technologies has brought forth a new era of smart environment development, which has led to significant transformations in environmental sensing and monitoring systems through the integration of data acquisition, data optimisation, data visualisation, and communication technologies. Advanced sensing technology for environmental monitoring is not limited to the digitalisation of the monitoring process but is also involved in creating data-driven smart sensors with processing capabilities and communication interfaces to enable real-time data monitoring and support decision making. A variety of environment-monitoring applications can have crucial roles in proper disaster management, pollution control, emission reduction, and sustainability management. Environment monitoring deals with tracking changes in environmental trends including weather changes, natural disasters, air quality, water quality, soil conditions, agriculture activities and hazardous radiation.

This Special Issue emphasises the practical applications of new technology-driven sensors in each area of environmental monitoring. High-quality research studies dealing with any aspect of advanced sensing technologies for environmental monitoring are encouraged. Original research reporting theoretical, simulation, or experimental studies related to sensors in environmental monitoring are welcomed. Preference will be given to papers from applied and experimental research in the field and review studies on the most recent sensing technologies for smart environment applications. This Special Issue, therefore, invites original contributions on topics related to recent advancements and technologies in smart sensing for environment monitoring and their possible applications and new challenges related to, but not limited to, the following topics:

  • Smart sensing technologies;
  • Smart environment applications;
  • Real-time environmental monitoring systems;
  • IoT-based systems for environment monitoring;
  • 5G-enabled sensors for monitoring environments;
  • AI-enabled environment-monitoring applications;
  • Future networks for environmental monitoring;
  • LoRa/Sigfox/NB-IoT for smart environments;
  • Augmented Reality for environmental monitoring;
  • Sensing technologies for disaster risk reduction;
  • Smart agriculture technologies;
  • Livestock monitoring;
  • Nanotechnology-based sensors for environmental monitoring;
  • Biosensors for environmental monitoring;
  • Security and privacy issues in environment monitoring;
  • Cutting-edge technologies for smart environments.

Dr. Waheb Abdullah
Dr. AbdulRahman Alsewari
Dr. Mario De Oliveira
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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.

Keywords

  • Advanced sensing systems
  • Smart sensing and monitoring
  • Environmental monitoring
  • Smart environment
  • Environmental quality
  • Disaster management
  • Internet of Things
  • Future networks
  • Artificial intelligence
  • Smart city

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Published Papers (15 papers)

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19 pages, 3861 KiB  
Article
Self-Interested Coalitional Crowdsensing for Multi-Agent Interactive Environment Monitoring
by Xiuwen Liu, Xinghua Lei, Xin Li and Sirui Chen
Sensors 2024, 24(2), 509; https://doi.org/10.3390/s24020509 - 14 Jan 2024
Viewed by 1068
Abstract
As a promising paradigm, mobile crowdsensing (MCS) takes advantage of sensing abilities and cooperates with multi-agent reinforcement learning technologies to provide services for users in large sensing areas, such as smart transportation, environment monitoring, etc. In most cases, strategy training for multi-agent reinforcement [...] Read more.
As a promising paradigm, mobile crowdsensing (MCS) takes advantage of sensing abilities and cooperates with multi-agent reinforcement learning technologies to provide services for users in large sensing areas, such as smart transportation, environment monitoring, etc. In most cases, strategy training for multi-agent reinforcement learning requires substantial interaction with the sensing environment, which results in unaffordable costs. Thus, environment reconstruction via extraction of the causal effect model from past data is an effective way to smoothly accomplish environment monitoring. However, the sensing environment is often so complex that the observable and unobservable data collected are sparse and heterogeneous, affecting the accuracy of the reconstruction. In this paper, we focus on developing a robust multi-agent environment monitoring framework, called self-interested coalitional crowdsensing for multi-agent interactive environment monitoring (SCC-MIE), including environment reconstruction and worker selection. In SCC-MIE, we start from a multi-agent generative adversarial imitation learning framework to introduce a new self-interested coalitional learning strategy, which forges cooperation between a reconstructor and a discriminator to learn the sensing environment together with the hidden confounder while providing interpretability on the results of environment monitoring. Based on this, we utilize the secretary problem to select suitable workers to collect data for accurate environment monitoring in a real-time manner. It is shown that SCC-MIE realizes a significant performance improvement in environment monitoring compared to the existing models. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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16 pages, 14740 KiB  
Article
Simulation Analysis of Sensors with Different Geometry Used in Measurements of Atmospheric Electricity
by Konrad Sobolewski and Marek Kubicki
Sensors 2023, 23(24), 9627; https://doi.org/10.3390/s23249627 - 5 Dec 2023
Viewed by 930
Abstract
The atmospheric electric current, “air–earth current”, flows between the low ionosphere and Earth’s surface. The source of this current is the potential difference between the global equalizing layer called the ionosphere and the ground surface. According to Wilson’s concept of the Earth’s Global [...] Read more.
