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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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71 pages, 15226 KiB  
Review
Applications of Graphene Quantum Dots in Biomedical Sensors
by Bhargav D. Mansuriya and Zeynep Altintas
Sensors 2020, 20(4), 1072; https://doi.org/10.3390/s20041072 - 16 Feb 2020
Cited by 196 | Viewed by 18828
Abstract
Due to the proliferative cancer rates, cardiovascular diseases, neurodegenerative disorders, autoimmune diseases and a plethora of infections across the globe, it is essential to introduce strategies that can rapidly and specifically detect the ultralow concentrations of relevant biomarkers, pathogens, toxins and pharmaceuticals in [...] Read more.
Due to the proliferative cancer rates, cardiovascular diseases, neurodegenerative disorders, autoimmune diseases and a plethora of infections across the globe, it is essential to introduce strategies that can rapidly and specifically detect the ultralow concentrations of relevant biomarkers, pathogens, toxins and pharmaceuticals in biological matrices. Considering these pathophysiologies, various research works have become necessary to fabricate biosensors for their early diagnosis and treatment, using nanomaterials like quantum dots (QDs). These nanomaterials effectively ameliorate the sensor performance with respect to their reproducibility, selectivity as well as sensitivity. In particular, graphene quantum dots (GQDs), which are ideally graphene fragments of nanometer size, constitute discrete features such as acting as attractive fluorophores and excellent electro-catalysts owing to their photo-stability, water-solubility, biocompatibility, non-toxicity and lucrativeness that make them favorable candidates for a wide range of novel biomedical applications. Herein, we reviewed about 300 biomedical studies reported over the last five years which entail the state of art as well as some pioneering ideas with respect to the prominent role of GQDs, especially in the development of optical, electrochemical and photoelectrochemical biosensors. Additionally, we outline the ideal properties of GQDs, their eclectic methods of synthesis, and the general principle behind several biosensing techniques. Full article
(This article belongs to the Special Issue Nanoimmunosensor)
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24 pages, 9732 KiB  
Article
Evaluation of HoloLens Tracking and Depth Sensing for Indoor Mapping Applications
by Patrick Hübner, Kate Clintworth, Qingyi Liu, Martin Weinmann and Sven Wursthorn
Sensors 2020, 20(4), 1021; https://doi.org/10.3390/s20041021 - 14 Feb 2020
Cited by 120 | Viewed by 15248
Abstract
The Microsoft HoloLens is a head-worn mobile augmented reality device that is capable of mapping its direct environment in real-time as triangle meshes and localize itself within these three-dimensional meshes simultaneously. The device is equipped with a variety of sensors including four tracking [...] Read more.
The Microsoft HoloLens is a head-worn mobile augmented reality device that is capable of mapping its direct environment in real-time as triangle meshes and localize itself within these three-dimensional meshes simultaneously. The device is equipped with a variety of sensors including four tracking cameras and a time-of-flight (ToF) range camera. Sensor images and their poses estimated by the built-in tracking system can be accessed by the user. This makes the HoloLens potentially interesting as an indoor mapping device. In this paper, we introduce the different sensors of the device and evaluate the complete system in respect of the task of mapping indoor environments. The overall quality of such a system depends mainly on the quality of the depth sensor together with its associated pose derived from the tracking system. For this purpose, we first evaluate the performance of the HoloLens depth sensor and its tracking system separately. Finally, we evaluate the overall system regarding its capability for mapping multi-room environments. Full article
(This article belongs to the Special Issue Sensors for Construction Automation and Management)
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21 pages, 2853 KiB  
Article
Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection
by Thomas Kundinger, Nikoletta Sofra and Andreas Riener
Sensors 2020, 20(4), 1029; https://doi.org/10.3390/s20041029 - 14 Feb 2020
Cited by 83 | Viewed by 10437
Abstract
Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to [...] Read more.
Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving. Full article
(This article belongs to the Special Issue Human-Machine Interaction and Sensors)
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48 pages, 7736 KiB  
Review
IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture
by Laura García, Lorena Parra, Jose M. Jimenez, Jaime Lloret and Pascal Lorenz
Sensors 2020, 20(4), 1042; https://doi.org/10.3390/s20041042 - 14 Feb 2020
Cited by 472 | Viewed by 73337
Abstract
Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability [...] Read more.
Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems. Full article
(This article belongs to the Special Issue Surveys of Sensor Networks and Sensor Systems Deployments)
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19 pages, 2612 KiB  
Review
Noble Metal-Assisted Surface Plasmon Resonance Immunosensors
by Jin-Ha Choi, Jin-Ho Lee, Joohyung Son and Jeong-Woo Choi
Sensors 2020, 20(4), 1003; https://doi.org/10.3390/s20041003 - 13 Feb 2020
Cited by 42 | Viewed by 7970
Abstract
For the early diagnosis of several diseases, various biomarkers have been discovered and utilized through the measurement of concentrations in body fluids such as blood, urine, and saliva. The most representative analytical method for biomarker detection is an immunosensor, which exploits the specific [...] Read more.
For the early diagnosis of several diseases, various biomarkers have been discovered and utilized through the measurement of concentrations in body fluids such as blood, urine, and saliva. The most representative analytical method for biomarker detection is an immunosensor, which exploits the specific antigen-antibody immunoreaction. Among diverse analytical methods, surface plasmon resonance (SPR)-based immunosensors are emerging as a potential detection platform due to high sensitivity, selectivity, and intuitive features. Particularly, SPR-based immunosensors could detect biomarkers without labeling of a specific detection probe, as typical immunosensors such as enzyme-linked immunosorbent assay (ELISA) use enzymes like horseradish peroxidase (HRP). In this review, SPR-based immunosensors utilizing noble metals such as Au and Ag as SPR-inducing factors for the measurement of different types of protein biomarkers, including viruses, microbes, and extracellular vesicles (EV), are briefly introduced. Full article
(This article belongs to the Special Issue Optical Immunosensors)
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21 pages, 11078 KiB  
Article
Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images
by Adrián Colomer, Jorge Igual and Valery Naranjo
Sensors 2020, 20(4), 1005; https://doi.org/10.3390/s20041005 - 13 Feb 2020
Cited by 71 | Viewed by 7222
Abstract
Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus [...] Read more.
Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Sensors)
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16 pages, 3288 KiB  
Article
Processing of Near Real Time Land Surface Temperature and Its Application in Forecasting Forest Fire Danger Conditions
by M. Razu Ahmed, Quazi K. Hassan, Masoud Abdollahi and Anil Gupta
Sensors 2020, 20(4), 984; https://doi.org/10.3390/s20040984 - 12 Feb 2020
Cited by 16 | Viewed by 3901
Abstract
Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Space-based instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users [...] Read more.
Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Space-based instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users by LANCE (Land, Atmosphere Near real-time Capability) for Earth Observing System (EOS) of NASA (National Aeronautics and Space Administration) free of cost. Such Ts products are swath data with 5 min temporal increments of satellite acquisition, and the average latency is 60-125 min to be available in public domain. The swath data of Ts requires a specialized tool, i.e., HEG (HDF-EOS to GeoTIFF conversion tool) to process and make the data useful for further analysis. However, the file naming convention of the available swath data files in LANCE is not appropriate to download for an area of interest (AOI) to be processed by HEG. In this study, we developed a method/algorithm to overcome such issues in identifying the appropriate swath data files for an AOI that would be able to further processes supported by the HEG. In this case, we used Terra MODIS acquired NRT swath data of Ts, and further applied it to an existing framework of forecasting forest fires (as a case study) for the performance evaluation of our processed Ts. We were successful in selecting appropriate swath data files of Ts for our study area that was further processed by HEG, and finally were able to generate fire danger map in the existing forecasting model. Our proposed method/algorithm could be applied on any swath data product available in LANCE for any location in the world. Full article
(This article belongs to the Special Issue Remote Sensing and Geoinformatics in Wildfire Management)
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22 pages, 2916 KiB  
Article
IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services
by Tarek Elsaleh, Shirin Enshaeifar, Roonak Rezvani, Sahr Thomas Acton, Valentinas Janeiko and Maria Bermudez-Edo
Sensors 2020, 20(4), 953; https://doi.org/10.3390/s20040953 - 11 Feb 2020
Cited by 74 | Viewed by 9793
Abstract
With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic [...] Read more.
