Sensors

17 pages, 5284 KiB

Open AccessEditor’s ChoiceArticle

An Improved Routing Schema with Special Clustering Using PSO Algorithm for Heterogeneous Wireless Sensor Network

by Jin Wang, Yu Gao, Wei Liu, Arun Kumar Sangaiah and Hye-Jin Kim

Sensors 2019, 19(3), 671; https://doi.org/10.3390/s19030671 - 7 Feb 2019

Cited by 209 | Viewed by 8781

Energy efficiency and energy balancing are crucial research issues as per routing protocol designing for self-organized wireless sensor networks (WSNs). Many literatures used the clustering algorithm to achieve energy efficiency and energy balancing, however, there are usually energy holes near the cluster heads [...] Read more.

Energy efficiency and energy balancing are crucial research issues as per routing protocol designing for self-organized wireless sensor networks (WSNs). Many literatures used the clustering algorithm to achieve energy efficiency and energy balancing, however, there are usually energy holes near the cluster heads (CHs) because of the heavy burden of forwarding. As the clustering problem in lossy WSNs is proved to be a NP-hard problem, many metaheuristic algorithms are utilized to solve the problem. In this paper, a special clustering method called Energy Centers Searching using Particle Swarm Optimization (EC-PSO) is presented to avoid these energy holes and search energy centers for CHs selection. During the first period, the CHs are elected using geometric method. After the energy of the network is heterogeneous, EC-PSO is adopted for clustering. Energy centers are searched using an improved PSO algorithm and nodes close to the energy center are elected as CHs. Additionally, a protection mechanism is also used to prevent low energy nodes from being the forwarder and a mobile data collector is introduced to gather the data. We conduct numerous simulations to illustrate that our presented EC-PSO outperforms than some similar works in terms of network lifetime enhancement and energy utilization ratio. Full article

(This article belongs to the Special Issue Recent Advances in Data Mining and Information Fusion in Wireless Sensors Networks)

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34 pages, 5506 KiB

Open AccessEditor’s ChoiceReview

Fluorescent Sensors for the Detection of Heavy Metal Ions in Aqueous Media

by Nerea De Acha, César Elosúa, Jesús M. Corres and Francisco J. Arregui

Sensors 2019, 19(3), 599; https://doi.org/10.3390/s19030599 - 31 Jan 2019

Cited by 183 | Viewed by 15421

Abstract

Due to the risks that water contamination implies for human health and environmental protection, monitoring the quality of water is a major concern of the present era. Therefore, in recent years several efforts have been dedicated to the development of fast, sensitive, and [...] Read more.

Due to the risks that water contamination implies for human health and environmental protection, monitoring the quality of water is a major concern of the present era. Therefore, in recent years several efforts have been dedicated to the development of fast, sensitive, and selective sensors for the detection of heavy metal ions. In particular, fluorescent sensors have gained in popularity due to their interesting features, such as high specificity, sensitivity, and reversibility. Thus, this review is devoted to the recent advances in fluorescent sensors for the monitoring of these contaminants, and special focus is placed on those devices based on fluorescent aptasensors, quantum dots, and organic dyes. Full article

(This article belongs to the Special Issue Optical Chemical Nanosensors)

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12 pages, 17892 KiB

Open AccessEditor’s ChoiceArticle

Embedded Smart Antenna for Non-Destructive Testing and Evaluation (NDT&E) of Moisture Content and Deterioration in Concrete

by Kah Hou Teng, Patryk Kot, Magomed Muradov, Andy Shaw, Khalid Hashim, Michaela Gkantou and Ahmed Al-Shamma’a

Sensors 2019, 19(3), 547; https://doi.org/10.3390/s19030547 - 28 Jan 2019

Cited by 68 | Viewed by 6960

Abstract

Concrete failure will lead to serious safety concerns in the performance of a building structure. It is one of the biggest challenges for engineers to inspect and maintain the quality of concrete throughout the service years in order to prevent structural deterioration. To [...] Read more.

Concrete failure will lead to serious safety concerns in the performance of a building structure. It is one of the biggest challenges for engineers to inspect and maintain the quality of concrete throughout the service years in order to prevent structural deterioration. To date, a lot of research is ongoing to develop different instruments to inspect concrete quality. Detection of moisture ingress is important in the structural monitoring of concrete. This paper presents a novel sensing technique using a smart antenna for the non-destructive evaluation of moisture content and deterioration inspection in concrete blocks. Two different standard concrete samples (United Kingdom and Malaysia) were investigated in this research. An electromagnetic (EM) sensor was designed and embedded inside the concrete to detect the moisture content within the structure. In addition, CST microwave studio was used to validate the theoretical model of the EM sensor against the test data. The results demonstrated that the EM sensor at 2.45 GHz is capable of detecting the moisture content in the concrete with linear regression of R² = 0.9752. Furthermore, identification of different mix ratios of concrete were successfully demonstrated in this paper. In conclusion, the EM sensor is capable of detecting moisture content non-destructively and could be a potential technique for maintenance and quality control of the building performance. Full article