The atmospheric electric current, “air–earth current”, flows between the low ionosphere and Earth’s surface. The source of this current is the potential difference between the global equalizing layer called the ionosphere and the ground surface. According to Wilson’s concept of the Earth’s Global Electric Circuit, in the areas of so-called fair weather, based on current measurements at the Earth’s surface, it is possible to conclude the global electrical processes in the ionosphere and higher layers. The theoretical basis for this inference is the law of continuity of electric current or the principle of conservation of electric charge. We present the results of simulations of the distribution of electric field lines for sensors with different geometries placed in a uniform electric field, representing the atmospheric electric field. The sensors are metal surfaces on which electric charges are induced or deposited. In the external measuring circuit to which the sensor is connected, an electric current [A] will flow, related to the air–earth current density [A/m2], but their relationship may be challenging to interpret. We analyze the impact of sensor geometry on the possibility of interpreting the atmospheric electric conduction and atmospheric displacement current based on the current measured in the external circuit. This present method can be used for the geometric construction of new sensors at the stage of determining the electrical characteristics of the sensor (e.g., effective collecting area). It can support the comprehensive design of a measurement system at the interface between an atmosphere, sensor, and electronic equipment. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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26 pages, 19925 KiB  
Article
Development of High-Precision Urban Flood-Monitoring Technology for Sustainable Smart Cities
by Bong-Joo Jang and Intaek Jung
Sensors 2023, 23(22), 9167; https://doi.org/10.3390/s23229167 - 14 Nov 2023
Cited by 1 | Viewed by 2424
Abstract
Owing to rapid climate change, large-scale floods have occurred yearly in cities worldwide, causing serious damage. We propose a real-time urban flood-monitoring technology as an urban disaster prevention technology for sustainable and secure smart cities. Our method takes advantage of the characteristic that [...] Read more.
Owing to rapid climate change, large-scale floods have occurred yearly in cities worldwide, causing serious damage. We propose a real-time urban flood-monitoring technology as an urban disaster prevention technology for sustainable and secure smart cities. Our method takes advantage of the characteristic that water flow is regularly detected at a certain distance with a constant Doppler velocity within the radar observation area. Therefore, a pure flow energy detection algorithm in this technology can accurately and immediately detect water flow due to flooding by effectively removing dynamic obstacles such as cars, people, and animals that cause changes in observation distance, and static obstacles that do not cause Doppler velocities. Specifically, in this method, the pure flow energy is detected by generating a two-dimensional range–Doppler relation map using 1 s periodic radar observation data and performing statistical analysis on the energy detected on the successive maps. Experiments to verify the proposed technology are conducted indoors and in real river basins. As a result of conducting experiments in a narrow indoor space that could be considered an urban underpass or underground facility, it was found that this method can detect flooding situations with centimeter-level accuracy by measuring water level and flow velocity in real time from the time of flood occurrence. And the experimental results in various river environments showed that our technology could accurately detect changes in distance and flow speed from the river surface. We also confirmed that this method could effectively eliminate moving obstacles within the observation range and detect only pure flow energy. Finally, we expect that our method will be able to build a high-density urban flood-monitoring network and a high-precision digital flood twin. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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15 pages, 5702 KiB  
Article
Detection of Water on Road Surface with Acoustic Vector Sensor
by Józef Kotus and Grzegorz Szwoch
Sensors 2023, 23(21), 8878; https://doi.org/10.3390/s23218878 - 1 Nov 2023
Viewed by 1326
Abstract
This paper presents a new approach to detecting the presence of water on a road surface, employing an acoustic vector sensor. The proposed method is based on sound intensity analysis in the frequency domain. Acoustic events, representing road vehicles, are detected in the [...] Read more.