With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT. Full article
(This article belongs to the Special Issue Selected Papers from the 3rd Global IoT Summit)
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15 pages, 2277 KiB  
Review
Electrochemical Paper-Based Biosensor Devices for Rapid Detection of Biomarkers
by Manuel Gutiérrez-Capitán, Antonio Baldi and César Fernández-Sánchez
Sensors 2020, 20(4), 967; https://doi.org/10.3390/s20040967 - 11 Feb 2020
Cited by 63 | Viewed by 8820
Abstract
In healthcare, new diagnostic tools that help in the diagnosis, prognosis, and monitoring of diseases rapidly and accurately are in high demand. For in-situ measurement of disease or infection biomarkers, point-of-care devices provide a dramatic speed advantage over conventional techniques, thus aiding clinicians [...] Read more.
In healthcare, new diagnostic tools that help in the diagnosis, prognosis, and monitoring of diseases rapidly and accurately are in high demand. For in-situ measurement of disease or infection biomarkers, point-of-care devices provide a dramatic speed advantage over conventional techniques, thus aiding clinicians in decision-making. During the last decade, paper-based analytical devices, combining paper substrates and electrochemical detection components, have emerged as important point-of-need diagnostic tools. This review highlights significant works on this topic over the last five years, from 2015 to 2019. The most relevant articles published in 2018 and 2019 are examined in detail, focusing on device fabrication techniques and materials applied to the production of paper fluidic and electrochemical cell architectures as well as on the final device assembly. Two main approaches were identified, that are, on one hand, those ones where the fabrication of the electrochemical cell is done on the paper substrate, where the fluidic structures are also defined, and, on the other hand, the fabrication of those ones where the electrochemical cell and liquid-driving paper component are defined on different substrates and then heterogeneously assembled. The main limitations of the current technologies are outlined and an outlook on the current technology status and future prospects is given. Full article
(This article belongs to the Special Issue Electrochemical Nanobiosensors)
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39 pages, 3192 KiB  
Review
Deep Learning in Physiological Signal Data: A Survey
by Beanbonyka Rim, Nak-Jun Sung, Sedong Min and Min Hong
Sensors 2020, 20(4), 969; https://doi.org/10.3390/s20040969 - 11 Feb 2020
Cited by 174 | Viewed by 18178
Abstract
Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to [...] Read more.
Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources. Full article
(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)
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19 pages, 1741 KiB  
Article
Secure Authentication and Credential Establishment in Narrowband IoT and 5G
by Jesus Sanchez-Gomez, Dan Garcia-Carrillo, Rafael Marin-Perez and Antonio F. Skarmeta
Sensors 2020, 20(3), 882; https://doi.org/10.3390/s20030882 - 7 Feb 2020
Cited by 27 | Viewed by 5799
Abstract
Security is critical in the deployment and maintenance of novel IoT and 5G networks. The process of bootstrapping is required to establish a secure data exchange between IoT devices and data-driven platforms. It entails, among other steps, authentication, authorization, and credential management. Nevertheless, [...] Read more.
Security is critical in the deployment and maintenance of novel IoT and 5G networks. The process of bootstrapping is required to establish a secure data exchange between IoT devices and data-driven platforms. It entails, among other steps, authentication, authorization, and credential management. Nevertheless, there are few efforts dedicated to providing service access authentication in the area of constrained IoT devices connected to recent wireless networks such as narrowband IoT (NB-IoT) and 5G. Therefore, this paper presents the adaptation of bootstrapping protocols to be compliant with the 3GPP specifications in order to enable the 5G feature of secondary authentication for constrained IoT devices. To allow the secondary authentication and key establishment in NB-IoT and 4G/5G environments, we have adapted two Extensible Authentication Protocol (EAP) lower layers, i.e., PANATIKI and LO-CoAP-EAP. In fact, this approach presents the evaluation of both aforementioned EAP lower layers, showing the contrast between a current EAP lower layer standard, i.e., PANA, and one specifically designed with the constraints of IoT, thus providing high flexibility and scalability in the bootstrapping process in 5G networks. The proposed solution is evaluated to prove its efficiency and feasibility, being one of the first efforts to support secure service authentication and key establishment for constrained IoT devices in 5G environments. Full article
(This article belongs to the Special Issue Selected Papers from the 3rd Global IoT Summit)
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11 pages, 3461 KiB  
Article
Modelling Remote Sensing Reflectance to Detect Dispersed Oil at Sea
by Emilia Baszanowska, Zbigniew Otremba and Jacek Piskozub
Sensors 2020, 20(3), 863; https://doi.org/10.3390/s20030863 - 6 Feb 2020
Cited by 18 | Viewed by 3197
Abstract
This paper presents a model of upwelling radiation above the seawater surface in the event of a threat of dispersed oil. The Monte Carlo method was used to simulate a large number of solar photons in the water, eventually obtaining values of remote [...] Read more.
This paper presents a model of upwelling radiation above the seawater surface in the event of a threat of dispersed oil. The Monte Carlo method was used to simulate a large number of solar photons in the water, eventually obtaining values of remote sensing reflectance (Rrs). Analyses were performed for the optical properties of seawater characteristic for the Gulf of Gdańsk (southern Baltic Sea). The case of seawater contaminated by dispersed oil at a concentration of 10 ppm was also discussed for different wind speeds. Two types of oils with extremely different optical properties (refraction and absorption coefficients) were taken into account for consideration. The optical properties (absorption and scattering coefficients and angular light scattering distribution) of the oil-in-water dispersion system were determined using the Mie theory. The spectral index for oil detection in seawater for different wind conditions was determined based on the results obtained for reflectance at selected wavelengths in the range 412–676 nm. The determined spectral index for seawater free of oil achieves higher values for seawater contaminated by oil. The analysis of the values of the spectral indices calculated for 28 combinations of wavelengths was used to identify the most universal spectral index of Rrs for 555 nm/440 nm for dispersed oil detection using any optical parameters. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)
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43 pages, 2321 KiB  
Article
5G support for Industrial IoT Applications— Challenges, Solutions, and Research gaps
by Pal Varga, Jozsef Peto, Attila Franko, David Balla, David Haja, Ferenc Janky, Gabor Soos, Daniel Ficzere, Markosz Maliosz and Laszlo Toka
Sensors 2020, 20(3), 828; https://doi.org/10.3390/s20030828 - 4 Feb 2020
Cited by 213 | Viewed by 30545
Abstract
Industrial IoT has special communication requirements, including high reliability, low latency, flexibility, and security. These are instinctively provided by the 5G mobile technology, making it a successful candidate for supporting Industrial IoT (IIoT) scenarios. The aim of this paper is to identify current [...] Read more.
Industrial IoT has special communication requirements, including high reliability, low latency, flexibility, and security. These are instinctively provided by the 5G mobile technology, making it a successful candidate for supporting Industrial IoT (IIoT) scenarios. The aim of this paper is to identify current research challenges and solutions in relation to 5G-enabled Industrial IoT, based on the initial requirements and promises of both domains. The methodology of the paper follows the steps of surveying state-of-the art, comparing results to identify further challenges, and drawing conclusions as lessons learned for each research domain. These areas include IIoT applications and their requirements; mobile edge cloud; back-end performance tuning; network function virtualization; and security, blockchains for IIoT, Artificial Intelligence support for 5G, and private campus networks. Beside surveying the current challenges and solutions, the paper aims to provide meaningful comparisons for each of these areas (in relation to 5G-enabled IIoT) to draw conclusions on current research gaps. Full article
(This article belongs to the Section Internet of Things)
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16 pages, 12224 KiB  
Article
Accuracy of Trajectory Tracking Based on Nonlinear Guidance Logic for Hydrographic Unmanned Surface Vessels
by Andrzej Stateczny, Pawel Burdziakowski, Klaudia Najdecka and Beata Domagalska-Stateczna
Sensors 2020, 20(3), 832; https://doi.org/10.3390/s20030832 - 4 Feb 2020
Cited by 36 | Viewed by 4746
Abstract
A new trend in recent years for hydrographic measurement in water bodies is the use of unmanned surface vehicles (USVs). In the process of navigation by USVs, it is particularly important to control position precisely on the measuring profile. Precise navigation with respect [...] Read more.