(This article belongs to the Special Issue RF Technology for Sensor Applications)

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17 pages, 32838 KiB

Open AccessEditor’s ChoiceArticle

Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley

by Victor P. Rueda-Ayala, José M. Peña, Mats Höglind, José M. Bengochea-Guevara and Dionisio Andújar

Sensors 2019, 19(3), 535; https://doi.org/10.3390/s19030535 - 28 Jan 2019

Cited by 67 | Viewed by 9254

Abstract

Pastures are botanically diverse and difficult to characterize. Digital modeling of pasture biomass and quality by non-destructive methods can provide highly valuable support for decision-making. This study aimed to evaluate aerial and on-ground methods to characterize grass ley fields, estimating plant height, biomass [...] Read more.

Pastures are botanically diverse and difficult to characterize. Digital modeling of pasture biomass and quality by non-destructive methods can provide highly valuable support for decision-making. This study aimed to evaluate aerial and on-ground methods to characterize grass ley fields, estimating plant height, biomass and volume, using digital grass models. Two fields were sampled, one timothy-dominant and the other ryegrass-dominant. Both sensing systems allowed estimation of biomass, volume and plant height, which were compared with ground truth, also taking into consideration basic economical aspects. To obtain ground-truth data for validation, 10 plots of 1 m² were manually and destructively sampled on each field. The studied systems differed in data resolution, thus in estimation capability. There was a reasonably good agreement between the UAV-based, the RGB-D-based estimates and the manual height measurements on both fields. RGB-D-based estimation correlated well with ground truth of plant height (

R^{2} > 0.80

) for both fields, and with dry biomass (

R^{2} = 0.88

), only for the timothy field. RGB-D-based estimation of plant volume for ryegrass showed a high agreement (

R^{2} = 0.87

). The UAV-based system showed a weaker estimation capability for plant height and dry biomass (

R^{2} < 0.6

). UAV-systems are more affordable, easier to operate and can cover a larger surface. On-ground techniques with RGB-D cameras can produce highly detailed models, but with more variable results than UAV-based models. On-ground RGB-D data can be effectively analysed with open source software, which is a cost reduction advantage, compared with aerial image analysis. Since the resolution for agricultural operations does not need fine identification the end-details of the grass plants, the use of aerial platforms could result a better option in grasslands. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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25 pages, 10669 KiB

Open AccessEditor’s ChoiceArticle

Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping

by Javier Burgués, Victor Hernández, Achim J. Lilienthal and Santiago Marco

Sensors 2019, 19(3), 478; https://doi.org/10.3390/s19030478 - 24 Jan 2019

Cited by 93 | Viewed by 15823

Abstract

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up [...] Read more.

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m²) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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17 pages, 4690 KiB

Open AccessEditor’s ChoiceReview

Nanostructured Chemiresistive Gas Sensors for Medical Applications

by Noushin Nasiri and Christian Clarke

Sensors 2019, 19(3), 462; https://doi.org/10.3390/s19030462 - 23 Jan 2019

Cited by 79 | Viewed by 8398

Abstract

Treating diseases at their earliest stages significantly increases the chance of survival while decreasing the cost of treatment. Therefore, compared to traditional blood testing methods it is the goal of medical diagnostics to deliver a technique that can rapidly predict and if required [...] Read more.

Treating diseases at their earliest stages significantly increases the chance of survival while decreasing the cost of treatment. Therefore, compared to traditional blood testing methods it is the goal of medical diagnostics to deliver a technique that can rapidly predict and if required non-invasively monitor illnesses such as lung cancer, diabetes, melanoma and breast cancer at their very earliest stages, when the chance of recovery is significantly higher. To date human breath analysis is a promising candidate for fulfilling this need. Here, we highlight the latest key achievements on nanostructured chemiresistive sensors for disease diagnosis by human breath with focus on the multi-scale engineering of both composition and nano-micro scale morphology. We critically assess and compare state-of-the-art devices with the intention to provide direction for the next generation of chemiresistive nanostructured sensors. Full article

(This article belongs to the Special Issue Nanostructured Surfaces in Sensing Systems)

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29 pages, 17176 KiB

Open AccessEditor’s ChoiceReview

Non-Covalent Functionalization of Carbon Nanotubes for Electrochemical Biosensor Development

by Yan Zhou, Yi Fang and Ramaraja P. Ramasamy

Sensors 2019, 19(2), 392; https://doi.org/10.3390/s19020392 - 18 Jan 2019

Cited by 235 | Viewed by 18331

Abstract

Carbon nanotubes (CNTs) have been widely studied and used for the construction of electrochemical biosensors owing to their small size, cylindrical shape, large surface-to-volume ratio, high conductivity and good biocompatibility. In electrochemical biosensors, CNTs serve a dual purpose: they act as immobilization support [...] Read more.