This paper presents a new approach to detecting the presence of water on a road surface, employing an acoustic vector sensor. The proposed method is based on sound intensity analysis in the frequency domain. Acoustic events, representing road vehicles, are detected in the sound intensity signals. The direction of the incoming sound is calculated for the individual spectral components of the intensity signal, and the components not originating from the observed road section are discarded. Next, an estimate of the road surface state is calculated from the sound intensity spectrum, and the wet surface detection is performed by comparing the estimate with a threshold. The proposed method was evaluated using sound recordings made in a real-world scenario, and the algorithm results were compared with data from a reference device. The proposed algorithm achieved 89% precision, recall and F1 score, and it outperforms the traditional approach based on sound pressure analysis. The test results confirm that the proposed method may be used for the detection of water on the road surface with acoustic sensors as an element of a smart city monitoring system. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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31 pages, 11430 KiB  
Article
Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques
by Farkhanda Abbas, Feng Zhang, Muhammad Ismail, Garee Khan, Javed Iqbal, Abdulwahed Fahad Alrefaei and Mohammed Fahad Albeshr
Sensors 2023, 23(15), 6843; https://doi.org/10.3390/s23156843 - 1 Aug 2023
Cited by 20 | Viewed by 2641
Abstract
Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of [...] Read more.
Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods such as metaheuristic algorithms, deep learning-based optimization, Bayesian optimization, and quantum optimization, and our paper focused mainly on metaheuristic and Bayesian optimization techniques and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment’s results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For instance, the metaheuristic algorithm boosted the random forest model’s overall accuracy by 5% and 3%, respectively, from baseline optimization methods GS and RS, and by 4% and 2% from baseline optimization methods GA and PSO. Additionally, for models like KNN and SVM, Bayesian methods with Gaussian processes had good results. When compared to the baseline algorithms RS and GS, the accuracy of the KNN model was enhanced by BO-TPE by 1% and 11%, respectively, and by BO-GP by 2% and 12%, respectively. For SVM, BO-TPE outperformed GS and RS by 6% in terms of performance, while BO-GP improved results by 5%. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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24 pages, 1893 KiB  
Article
Calibration and Uncertainty Quantification for Single-Ended Raman-Based Distributed Temperature Sensing: Case Study in a 800 m Deep Coaxial Borehole Heat Exchanger
by Willem Mazzotti Pallard, Alberto Lazzarotto, José Acuña and Björn Palm
Sensors 2023, 23(12), 5498; https://doi.org/10.3390/s23125498 - 11 Jun 2023
Cited by 1 | Viewed by 1572
Abstract
Raman-based distributed temperature sensing (DTS) is a valuable tool for field testing and validating heat transfer models in borehole heat exchanger (BHE) and ground source heat pump (GSHP) applications. However, temperature uncertainty is rarely reported in the literature. In this paper, a new [...] Read more.
Raman-based distributed temperature sensing (DTS) is a valuable tool for field testing and validating heat transfer models in borehole heat exchanger (BHE) and ground source heat pump (GSHP) applications. However, temperature uncertainty is rarely reported in the literature. In this paper, a new calibration method was proposed for single-ended DTS configurations, along with a method to remove fictitious temperature drifts due to ambient air variations. The methods were implemented for a distributed thermal response test (DTRT) case study in an 800 m deep coaxial BHE. The results show that the calibration method and temperature drift correction are robust and give adequate results, with a temperature uncertainty increasing non-linearly from about 0.4 K near the surface to about 1.7 K at 800 m. The temperature uncertainty is dominated by the uncertainty in the calibrated parameters for depths larger than 200 m. The paper also offers insights into thermal features observed during the DTRT, including a heat flux inversion along the borehole depth and the slow temperature homogenization under circulation. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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17 pages, 5473 KiB  
Article
Improving Hazardous Gas Detection Behavior with Palladium Decorated SnO2 Nanobelts Networks
by Estácio P. de Araújo, Murilo P. Paiva, Lucas A. Moisés, Gabriel S. do Espírito Santo, Kate C. Blanco, Adenilson J. Chiquito and Cleber A. Amorim
Sensors 2023, 23(10), 4783; https://doi.org/10.3390/s23104783 - 16 May 2023
Cited by 5 | Viewed by 1544
Abstract
Transparent Conductive Oxides (TCOs) have been widely used as sensors for various hazardous gases. Among the most studied TCOs is SnO2, due to tin being an abundant material in nature, and therefore being accessible for moldable-like nanobelts. Sensors based on SnO [...] Read more.