A new trend in recent years for hydrographic measurement in water bodies is the use of unmanned surface vehicles (USVs). In the process of navigation by USVs, it is particularly important to control position precisely on the measuring profile. Precise navigation with respect to the measuring profile avoids registration of redundant data and thus saves time and survey costs. This article addresses the issue of precise navigation of the hydrographic unit on the measuring profile with the use of a nonlinear adaptive autopilot. The results of experiments concerning hydrographic measurements performed in real conditions using an USV are discussed. Full article
(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)
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25 pages, 10331 KiB  
Article
Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
by Matej Bažec, Franc Dimc and Polona Pavlovčič-Prešeren
Sensors 2020, 20(3), 814; https://doi.org/10.3390/s20030814 - 3 Feb 2020
Cited by 19 | Viewed by 5488
Abstract
Understanding the factors that might intentionally influence the reception of global navigation satellite system (GNSS) signals can be a challenging topic today. The focus of this research is to evaluate the vulnerability of geodetic GNSS receivers under the use of a low-cost L1/E1 [...] Read more.
Understanding the factors that might intentionally influence the reception of global navigation satellite system (GNSS) signals can be a challenging topic today. The focus of this research is to evaluate the vulnerability of geodetic GNSS receivers under the use of a low-cost L1/E1 frequency jammer. A suitable area for testing was established in Slovenia. Nine receivers from different manufacturers were under consideration in this study. While positioning, intentional 3-minute jammings were performed by a jammer that was located statically at different distances from receivers. Furthermore, kinematic disturbances were performed using a jammer placed in a vehicle that passed the testing area at various speeds. An analysis of different scenarios indicated that despite the use of an L1/E1 jammer, the GLONASS (Russian: Globalnaya Navigatsionnaya Sputnikovaya Sistema) and Galileo signals were also affected, either due to the increased carrier-to-noise-ratio (C/N0) or, in the worst cases, by a loss-of-signal. A jammer could substantially affect the position, either with a lack of any practical solution or even with a wrong position. Maximal errors in the carrier-phase positions, which should be considered a concern for geodesy, differed by a few metres from the exact solution. The factor that completely disabled the signal reception was the proximity of a jammer, regardless of its static or kinematic mode. Full article
(This article belongs to the Special Issue GNSS Data Processing and Navigation)
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18 pages, 1302 KiB  
Article
Detection and Mitigation of DoS and DDoS Attacks in IoT-Based Stateful SDN: An Experimental Approach
by Jesús Galeano-Brajones, Javier Carmona-Murillo, Juan F. Valenzuela-Valdés and Francisco Luna-Valero
Sensors 2020, 20(3), 816; https://doi.org/10.3390/s20030816 - 3 Feb 2020
Cited by 127 | Viewed by 15433
Abstract
The expected advent of the Internet of Things (IoT) has triggered a large demand of embedded devices, which envisions the autonomous interaction of sensors and actuators while offering all sort of smart services. However, these IoT devices are limited in computation, storage, and [...] Read more.
The expected advent of the Internet of Things (IoT) has triggered a large demand of embedded devices, which envisions the autonomous interaction of sensors and actuators while offering all sort of smart services. However, these IoT devices are limited in computation, storage, and network capacity, which makes them easy to hack and compromise. To achieve secure development of IoT, it is necessary to engineer scalable security solutions optimized for the IoT ecosystem. To this end, Software Defined Networking (SDN) is a promising paradigm that serves as a pillar in the fifth generation of mobile systems (5G) that could help to detect and mitigate Denial of Service (DoS) and Distributed DoS (DDoS) threats. In this work, we propose to experimentally evaluate an entropy-based solution to detect and mitigate DoS and DDoS attacks in IoT scenarios using a stateful SDN data plane. The obtained results demonstrate for the first time the effectiveness of this technique targeting real IoT data traffic. Full article
(This article belongs to the Special Issue Monitoring of Security Properties in IoT and Emerging Technologies)
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18 pages, 817 KiB  
Article
IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings
by Raffaele Carli, Graziana Cavone, Sarah Ben Othman and Mariagrazia Dotoli
Sensors 2020, 20(3), 781; https://doi.org/10.3390/s20030781 - 31 Jan 2020
Cited by 109 | Viewed by 17618
Abstract
The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control [...] Read more.
The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach. Full article
(This article belongs to the Special Issue Sensor Based Smart Grid in Internet of Things Era)
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24 pages, 1716 KiB  
Article
Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine
by Robert Czabanski, Krzysztof Horoba, Janusz Wrobel, Adam Matonia, Radek Martinek, Tomasz Kupka, Michal Jezewski, Radana Kahankova, Janusz Jezewski and Jacek M. Leski
Sensors 2020, 20(3), 765; https://doi.org/10.3390/s20030765 - 30 Jan 2020
Cited by 60 | Viewed by 12093
Abstract
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient [...] Read more.
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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39 pages, 3130 KiB  
Review
Sensors to Increase the Security of Underwater Communication Cables: A Review of Underwater Monitoring Sensors
by Dimitrios Eleftherakis and Raul Vicen-Bueno
Sensors 2020, 20(3), 737; https://doi.org/10.3390/s20030737 - 29 Jan 2020
Cited by 40 | Viewed by 16145
Abstract
Underwater communication cables transport large amounts of sensitive information between countries. This fact converts these cables into a critical infrastructure that must be protected. Monitoring the underwater cable environment is rare and any intervention is usually driven by cable faults. In the last [...] Read more.
Underwater communication cables transport large amounts of sensitive information between countries. This fact converts these cables into a critical infrastructure that must be protected. Monitoring the underwater cable environment is rare and any intervention is usually driven by cable faults. In the last few years, several reports raised issues about possible future malicious attacks on such cables. The main objective of this operational research and analysis (ORA) paper is to present an overview of different commercial and already available marine sensor technologies (acoustic, optic, magnetic and oceanographic) that could be used for autonomous monitoring of the underwater cable environment. These sensors could be mounted on different autonomous platforms, such as unmanned surface vehicles (USVs) or autonomous underwater vehicles (AUVs). This paper analyses a multi-threat sabotage scenario where surveying a transatlantic cable of 13,000 km, (reaching water depths up to 4000 m) is necessary. The potential underwater threats identified for such a scenario are: divers, anchors, fishing trawls, submarines, remotely operated vehicles (ROVs) and AUVs. The paper discusses the capabilities of the identified sensors to detect such identified threats for the scenario under study. It also presents ideas on the construction of periodic and permanent surveillance networks. Research study and results are focused on providing useful information to decision-makers in charge of designing surveillance capabilities to secure underwater communication cables. Full article
(This article belongs to the Special Issue Sensor Applications on Marine Recognition)
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16 pages, 3157 KiB  
Article
Multi-Parameter Sensing in a Multimode Self-Interference Micro-Ring Resonator by Machine Learning
by Dong Hu, Chang-ling Zou, Hongliang Ren, Jin Lu, Zichun Le, Yali Qin, Shunqin Guo, Chunhua Dong and Weisheng Hu
Sensors 2020, 20(3), 709; https://doi.org/10.3390/s20030709 - 28 Jan 2020
Cited by 30 | Viewed by 6138
Abstract
A universal multi-parameter sensing scheme based on a self-interference micro-ring resonator (SIMRR) is proposed. Benefit from the special intensity sensing mechanism, the SIMRR allows multimode sensing in a wide range of wavelengths but immune from frequency noise. To process the multiple mode spectra [...] Read more.