Carbon nanotubes (CNTs) have been widely studied and used for the construction of electrochemical biosensors owing to their small size, cylindrical shape, large surface-to-volume ratio, high conductivity and good biocompatibility. In electrochemical biosensors, CNTs serve a dual purpose: they act as immobilization support for biomolecules as well as provide the necessary electrical conductivity for electrochemical transduction. The ability of a recognition molecule to detect the analyte is highly dependent on the type of immobilization used for the attachment of the biomolecule to the CNT surface, a process also known as biofunctionalization. A variety of biofunctionalization methods have been studied and reported including physical adsorption, covalent cross-linking, polymer encapsulation etc. Each method carries its own advantages and limitations. In this review we provide a comprehensive review of non-covalent functionalization of carbon nanotubes with a variety of biomolecules for the development of electrochemical biosensors. This method of immobilization is increasingly being used in bioelectrode development using enzymes for biosensor and biofuel cell applications. Full article

(This article belongs to the Special Issue Nanostructured Surfaces in Sensing Systems)

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17 pages, 2931 KiB

Open AccessEditor’s ChoiceArticle

Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

by Zied Tayeb, Juri Fedjaev, Nejla Ghaboosi, Christoph Richter, Lukas Everding, Xingwei Qu, Yingyu Wu, Gordon Cheng and Jörg Conradt

Sensors 2019, 19(1), 210; https://doi.org/10.3390/s19010210 - 8 Jan 2019

Cited by 132 | Viewed by 15091

Abstract

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an [...] Read more.

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI. Full article

(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Deep Learning for Sensor Information Fusion)

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14 pages, 4648 KiB

Open AccessEditor’s ChoiceArticle

Synthesis of Cu₂O/CuO Nanocrystals and Their Application to H₂S Sensing

by Kazuki Mikami, Yuta Kido, Yuji Akaishi, Armando Quitain and Tetsuya Kida

Sensors 2019, 19(1), 211; https://doi.org/10.3390/s19010211 - 8 Jan 2019

Cited by 58 | Viewed by 9365

Abstract

Semiconducting metal oxide nanocrystals are an important class of materials that have versatile applications because of their useful properties and high stability. Here, we developed a simple route to synthesize nanocrystals (NCs) of copper oxides such as Cu₂O and CuO using [...] Read more.

Semiconducting metal oxide nanocrystals are an important class of materials that have versatile applications because of their useful properties and high stability. Here, we developed a simple route to synthesize nanocrystals (NCs) of copper oxides such as Cu₂O and CuO using a hot-soap method, and applied them to H₂S sensing. Cu₂O NCs were synthesized by simply heating a copper precursor in oleylamine in the presence of diol at 160 °C under an Ar flow. X-ray diffractometry (XRD), dynamic light scattering (DLS), and transmission electron microscopy (TEM) results indicated the formation of monodispersed Cu₂O NCs having approximately 5 nm in crystallite size and 12 nm in colloidal size. The conversion of the Cu₂O NCs to CuO NCs was undertaken by straightforward air oxidation at room temperature, as confirmed by XRD and UV-vis analyses. A thin film Cu₂O NC sensor fabricated by spin coating showed responses to H₂S in dilute concentrations (1–8 ppm) at 50–150 °C, but the stability was poor because of the formation of metallic Cu₂S in a H₂S atmosphere. We found that Pd loading improved the stability of the sensor response. The Pd-loaded Cu₂O NC sensor exhibited reproducible responses to H₂S at 200 °C. Based on the gas sensing mechanism, it is suggested that Pd loading facilitates the reaction of adsorbed oxygen with H₂S and suppresses the irreversible formation of Cu₂S. Full article

(This article belongs to the Special Issue Functional Materials for the Applications of Advanced Gas Sensors)

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32 pages, 2379 KiB

Open AccessEditor’s ChoiceReview

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

by Youness Arjoune and Naima Kaabouch

Sensors 2019, 19(1), 126; https://doi.org/10.3390/s19010126 - 2 Jan 2019

Cited by 289 | Viewed by 15525

Abstract

Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access. Since its introduction, researchers have been working on enabling this innovative technology in managing the radio spectrum. As a result, this research field has [...] Read more.

Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access. Since its introduction, researchers have been working on enabling this innovative technology in managing the radio spectrum. As a result, this research field has been progressing at a rapid pace and significant advances have been made. To help researchers stay abreast of these advances, surveys and tutorial papers are strongly needed. Therefore, in this paper, we aimed to provide an in-depth survey on the most recent advances in spectrum sensing, covering its development from its inception to its current state and beyond. In addition, we highlight the efficiency and limitations of both narrowband and wideband spectrum sensing techniques as well as the challenges involved in their implementation. TV white spaces are also discussed in this paper as the first real application of cognitive radio. Last but by no means least, we discuss future research directions. This survey paper was designed in a way to help new researchers in the field to become familiar with the concepts of spectrum sensing, compressive sensing, and machine learning, all of which are the enabling technologies of the future networks, yet to help researchers further improve the efficiently of spectrum sensing. Full article

(This article belongs to the Section Sensor Networks)

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