Transparent Conductive Oxides (TCOs) have been widely used as sensors for various hazardous gases. Among the most studied TCOs is SnO2, due to tin being an abundant material in nature, and therefore being accessible for moldable-like nanobelts. Sensors based on SnO2 nanobelts are generally quantified according to the interaction of the atmosphere with its surface, changing its conductance. The present study reports on the fabrication of a nanobelt-based SnO2 gas sensor, in which electrical contacts to nanobelts are self-assembled, and thus the sensors do not need any expensive and complicated fabrication processes. The nanobelts were grown using the vapor–solid–liquid (VLS) growth mechanism with gold as the catalytic site. The electrical contacts were defined using testing probes, thus the device is considered ready after the growth process. The sensorial characteristics of the devices were tested for the detection of CO and CO2 gases at temperatures from 25 to 75 °C, with and without palladium nanoparticle deposition in a wide concentration range of 40–1360 ppm. The results showed an improvement in the relative response, response time, and recovery, both with increasing temperature and with surface decoration using Pd nanoparticles. These features make this class of sensors important candidates for CO and CO2 detection for human health. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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14 pages, 2769 KiB  
Article
A Honeybee-Inspired Framework for a Smart City Free of Internet Scams
by Abdulghani Ali Ahmed, Ali Al-Bayatti, Mubarak Saif, Waheb A. Jabbar and Taha H. Rassem
Sensors 2023, 23(9), 4284; https://doi.org/10.3390/s23094284 - 26 Apr 2023
Cited by 1 | Viewed by 1978
Abstract
Internet scams are fraudulent attempts aim to lure computer users to reveal their credentials or redirect their connections to spoofed webpages rather than the actual ones. Users’ confidential information, such as usernames, passwords, and financial account numbers, is the main target of these [...] Read more.
Internet scams are fraudulent attempts aim to lure computer users to reveal their credentials or redirect their connections to spoofed webpages rather than the actual ones. Users’ confidential information, such as usernames, passwords, and financial account numbers, is the main target of these fraudulent attempts. Internet scammers often use phishing attacks, which have no boundaries, since they could exceed hijacking conventional cyber ecosystems to hack intelligent systems, which emerged recently for the use within smart cities. This paper therefore develops a real-time framework inspired by the honeybee defense mechanism in nature for filtering phishing website attacks in smart cities. In particular, the proposed framework filters phishing websites through three main phases of investigation: PhishTank-Match (PM), Undesirable-Absent (UA), and Desirable-Present (DP) investigation phases. The PM phase is used at first in order to check whether the requested URL is listed in the blacklist of the PhishTank database. On the other hand, the UA phase is used for investigation and checking for the absence of undesirable symbols in uniform resource locators (URLs) of the requested website. Finally, the DP phase is used as another level of investigation in order to check for the presence of the requested URL in the desirable whitelist. The obtained results show that the proposed framework is deployable and capable of filtering various types of phishing website by maintaining a low rate of false alarms. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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14 pages, 2708 KiB  
Article
Evaluation of Electrical Impedance Spectra by Long Short-Term Memory to Estimate Nitrate Concentrations in Soil
by Xiaohu Ma, Luca Bifano and Gerhard Fischerauer
Sensors 2023, 23(4), 2172; https://doi.org/10.3390/s23042172 - 15 Feb 2023
Cited by 4 | Viewed by 1803
Abstract
Monitoring the nitrate concentration in soil is crucial to guide the use of nitrate-based fertilizers. This study presents the characteristics of an impedance sensor used to estimate the nitrate concentration in soil based on the sensitivity of the soil dielectric constant to ion [...] Read more.
Monitoring the nitrate concentration in soil is crucial to guide the use of nitrate-based fertilizers. This study presents the characteristics of an impedance sensor used to estimate the nitrate concentration in soil based on the sensitivity of the soil dielectric constant to ion conductivity and on electrical double layer effects at electrodes. The impedance of synthetic sandy soil samples with nitrate nitrogen concentrations ranging from 0 to 15 mg/L was measured at frequencies between 20 Hz and 5 kHz and noticeable conductance and susceptance effects were observed. Long short-term memory (LSTM), a variant of recurrent artificial neural networks (RNN), was investigated with respect to its suitability to extract nitrate concentrations from the measured impedance spectra and additional physical properties of the soils, such as mass density and water content. Both random forest and LSTM were tested as feature selection methods. Then, numerous LSTMs were trained to estimate the nitrate concentrations in the soils. To increase estimation accuracy, hyperparameters were optimized with Bayesian optimization. The resulting optimal regression model showed coefficients of determination between true and predicted nitrate concentrations as high as 0.95. Thus, it could be demonstrated that the system has the potential to monitor nitrate concentrations in soils in real time and in situ. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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20 pages, 6704 KiB  
Article
Remote Sensing of Seawater Temperature and Salinity Profiles by the Brillouin Lidar Based on a Fizeau Interferometer and Multichannel Photomultiplier Tube
by Yuanqing Wang, Yangrui Xu, Ping Chen and Kun Liang
Sensors 2023, 23(1), 446; https://doi.org/10.3390/s23010446 - 31 Dec 2022
Cited by 11 | Viewed by 2501
Abstract
Brillouin spectroscopy is a powerful tool to measure the water temperature and salinity profiles of seawater. Considering the insufficiency of the current spectral measurement methods in real-time, spectral integrity, continuity, and stability, we developed a new lidar system for spectrum measurement on an [...] Read more.