A universal multi-parameter sensing scheme based on a self-interference micro-ring resonator (SIMRR) is proposed. Benefit from the special intensity sensing mechanism, the SIMRR allows multimode sensing in a wide range of wavelengths but immune from frequency noise. To process the multiple mode spectra that are dependent on multiple parameters, we adopt the machine learning algorithm instead of massive asymptotic solutions of resonators. Employing the proposed multi-mode sensing approach, a two-parameter SIMRR sensor is designed. Assuming that two gases have different wavelength dependence of refractive indices, the feasibility and effectiveness of the two-parameter sensing strategy are verified numerically. Moreover, the dependence of parameter estimation accuracy on the laser intensity noises is also investigated. The numerical results indicate that our scheme of multi-parameter sensing in a multimode SIMRR holds great potential for practical high-sensitive sensing platforms compared with the single-mode sensing based on whispering gallery mode (WGM) resonators. Full article
(This article belongs to the Special Issue Optical–Resonant Microsensors)
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25 pages, 11187 KiB  
Article
Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
by Vanesa Lopez-Vazquez, Jose Manuel Lopez-Guede, Simone Marini, Emanuela Fanelli, Espen Johnsen and Jacopo Aguzzi
Sensors 2020, 20(3), 726; https://doi.org/10.3390/s20030726 - 28 Jan 2020
Cited by 57 | Viewed by 9053
Abstract
An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, [...] Read more.
An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%. Full article
(This article belongs to the Special Issue Imaging Sensor Systems for Analyzing Subsea Environment and Life)
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12 pages, 2335 KiB  
Article
Light Drone-Based Application to Assess Soil Tillage Quality Parameters
by Roberto Fanigliulo, Francesca Antonucci, Simone Figorilli, Daniele Pochi, Federico Pallottino, Laura Fornaciari, Renato Grilli and Corrado Costa
Sensors 2020, 20(3), 728; https://doi.org/10.3390/s20030728 - 28 Jan 2020
Cited by 23 | Viewed by 5612
Abstract
The evaluation of soil tillage quality parameters, such as cloddiness and surface roughness produced by tillage tools, is based on traditional methods ranging, respectively, from manual or mechanical sieving of ground samples to handheld rulers, non-contact devices or Precision Agriculture technics, such as [...] Read more.
The evaluation of soil tillage quality parameters, such as cloddiness and surface roughness produced by tillage tools, is based on traditional methods ranging, respectively, from manual or mechanical sieving of ground samples to handheld rulers, non-contact devices or Precision Agriculture technics, such as laser profile meters. The aim of the study was to compare traditional methods of soil roughness and cloddiness assessment (laser profile meter and manual sieving), with light drone RGB 3D imaging techniques for the evaluation of different tillage methods (ploughed, harrowed and grassed). Light drone application was able to replicate the results obtained by the traditional methods, introducing advantages in terms of time, repeatability and analysed surface while reducing the human error during the data collection on the one hand and allowing a labour-intensive field monitoring solution for digital farming on the other. Indeed, the profilometer positioning introduces errors and may lead to false reading due to limited data collection. Future work could be done in order to streamline the data processing operation and so to produce a practical application ready to use and stimulate the adoption of new evaluation indices of soil cloddiness, such as Entropy and the Angular Second Moment (ASM), which seem more suitable than the classic ones to achieved data referred to more extended surfaces. Full article
(This article belongs to the Section Optical Sensors)
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25 pages, 3135 KiB  
Article
Mobile Synchronization Recovery for Ultrasonic Indoor Positioning
by Riccardo Carotenuto, Massimo Merenda, Demetrio Iero and Francesco G. Della Corte
Sensors 2020, 20(3), 702; https://doi.org/10.3390/s20030702 - 27 Jan 2020
Cited by 29 | Viewed by 4135
Abstract
The growing interest for indoor position-based applications and services, as well as ubiquitous computing and location aware information, have led to increasing efforts toward the development of positioning techniques. Many applications require accurate positioning or tracking of people and assets inside buildings, and [...] Read more.
The growing interest for indoor position-based applications and services, as well as ubiquitous computing and location aware information, have led to increasing efforts toward the development of positioning techniques. Many applications require accurate positioning or tracking of people and assets inside buildings, and some market sectors are waiting for such technologies for starting a fast growth. Ultrasonic systems have already been shown to possess the desired positioning accuracy and refresh rate. However, they still require accurate synchronization between ultrasound emitters and receivers to work properly. Usually, synchronization is carried out through radio frequency (RF) signals, adding system complexity and raising the cost. In this work, this limit is overcome by introducing a novel self-synchronizing indoor positioning technique. Ultrasonic signals travel from emitters placed at fixed reference positions to any number of mobile devices (MD). The travelled distance is computed from the time of flight (TOF), which requires in turn synchronism between emitter and receiver. It is shown that this synchronism can be indirectly estimated from the time difference of arrival (TDOA) of the ultrasonic signals. The obtained positioning information is private, in the sense that the positioning infrastructure is not aware of the number or identity of the MDs that use it. Computer simulations and experimental results obtained in a typical office room are provided. Full article
(This article belongs to the Section Electronic Sensors)
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15 pages, 5221 KiB  
Article
Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals
by Lin Chen, Jianting Fu, Yuheng Wu, Haochen Li and Bin Zheng
Sensors 2020, 20(3), 672; https://doi.org/10.3390/s20030672 - 26 Jan 2020
Cited by 168 | Viewed by 12454
Abstract
By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of [...] Read more.
By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results. Full article
(This article belongs to the Special Issue Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments)
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23 pages, 565 KiB  
Review
Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review
by Ive Weygers, Manon Kok, Marco Konings, Hans Hallez, Henri De Vroey and Kurt Claeys
Sensors 2020, 20(3), 673; https://doi.org/10.3390/s20030673 - 26 Jan 2020
Cited by 114 | Viewed by 12772
Abstract
The use of inertial measurement units (IMUs) has gained popularity for the estimation of lower limb kinematics. However, implementations in clinical practice are still lacking. The aim of this review is twofold—to evaluate the methodological requirements for IMU-based joint kinematic estimation to be [...] Read more.
The use of inertial measurement units (IMUs) has gained popularity for the estimation of lower limb kinematics. However, implementations in clinical practice are still lacking. The aim of this review is twofold—to evaluate the methodological requirements for IMU-based joint kinematic estimation to be applicable in a clinical setting, and to suggest future research directions. Studies within the PubMed, Web Of Science and EMBASE databases were screened for eligibility, based on the following inclusion criteria: (1) studies must include a methodological description of how kinematic variables were obtained for the lower limb, (2) kinematic data must have been acquired by means of IMUs, (3) studies must have validated the implemented method against a golden standard reference system. Information on study characteristics, signal processing characteristics and study results was assessed and discussed. This review shows that methods for lower limb joint kinematics are inherently application dependent. Sensor restrictions are generally compensated with biomechanically inspired assumptions and prior information. Awareness of the possible adaptations in the IMU-based kinematic estimates by incorporating such prior information and assumptions is necessary, before drawing clinical decisions. Future research should focus on alternative validation methods, subject-specific IMU-based biomechanical joint models and disturbed movement patterns in real-world settings. Full article
(This article belongs to the Special Issue Inertial Sensors)
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15 pages, 3180 KiB  
Article
SeisMote: A Multi-Sensor Wireless Platform for Cardiovascular Monitoring in Laboratory, Daily Life, and Telemedicine
by Marco Di Rienzo, Giovannibattista Rizzo, Zeynep Melike Işilay and Prospero Lombardi
Sensors 2020, 20(3), 680; https://doi.org/10.3390/s20030680 - 26 Jan 2020
Cited by 33 | Viewed by 5893
Abstract
This article presents a new wearable platform, SeisMote, for the monitoring of cardiovascular function in controlled conditions and daily life. It consists of a wireless network of sensorized nodes providing simultaneous multiple measures of electrocardiogram (ECG), acceleration, rotational velocity, and photoplethysmogram (PPG) from [...] Read more.