Brillouin spectroscopy is a powerful tool to measure the water temperature and salinity profiles of seawater. Considering the insufficiency of the current spectral measurement methods in real-time, spectral integrity, continuity, and stability, we developed a new lidar system for spectrum measurement on an airborne platform that is based on a Fizeau interferometer and multichannel photomultiplier tube. In this approach, the lidar system uses time-of-flight information to measure the depth and relies on Brillouin spectroscopy as the temperature and salinity indicator. In this study, the system parameters were first optimized and analyzed. Based on the analysis results, the performance of the system in terms of detection depth and accuracy was evaluated. The results showed that this method has strong anti-interference ability, and under a temperature measurement accuracy of 0.5 °C and a salinity measurement accuracy of 1‰, the effective detection depth exceeds 40.51 m. Therefore, the proposed method performs well and will be a good choice for achieving Brillouin lidar application in seawater remote sensing. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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14 pages, 2270 KiB  
Article
Monitoring Vehicle Pollution and Fuel Consumption Based on AI Camera System and Gas Emission Estimator Model
by Manuel Rodriguez Valido, Oscar Gomez-Cardenes and Eduardo Magdaleno
Sensors 2023, 23(1), 312; https://doi.org/10.3390/s23010312 - 28 Dec 2022
Cited by 5 | Viewed by 5274
Abstract
Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to [...] Read more.
Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the transport sector. Countries consider emission inventories to be important for assessing emission levels in order to identify air quality and to further contribute in this field to reduce hazardous emissions that affect human health and the environment. The main goal of this work is to design and implement an artificial intelligence-based (AI) system to estimate pollution and consumption of real-world traffic roads. The system is a pipeline structure that is comprised of three fundamental blocks: classification and localisation, screen coordinates to world coordinates transform and emission estimation. The authors propose a novel system that combines existing technologies, such as convolutional neural networks and emission models, to enable a camera to be an emission detector. Compared with other real-world emission measurement methods (LIDAR, speed and acceleration sensors, weather sensors and cameras), our system integrates all measurements into a single sensor: the camera combined with a processing unit. The system was tested on a ground truth dataset. The speed estimation obtained from our AI algorithm is compared with real data measurements resulting in a 5.59% average error. Then these estimations are fed to a model to understand how the errors propagate. This yielded an average error of 12.67% for emitted particle matter, 19.57% for emitted gases and 5.48% for consumed fuel and energy. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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14 pages, 6330 KiB  
Article
A Novel Paper-Based Reagentless Dual Functional Soil Test to Instantly Detect Phosphate Infield
by Reem Zeitoun, Viacheslav Adamchuk and Asim Biswas
Sensors 2022, 22(22), 8803; https://doi.org/10.3390/s22228803 - 14 Nov 2022
Cited by 2 | Viewed by 2124
Abstract
Soil tests for plant-available phosphorus (P) are suggested to provide offsite P analysis required to monitor P fertilizer application and reduce P losses to downstream water. However, procedural and cost limitations of current soil phosphate tests have restricted their widespread use and have [...] Read more.
Soil tests for plant-available phosphorus (P) are suggested to provide offsite P analysis required to monitor P fertilizer application and reduce P losses to downstream water. However, procedural and cost limitations of current soil phosphate tests have restricted their widespread use and have made them accessible only in laboratories. This study proposes a novel paper-based reagentless electrochemical soil phosphate sensor to extract and detect soil phosphate using an inexpensive and simple approach. In this test, concentrated Mehlich-3 and molybdate ions were impregnated in filter paper, which served as the phosphate extraction and reaction zone, and was followed by electrochemical detection using cyclic voltammetry signals. Soil samples from 22 sampling sites were used to validate this method against inductively coupled plasma optical emission spectroscopy (ICP) soil phosphate tests. Regression and correlation analyses showed a significant relationship between phosphate determinations by ICP and the proposed method, delivering a correlation coefficient, r, of 0.98 and a correlation slope of 1.02. The proposed approach provided a fast, portable, low-cost, accessible, reliable, and single-step test to extract and detect phosphate simultaneously with minimum waste (0.5 mL per sample), which made phosphate characterization possible in the field. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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15 pages, 2782 KiB  
Communication
LZER0: A Cost-Effective Multi-Purpose GNSS Platform
by David Zuliani, Lavinia Tunini, Marco Severin, Michele Bertoni, Cristian Ponton and Stefano Parolai
Sensors 2022, 22(21), 8314; https://doi.org/10.3390/s22218314 - 29 Oct 2022
Cited by 3 | Viewed by 3068
Abstract
Recent advances in Global Navigation Satellite System (GNSS) technology have made low-cost sensors available to the mass market, opening up new opportunities for real-time ground deformation and structure monitoring. In this paper, we present a new product developed in this framework by the [...] Read more.
Recent advances in Global Navigation Satellite System (GNSS) technology have made low-cost sensors available to the mass market, opening up new opportunities for real-time ground deformation and structure monitoring. In this paper, we present a new product developed in this framework by the National Institute of Oceanography and Applied Geophysics–OGS in collaboration with a private company (SoluTOP SAS): a cost-effective, multi-purpose GNSS platform called LZER0, suitable not only for surveying measurements, but also for monitoring tasks. The LZER0 platform is a complete system that includes the GNSS equipment (M8T single-frequency model produced by u-blox) and the web portal where the results are displayed. The GNSS data are processed using the RTKLIB software package, and the processed results are made available to the end user. The relative positioning mode was adopted both with real-time and post-processing RTKLIB engines. We present three applications of LZER0—cadastral, monitoring, and automotive—which demonstrate that it is a flexible, multi-purpose platform that is easy to use in terms of both hardware and software, and can be easily deployed to perform various tasks in the research, educational, or professional sectors. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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26 pages, 1516 KiB  
Article
A Comprehensive Collection and Analysis Model for the Drone Forensics Field
by Fahad Mazaed Alotaibi, Arafat Al-Dhaqm, Yasser D. Al-Otaibi and Abdulrahman A. Alsewari
Sensors 2022, 22(17), 6486; https://doi.org/10.3390/s22176486 - 29 Aug 2022
Cited by 12 | Viewed by 3759
Abstract
Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by [...] Read more.
Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by drone-based evidence. Drone-based evidence collection and preservation entails accumulating and collecting digital evidence from the drone of the victim for subsequent analysis and presentation. Digital evidence must, however, be collected and analyzed in a forensically sound manner using the appropriate collection and analysis methodologies and tools to preserve the integrity of the evidence. For this purpose, various collection and analysis models have been proposed for drone forensics based on the existing literature; several models are inclined towards specific scenarios and drone systems. As a result, the literature lacks a suitable and standardized drone-based collection and analysis model devoid of commonalities, which can solve future problems that may arise in the drone forensics field. Therefore, this paper has three contributions: (a) studies the machine learning existing in the literature in the context of handling drone data to discover criminal actions, (b) highlights the existing forensic models proposed for drone forensics, and (c) proposes a novel comprehensive collection and analysis forensic model (CCAFM) applicable to the drone forensics field using the design science research approach. The proposed CCAFM consists of three main processes: (1) acquisition and preservation, (2) reconstruction and analysis, and (3) post-investigation process. CCAFM contextually leverages the initially proposed models herein incorporated in this study. CCAFM allows digital forensic investigators to collect, protect, rebuild, and examine volatile and nonvolatile items from the suspected drone based on scientific forensic techniques. Therefore, it enables sharing of knowledge on drone forensic investigation among practitioners working in the forensics domain. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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Review

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37 pages, 2347 KiB  
Review
Review of Learning-Based Robotic Manipulation in Cluttered Environments
by Marwan Qaid Mohammed, Lee Chung Kwek, Shing Chyi Chua, Arafat Al-Dhaqm, Saeid Nahavandi, Taiseer Abdalla Elfadil Eisa, Muhammad Fahmi Miskon, Mohammed Nasser Al-Mhiqani, Abdulalem Ali, Mohammed Abaker and Esmail Ali Alandoli
Sensors 2022, 22(20), 7938; https://doi.org/10.3390/s22207938 - 18 Oct 2022
Cited by 19 | Viewed by 5368
Abstract
Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous [...] Read more.
Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Environment Monitoring)
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