This article presents a new wearable platform, SeisMote, for the monitoring of cardiovascular function in controlled conditions and daily life. It consists of a wireless network of sensorized nodes providing simultaneous multiple measures of electrocardiogram (ECG), acceleration, rotational velocity, and photoplethysmogram (PPG) from different body areas. A custom low-power transmission protocol was developed to allow the concomitant real-time monitoring of 32 signals (16 bit @200 Hz) from up to 12 nodes with a jitter in the among-node time synchronization lower than 0.2 ms. The BluetoothLE protocol may be used when only a single node is needed. Data can also be collected in the off-line mode. Seismocardiogram and pulse transit times can be derived from the collected data to obtain additional information on cardiac mechanics and vascular characteristics. The employment of the system in the field showed recordings without data gaps caused by transmission errors, and the duration of each battery charge exceeded 16 h. The system is currently used to investigate strategies of hemodynamic regulation in different vascular districts (through a multisite assessment of ECG and PPG) and to study the propagation of precordial vibrations along the thorax. The single-node version is presently exploited to monitor cardiac patients during telerehabilitation. Full article
(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)
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16 pages, 25191 KiB  
Article
LoRaWAN for Smart City IoT Deployments: A Long Term Evaluation
by Philip J. Basford, Florentin M. J. Bulot, Mihaela Apetroaie-Cristea, Simon J. Cox and Steven J. Ossont
Sensors 2020, 20(3), 648; https://doi.org/10.3390/s20030648 - 23 Jan 2020
Cited by 118 | Viewed by 12431
Abstract
LoRaWAN is a Low-Power Wide Area Network (LPWAN) technology designed for Internet of Things (IoT) deployments; this paper presents experiences from deploying a city-scale LoRaWAN network across Southampton, UK. This network was deployed to support an installation of air quality monitors and to [...] Read more.
LoRaWAN is a Low-Power Wide Area Network (LPWAN) technology designed for Internet of Things (IoT) deployments; this paper presents experiences from deploying a city-scale LoRaWAN network across Southampton, UK. This network was deployed to support an installation of air quality monitors and to explore the capabilities of LoRaWAN. This deployment uses a mixture of commercial off-the-shelf gateways and custom gateways. These gateway locations were chosen based on network access, site permission and accessibility, and are not necessarily the best locations theoretically. Over 135,000 messages have been transmitted by the twenty devices analysed. Over the course of the complete deployment, 72.4 % of the messages were successfully received by the data server. Of the messages that were received, 99% were received within 10 s of transmission. We conclude that LoRaWAN is an applicable communication technology for city-scale air quality monitoring and other smart city applications. Full article
(This article belongs to the Special Issue Selected Papers from the 3rd Global IoT Summit)
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21 pages, 1341 KiB  
Article
An IoT Architecture for Water Resource Management in Agroindustrial Environments: A Case Study in Almería (Spain)
by Manuel Muñoz, Juan D. Gil, Lidia Roca, Francisco Rodríguez and Manuel Berenguel
Sensors 2020, 20(3), 596; https://doi.org/10.3390/s20030596 - 21 Jan 2020
Cited by 33 | Viewed by 7255
Abstract
The current agricultural water panorama in many Mediterranean countries is composed by desalination facilities, wells (frequently overexploited), the water public utility network, and several consumer agents with different water needs. This distributed water network requires centralized management methods for its proper use, which [...] Read more.
The current agricultural water panorama in many Mediterranean countries is composed by desalination facilities, wells (frequently overexploited), the water public utility network, and several consumer agents with different water needs. This distributed water network requires centralized management methods for its proper use, which are difficult to implement as the different agents are usually geographically separated. In this sense, the use of enabling technologies such as the Internet of Things can be essential to the proper operation of these agroindustrial systems. In this paper, an Internet of Things cloud architecture based on the FIWARE standard is proposed for interconnecting the several agents that make up the agroindustrial system. In addition, this architecture includes an efficient management method based on a model predictive control technique, which is aimed at minimizing operating costs. A case study inspired by three real facilities located in Almería (southeast of Spain) is used as the simulation test bed. The obtained results show how around 75% of the total operating costs can be saved with the application of the proposed approach, which could be very significant to decrease the costs of desalinated water and, therefore, to maintain the sustainability of the agricultural system. Full article
(This article belongs to the Special Issue Selected Papers from the 3rd Global IoT Summit)
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19 pages, 1999 KiB  
Article
Predictive Maintenance of Boiler Feed Water Pumps Using SCADA Data
by Marek Moleda, Alina Momot and Dariusz Mrozek
Sensors 2020, 20(2), 571; https://doi.org/10.3390/s20020571 - 20 Jan 2020
Cited by 36 | Viewed by 13273
Abstract
IoT enabled predictive maintenance allows companies in the energy sector to identify potential problems in the production devices far before the failure occurs. In this paper, we propose a method for early detection of faults in boiler feed pumps using existing measurements currently [...] Read more.
IoT enabled predictive maintenance allows companies in the energy sector to identify potential problems in the production devices far before the failure occurs. In this paper, we propose a method for early detection of faults in boiler feed pumps using existing measurements currently captured by control devices. In the experimental part, we work on real measurement data and events from a coal fired power plant. The main research objective is to implement a model that detects deviations from the normal operation state based on regression and to check which events or failures can be detected by it. The presented technique allows the creation of a predictive system working on the basis of the available data with a minimal requirement of expert knowledge, in particular the knowledge related to the categorization of failures and the exact time of their occurrence, which is sometimes difficult to identify. The paper shows that with modern technologies, such as the Internet of Things, big data, and cloud computing, it is possible to integrate automation systems, designed in the past only to control the production process, with IT systems that make all processes more efficient through the use of advanced analytic tools. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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17 pages, 4209 KiB  
Article
A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers
by Daniela Lo Presti, Arianna Carnevale, Jessica D’Abbraccio, Luca Massari, Carlo Massaroni, Riccardo Sabbadini, Martina Zaltieri, Joshua Di Tocco, Marco Bravi, Sandra Miccinilli, Silvia Sterzi, Umile G. Longo, Vincenzo Denaro, Michele A. Caponero, Domenico Formica, Calogero M. Oddo and Emiliano Schena
Sensors 2020, 20(2), 536; https://doi.org/10.3390/s20020536 - 18 Jan 2020
Cited by 75 | Viewed by 6670
Abstract
Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These [...] Read more.
Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors ≤6.09% and ≤1.90%, during quiet breathing and tachypnea, respectively. Full article
(This article belongs to the Collection Respiratory Monitoring for Healthcare, Sport and Physical Activity: Sensors, Techniques and Applications)
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18 pages, 8540 KiB  
Article
Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
by Wei-Hsun Lee and Chi-Yi Chiu
Sensors 2020, 20(2), 508; https://doi.org/10.3390/s20020508 - 16 Jan 2020
Cited by 135 | Viewed by 40597
Abstract
Infrastructure supporting vehicular network (V2X) capability is the key factor to the success of smart city because it enables many smart transportation services. In order to reduce the traffic congestion and improve the public transport efficiency, many intelligent transportation systems (ITS) need to [...] Read more.
Infrastructure supporting vehicular network (V2X) capability is the key factor to the success of smart city because it enables many smart transportation services. In order to reduce the traffic congestion and improve the public transport efficiency, many intelligent transportation systems (ITS) need to be developed. In this paper, a smart traffic signal control (STSC) system is designed and implemented, it supports several smart city transportation applications including emergency vehicle signal preemption (EVSP), public transport signal priority (TSP), adaptive traffic signal control (ATSC), eco-driving supporting, and message broadcasting. The roadside unit (RSU) controller is the core of the proposed STSC system, where the system architecture, middleware, control algorithms, and peripheral modules are detailed discussed in this paper. It is compatible with existed traffic signal controller so that it can be fast and cost−effectively deployed. A new traffic signal scheme is specially designed for the EVSP scenario, it can inform all the drivers near the intersection regarding which direction the emergency vehicle (EV) is approaching, smoothing the traffic flow, and enhancing the safety. EVSP scenario and the related control algorithms are implemented in this work; integration test and field test are performed to demonstrate the STSC system. Full article
(This article belongs to the Special Issue Architectures and Platforms for Smart and Sustainable Cities)
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18 pages, 951 KiB  
Review
Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review
by Hugo F. Posada-Quintero and Ki H. Chon
Sensors 2020, 20(2), 479; https://doi.org/10.3390/s20020479 - 15 Jan 2020
Cited by 317 | Viewed by 26696
Abstract
The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA [...] Read more.
The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research. Full article
(This article belongs to the Special Issue Skin Sensors)
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22 pages, 611 KiB  
Review
Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
by Amina Seferagić, Jeroen Famaey, Eli De Poorter and Jeroen Hoebeke
Sensors 2020, 20(2), 488; https://doi.org/10.3390/s20020488 - 15 Jan 2020
Cited by 92 | Viewed by 9948
Abstract
Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, [...] Read more.
Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment. Full article
(This article belongs to the Special Issue Real-Time Sensor Networks and Systems for the Industrial IoT)
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12 pages, 5585 KiB  
Article
Fused-Deposition-Material 3D-Printing Procedure and Algorithm Avoiding Use of Any Supports
by Gianluca Barile, Alfiero Leoni, Mirco Muttillo, Romina Paolucci, Gianfranco Fazzini and Leonardo Pantoli
Sensors 2020, 20(2), 470; https://doi.org/10.3390/s20020470 - 14 Jan 2020
Cited by 6 | Viewed by 8147
Abstract
The three-dimensional printing of complex shapes without using supporting structures is the most attractive factor of merit in current additive manufacturing because it allows to drastically reduce printing time, and ideally nullify postprocessing and waste material. In this work, we present an innovative [...] Read more.
The three-dimensional printing of complex shapes without using supporting structures is the most attractive factor of merit in current additive manufacturing because it allows to drastically reduce printing time, and ideally nullify postprocessing and waste material. In this work, we present an innovative procedure and algorithm (Print on Air, PoA) for additive manufacturing that, relying on sensing systems embedded into the three-dimensional (3D) printer (e.g., temperature and speed sensors), aims at generating a printing sequence capable of a self-sustaining bridge and overhang structures. This feature was achieved by splitting the actual floating area of the layer where the aforementioned structures are in many subsections. Each is generated with a negligible floating surface and printed in a well-determined sequence with accurate temperature and speed profiles. Therefore, each subsection is formed without the need for scaffolding, simultaneously acting as a supporting structure for the following subsection. The array of subsections constitutes the actual bridge or overhang structure. The proposed method can be used for any object, including very long bridges or convex surfaces. The revolutionary method is here reported and evaluated in order to show its applicability in any condition. Although the study was conducted in a Fused Deposition Material (FDM) environment, it can certainly be adapted to other manufacturing environments with adequate modifications. Full article
(This article belongs to the Special Issue Selected papers from the 2019 IEEE International Worshop on Metrology for Industry 4.0 and IoT)
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14 pages, 2054 KiB  
Article
An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel
by Kelsey Herndon, Rebekke Muench, Emil Cherrington and Robert Griffin
Sensors 2020, 20(2), 431; https://doi.org/10.3390/s20020431 - 12 Jan 2020
Cited by 51 | Viewed by 6399
Abstract
Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location [...] Read more.
Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs. Full article
(This article belongs to the Special Issue Applications of Remote Sensing Data in Water Resources Management)
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25 pages, 7519 KiB  
Article
Accuracy of the Dynamic Acoustic Map in a Large City Generated by Fixed Monitoring Units
by Roberto Benocci, Chiara Confalonieri, Hector Eduardo Roman, Fabio Angelini and Giovanni Zambon
Sensors 2020, 20(2), 412; https://doi.org/10.3390/s20020412 - 11 Jan 2020
Cited by 28 | Viewed by 3864
Abstract
DYNAMAP, a European Life project, aims at giving a real image of the noise generated by vehicular traffic in urban areas developing a dynamic acoustic map based on a limited number of low-cost permanent noise monitoring stations. The system has been implemented in [...] Read more.
DYNAMAP, a European Life project, aims at giving a real image of the noise generated by vehicular traffic in urban areas developing a dynamic acoustic map based on a limited number of low-cost permanent noise monitoring stations. The system has been implemented in two pilot areas located in the agglomeration of Milan (Italy) and along the Motorway A90 (Rome-Italy). The paper reports the final assessment of the system installed in the pilot area of Milan. Traffic noise data collected by the monitoring stations, each one representative of a number of roads (groups) sharing similar characteristics (e.g., daily traffic flow), are used to build-up a “real-time” noise map. In particular, we focused on the results of the testing campaign (21 sites distributed over the pilot area and 24 h duration of each recording). It allowed evaluating the accuracy and reliability of the system by comparing the predicted noise level of DYNAMAP with field measurements in randomly selected sites. To this end, a statistical analysis has been implemented to determine the error associated with such prediction, and to optimize the system by developing a correction procedure aimed at keeping the error below some acceptable threshold. The steps and the results of this procedure are given in detail. It is shown that it is possible to describe a complex road network on the basis of a statistical approach, complemented by empirical data, within a threshold of 3 dB provided that the traffic flow model achieves a comparable accuracy within each single groups of roads in the network. Full article
(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)
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22 pages, 5866 KiB  
Article
Algorithm with Patterned Singular Value Approach for Highly Reliable Autonomous Star Identification
by Kiduck Kim and Hyochoong Bang
Sensors 2020, 20(2), 374; https://doi.org/10.3390/s20020374 - 9 Jan 2020
Cited by 10 | Viewed by 3391
Abstract
In the work reported in this paper, a lost-in-space star pattern identification algorithm for agile spacecraft was studied. Generally, the operation of a star tracker is known to exhibit serious degradation or even failure during fast attitude maneuvers. While tracking methods are widely [...] Read more.
In the work reported in this paper, a lost-in-space star pattern identification algorithm for agile spacecraft was studied. Generally, the operation of a star tracker is known to exhibit serious degradation or even failure during fast attitude maneuvers. While tracking methods are widely used solutions to handle the dynamic conditions, they require prior information about the initial orientation. Therefore, the tracking methods may not be adequate for autonomy of attitude and control systems. In this paper a novel autonomous identification method for dynamic conditions is proposed. Additional constraints are taken into account that can significantly decrease the number of stars imaged and the centroid accuracy. A strategy combining two existing classes for star pattern identification is proposed. The new approach is intended to provide a unique way to determine the identity of stars that promises robustness against noise and rapid identification. Moreover, representative algorithms implemented in actual space applications were utilized as counterparts to analyze the performance of the proposed method in various scenarios. Numerical simulations show that the proposed method is not only highly robust against positional noise and false stars, but also guarantees fast run-time, which is appropriate for high-speed applications. Full article
(This article belongs to the Special Issue Sensor Systems for Satellite Attitude Determination)
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10 pages, 3334 KiB  
Article
A Novel Piezoresistive MEMS Pressure Sensors Based on Temporary Bonding Technology
by Peishuai Song, Chaowei Si, Mingliang Zhang, Yongmei Zhao, Yurong He, Wen Liu and Xiaodong Wang
Sensors 2020, 20(2), 337; https://doi.org/10.3390/s20020337 - 7 Jan 2020
Cited by 32 | Viewed by 7828
Abstract
A miniature piezoresistive pressure sensor fabricated by temporary bonding technology was reported in this paper. The sensing membrane was formed on the device layer of an SOI (Silicon-On-Insulator) wafer, which was bonded to borosilicate glass (Borofloat 33, BF33) wafer for supporting before releasing [...] Read more.
A miniature piezoresistive pressure sensor fabricated by temporary bonding technology was reported in this paper. The sensing membrane was formed on the device layer of an SOI (Silicon-On-Insulator) wafer, which was bonded to borosilicate glass (Borofloat 33, BF33) wafer for supporting before releasing with Cu-Cu bonding after boron doping and electrode patterning. The handle layer was bonded to another BF33 wafer after thinning and etching. Finally, the substrate BF33 wafer was thinned by chemical mechanical polishing (CMP) to reduce the total device thickness. The copper temporary bonding layer was removed by acid solution after dicing to release the sensing membrane. The chip area of the fabricated pressure sensor was of 1600 μm × 650 μm × 104 μm, and the size of a sensing membrane was of 100 μm × 100 μm × 2 μm. A higher sensitivity of 36 μV/(V∙kPa) in the range of 0–180 kPa was obtained. By further reducing the width, the fabricated miniature pressure sensor could be easily mounted in a medical catheter for the blood pressure measurement. Full article
(This article belongs to the Section Sensor Materials)
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24 pages, 4287 KiB  
Article
Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks
by Dezhi Han, Yunping Yu, Kuan-Ching Li and Rodrigo Fernandes de Mello
Sensors 2020, 20(2), 343; https://doi.org/10.3390/s20020343 - 7 Jan 2020
Cited by 72 | Viewed by 5120
Abstract
The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless [...] Read more.
The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements. Full article
(This article belongs to the Special Issue Trends on Edge Computing and Artificial Intelligence for Next Generation Sensor Networks)
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14 pages, 591 KiB  
Article
Distributed Optical Fiber-Based Approach for Soil–Structure Interaction
by Nissrine Boujia, Franziska Schmidt, Christophe Chevalier, Dominique Siegert and Damien Pham Van Bang
Sensors 2020, 20(1), 321; https://doi.org/10.3390/s20010321 - 6 Jan 2020
Cited by 10 | Viewed by 5216
Abstract
Scour is a hydraulic risk threatening the stability of bridges in fluvial and coastal areas. Therefore, developing permanent and real-time monitoring techniques is crucial. Recent advances in strain measurements using fiber optic sensors allow new opportunities for scour monitoring. In this study, the [...] Read more.
Scour is a hydraulic risk threatening the stability of bridges in fluvial and coastal areas. Therefore, developing permanent and real-time monitoring techniques is crucial. Recent advances in strain measurements using fiber optic sensors allow new opportunities for scour monitoring. In this study, the innovative optical frequency domain reflectometry (OFDR) was used to evaluate the effect of scour by performing distributed strain measurements along a rod under static lateral loads. An analytical analysis based on the Winkler model of the soil was carefully established and used to evaluate the accuracy of the fiber optic sensors and helped interpret the measurements results. Dynamic tests were also performed and results from static and dynamic tests were compared using an equivalent cantilever model. Full article
(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)
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17 pages, 981 KiB  
Article
A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction
by Faraz Malik Awan, Yasir Saleem, Roberto Minerva and Noel Crespi
Sensors 2020, 20(1), 322; https://doi.org/10.3390/s20010322 - 6 Jan 2020
Cited by 102 | Viewed by 11329
Abstract
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on [...] Read more.
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots. Full article
(This article belongs to the Special Issue Sensor Systems in Smart Environments)
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26 pages, 9504 KiB  
Article
Vehicle Trajectory Prediction and Collision Warning via Fusion of Multisensors and Wireless Vehicular Communications
by Minjin Baek, Donggi Jeong, Dongho Choi and Sangsun Lee
Sensors 2020, 20(1), 288; https://doi.org/10.3390/s20010288 - 4 Jan 2020
Cited by 63 | Viewed by 9036
Abstract
Driver inattention is one of the leading causes of traffic crashes worldwide. Providing the driver with an early warning prior to a potential collision can significantly reduce the fatalities and level of injuries associated with vehicle collisions. In order to monitor the vehicle [...] Read more.
Driver inattention is one of the leading causes of traffic crashes worldwide. Providing the driver with an early warning prior to a potential collision can significantly reduce the fatalities and level of injuries associated with vehicle collisions. In order to monitor the vehicle surroundings and predict collisions, on-board sensors such as radar, lidar, and cameras are often used. However, the driving environment perception based on these sensors can be adversely affected by a number of factors such as weather and solar irradiance. In addition, potential dangers cannot be detected if the target is located outside the limited field-of-view of the sensors, or if the line of sight to the target is occluded. In this paper, we propose an approach for designing a vehicle collision warning system based on fusion of multisensors and wireless vehicular communications. A high-level fusion of radar, lidar, camera, and wireless vehicular communication data was performed to predict the trajectories of remote targets and generate an appropriate warning to the driver prior to a possible collision. We implemented and evaluated the proposed vehicle collision system in virtual driving environments, which consisted of a vehicle–vehicle collision scenario and a vehicle–pedestrian collision scenario. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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21 pages, 1089 KiB  
Article
Impact of SCHC Compression and Fragmentation in LPWAN: A Case Study with LoRaWAN
by Jesus Sanchez-Gomez, Jorge Gallego-Madrid, Ramon Sanchez-Iborra, Jose Santa and Antonio F. Skarmeta
Sensors 2020, 20(1), 280; https://doi.org/10.3390/s20010280 - 3 Jan 2020
Cited by 32 | Viewed by 6070
Abstract
The dawn of the Internet of Things (IoT) paradigm has brought about a series of novel services never imagined until recently. However, certain deployments such as those employing Low-Power Wide-Area Network (LPWAN)-based technologies may present severe network restrictions in terms of throughput and [...] Read more.
The dawn of the Internet of Things (IoT) paradigm has brought about a series of novel services never imagined until recently. However, certain deployments such as those employing Low-Power Wide-Area Network (LPWAN)-based technologies may present severe network restrictions in terms of throughput and supported packet length. This situation prompts the isolation of LPWAN systems on islands with limited interoperability with the Internet. For that reason, the IETF’s LPWAN working group has proposed a Static Context Header Compression (SCHC) scheme that permits compression and fragmentation of and IPv6/UDP/CoAP packets with the aim of making them suitable for transmission over the restricted links of LPWANs. Given the impact that such a solution can have in many IoT scenarios, this paper addresses its real evaluation in terms not only of latency and delivery ratio improvements, as a consequence of different compression and fragmentation levels, but also of the overhead in end node resources and useful payload sent per fragment. This has been carried out with the implementation of middleware and using a real testbed implementation of a LoRaWAN-to-IPv6 architecture together with a publish/subscribe broker for CoAP. The attained results show the advantages of SCHC, and sustain discussion regarding the impact of different SCHC and LoRaWAN configurations on the performance. It is highlighted that necessary end node resources are low as compared to the benefit of delivering long IPv6 packets over the LPWAN links. In turn, fragmentation can impose a lack of efficiency in terms of data and energy and, hence, a cross-layer solution is needed in order to obtain the best throughput of the network. Full article
(This article belongs to the Special Issue Pub/Sub Solutions for IoT)
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19 pages, 7293 KiB  
Article
Fusion of Spectroscopy and Cobalt Electrochemistry Data for Estimating Phosphate Concentration in Hydroponic Solution
by Dae-Hyun Jung, Hak-Jin Kim, Hyoung Seok Kim, Jaeyoung Choi, Jeong Do Kim and Soo Hyun Park
Sensors 2019, 19(11), 2596; https://doi.org/10.3390/s19112596 - 7 Jun 2019
Cited by 19 | Viewed by 6060
Abstract
Phosphate is a key element affecting plant growth. Therefore, the accurate determination of phosphate concentration in hydroponic nutrient solutions is essential for providing a balanced set of nutrients to plants within a suitable range. This study aimed to develop a data fusion approach [...] Read more.
Phosphate is a key element affecting plant growth. Therefore, the accurate determination of phosphate concentration in hydroponic nutrient solutions is essential for providing a balanced set of nutrients to plants within a suitable range. This study aimed to develop a data fusion approach for determining phosphate concentrations in a paprika nutrient solution. As a conventional multivariate analysis approach using spectral data, partial least squares regression (PLSR) and principal components regression (PCR) models were developed using 56 samples for calibration and 24 samples for evaluation. The R2 values of estimation models using PCR and PLSR ranged from 0.44 to 0.64. Furthermore, an estimation model using raw electromotive force (EMF) data from cobalt electrodes gave R2 values of 0.58–0.71. To improve the model performance, a data fusion method was developed to estimate phosphate concentration using near infrared (NIR) spectral and cobalt electrochemical data. Raw EMF data from cobalt electrodes and principle component values from the spectral data were combined. Results of calibration and evaluation tests using an artificial neural network estimation model showed that R2 = 0.90 and 0.89 and root mean square error (RMSE) = 96.70 and 119.50 mg/L, respectively. These values are sufficiently high for application to measuring phosphate concentration in hydroponic solutions. Full article
(This article belongs to the Special Issue Ion Selective Electrodes and Optodes)
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19 pages, 6019 KiB  
Article
Deep CT to MR Synthesis Using Paired and Unpaired Data
by Cheng-Bin Jin, Hakil Kim, Mingjie Liu, Wonmo Jung, Seongsu Joo, Eunsik Park, Young Saem Ahn, In Ho Han, Jae Il Lee and Xuenan Cui
Sensors 2019, 19(10), 2361; https://doi.org/10.3390/s19102361 - 22 May 2019
Cited by 161 | Viewed by 11941
Abstract
Magnetic resonance (MR) imaging plays a highly important role in radiotherapy treatment planning for the segmentation of tumor volumes and organs. However, the use of MR is limited, owing to its high cost and the increased use of metal implants for patients. This [...] Read more.
Magnetic resonance (MR) imaging plays a highly important role in radiotherapy treatment planning for the segmentation of tumor volumes and organs. However, the use of MR is limited, owing to its high cost and the increased use of metal implants for patients. This study is aimed towards patients who are contraindicated owing to claustrophobia and cardiac pacemakers, and many scenarios in which only computed tomography (CT) images are available, such as emergencies, situations lacking an MR scanner, and situations in which the cost of obtaining an MR scan is prohibitive. From medical practice, our approach can be adopted as a screening method by radiologists to observe abnormal anatomical lesions in certain diseases that are difficult to diagnose by CT. The proposed approach can estimate an MR image based on a CT image using paired and unpaired training data. In contrast to existing synthetic methods for medical imaging, which depend on sparse pairwise-aligned data or plentiful unpaired data, the proposed approach alleviates the rigid registration of paired training, and overcomes the context-misalignment problem of unpaired training. A generative adversarial network was trained to transform two-dimensional (2D) brain CT image slices into 2D brain MR image slices, combining the adversarial, dual cycle-consistent, and voxel-wise losses. Qualitative and quantitative comparisons against independent paired and unpaired training methods demonstrated the superiority of our approach. Full article
(This article belongs to the Special Issue Biomedical Imaging and Sensing)
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39 pages, 11559 KiB  
Review
Tapered Optical Fibre Sensors: Current Trends and Future Perspectives
by Sergiy Korposh, Stephen W. James, Seung-Woo Lee and Ralph P. Tatam
Sensors 2019, 19(10), 2294; https://doi.org/10.3390/s19102294 - 17 May 2019
Cited by 160 | Viewed by 17932
Abstract
The development of reliable, affordable and efficient sensors is a key step in providing tools for efficient monitoring of critical environmental parameters. This review focuses on the use of tapered optical fibres as an environmental sensing platform. Tapered fibres allow access to the [...] Read more.
The development of reliable, affordable and efficient sensors is a key step in providing tools for efficient monitoring of critical environmental parameters. This review focuses on the use of tapered optical fibres as an environmental sensing platform. Tapered fibres allow access to the evanescent wave of the propagating mode, which can be exploited to facilitate chemical sensing by spectroscopic evaluation of the medium surrounding the optical fibre, by measurement of the refractive index of the medium, or by coupling to other waveguides formed of chemically sensitive materials. In addition, the reduced diameter of the tapered section of the optical fibre can offer benefits when measuring physical parameters such as strain and temperature. A review of the basic sensing platforms implemented using tapered optical fibres and their application for development of fibre-optic physical, chemical and bio-sensors is presented. Full article
(This article belongs to the Special Issue Sensors for Emerging Environmental Markers and Contaminants)
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11 pages, 706 KiB  
Article
Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
by Andrey V. Savkin and Hailong Huang
Sensors 2019, 19(9), 2068; https://doi.org/10.3390/s19092068 - 3 May 2019
Cited by 45 | Viewed by 6126
Abstract
This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable [...] Read more.
This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable algorithm to estimate the minimum number of drones and determine their locations is developed. Moreover, it is proved that this algorithm is asymptotically optimal in the sense that the ratio of the number of drones required by this algorithm and the minimum number of drones converges to one as the area of the ground region tends to infinity. The proof is based on Kershner’s theorem from combinatorial geometry. Illustrative examples and comparisons with other existing methods show the efficiency of the developed algorithm. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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26 pages, 2330 KiB  
Review
Review on Wearable Technology Sensors Used in Consumer Sport Applications
by Gobinath Aroganam, Nadarajah Manivannan and David Harrison
Sensors 2019, 19(9), 1983; https://doi.org/10.3390/s19091983 - 28 Apr 2019
Cited by 277 | Viewed by 42518
Abstract
This review paper discusses the trends and projections for wearable technology in the consumer sports sector (excluding professional sport). Analyzing the role of wearable technology for different users and why there is such a need for these devices in everyday lives. It shows [...] Read more.
This review paper discusses the trends and projections for wearable technology in the consumer sports sector (excluding professional sport). Analyzing the role of wearable technology for different users and why there is such a need for these devices in everyday lives. It shows how different sensors are influential in delivering a variety of readings that are useful in many ways regarding sport attributes. Wearables are increasing in function, and through integrating technology, users are gathering more data about themselves. The amount of wearable technology available is broad, each having its own role to play in different industries. Inertial measuring unit (IMU) and Global Positioning System (GPS) sensors are predominantly present in sport wearables but can be programmed for different needs. In this review, the differences are displayed to show which sensors are compatible and which ones can evolve sensor technology for sport applications. Full article
(This article belongs to the Special Issue Wearable Wireless Sensors)
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19 pages, 407 KiB  
Article
Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things
by Geethapriya Thamilarasu and Shiven Chawla
Sensors 2019, 19(9), 1977; https://doi.org/10.3390/s19091977 - 27 Apr 2019
Cited by 239 | Viewed by 12432
Abstract
Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end [...] Read more.
Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively. Full article
(This article belongs to the Special Issue Security and Privacy in Internet of Things)
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9 pages, 2611 KiB  
Article
Sperm-Cultured Gate Ion-Sensitive Field-Effect Transistor for Non-Optical and Live Monitoring of Sperm Capacitation
by Akiko Saito and Toshiya Sakata
Sensors 2019, 19(8), 1784; https://doi.org/10.3390/s19081784 - 14 Apr 2019
Cited by 13 | Viewed by 4060
Abstract
We have successfully monitored the effect of progesterone and Ca2+ on artificially induced sperm capacitation in a real-time, noninvasive and label-free manner using an ion-sensitive field-effect transistor (ISFET) sensor. The sperm activity can be electrically detected as a change in pH generated [...] Read more.
We have successfully monitored the effect of progesterone and Ca2+ on artificially induced sperm capacitation in a real-time, noninvasive and label-free manner using an ion-sensitive field-effect transistor (ISFET) sensor. The sperm activity can be electrically detected as a change in pH generated by sperm respiration based on the principle of the ISFET sensor. Upon adding mouse sperm to the gate of the ISFET sensor in the culture medium with progesterone, the pH decreases with an increasing concentration of progesterone from 1 to 40 μM. This is because progesterone induces Ca2+ influx into spermatozoa and triggers multiple Ca2+-dependent physiological responses, which subsequently activates sperm respiration. Moreover, this pH response of the ISFET sensor is not observed for a Ca2+-free medium even when progesterone is introduced, which means that Ca2+ influx is necessary for sperm activation that results in sperm capacitation. Thus, a platform based on the ISFET sensor system can provide a simple method of evaluating artificially induced sperm capacitation in the field of male infertility treatment. Full article
(This article belongs to the Special Issue Potentiometric Bio/Chemical Sensing)
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