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Sensors Application on Early Warning System
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Sensors Application on Early Warning System

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 46243

Special Issue Editors

Dr. Stefan Poslad

E-Mail Website
Guest Editor
Director, IoT2US Lab, Queen Mary University of London, London, UK
Interests: Internet of Things; ubiquitous computing; smart environments; spatial-awareness; pervasive games; security; privacy
Special Issues, Collections and Topics in MDPI journals
Dr. Stuart E. Middleton

E-Mail Website
Guest Editor
Electronics and Computer Science, University of Southampton, IT Innovation Centre, Gamma House, Enterprise Road, Southampton SO16 7NS, UK
Interests: computational linguistics, information extraction, social media analytics, user-generated content, volunteer geographic information, semantics, sensor fusion, decision support systems
Dr. Öcal Necmioğlu

E-Mail Website
Guest Editor
Regional Earthquake and Tsunami Monitoring Center, Kandilli Observatory and Earthquake Research Institute, Boğaziçi University, Istanbul-Turkey
Interests: tsunami and earthquake early warning, tsunami hazard-risk assessment, tsunami preparedness-mitigation

Special Issue Information

Dear Colleagues,

The focus and scope of this Special Issue (SI) is on advances in sensor applications for Early Warning Systems (EWS).

Although, Early Warning Systems (EWS) have been traditionally targeted at physical environment disaster applications, they are increasingly being deployed to detect and warn in advance about a wider range of events in other domains such as transport, maritime, medicine, finance etc. EWS applications include any warning or alerting systems, such as financial event prediction, vehicular collision warnings, alerts for likelihood of infrastructure failure, identification of early indicators for disease outbreaks in a population, personalized patient risk predictions and alerts for safety (e.g., animal and human overcrowding, stampedes) and security threats.  

This SI targets innovations that support any of the main EWS functions: event prediction and risk analysis, monitoring and warning, dissemination of warnings and response planning.  

It also includes new methods and designs for: systems based upon Internet of Things and Cyber Physical System; EWS processes including prediction and risk analysis; simulations and digital twins; sensor data acquisition; EWS sensor data fusion; data analytics, data science and AI (e.g., deep learning), data visualisation, and decision support. Note, accepted papers need to have a viable sensing element; they can’t be just about the data analysis part.

Dr. Stefan Poslad
Dr. Stuart E. Middleton
Dr. Öcal Necmioğlu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Early Warming System
  • Internet of Things
  • Cyber Physical System
  • Event Prediction
  • Data Science
  • Sensors

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Published Papers (6 papers)

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Research

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18 pages, 3972 KiB  
Article
Study on the Vibration Characteristics of the Telescope T80 in the Javalambre Astrophysical Observatory (JAO) Aimed at Detecting Invalid Images
by Fernando Arranz Martínez, Raúl Martín Ferrer, Guillermo Palacios-Navarro and Pedro Ramos Lorente
Sensors 2020, 20(22), 6523; https://doi.org/10.3390/s20226523 - 15 Nov 2020
Cited by 3 | Viewed by 2443
Abstract
The location of large telescopes, generally far from the data processing centers, represents a logistical problem for the supervision of the capture of images. In this work, we carried out a preliminary study of the vibration signature of the T80 telescope at the [...] Read more.
The location of large telescopes, generally far from the data processing centers, represents a logistical problem for the supervision of the capture of images. In this work, we carried out a preliminary study of the vibration signature of the T80 telescope at the Javalambre Astrophysical Observatory (JAO). The study analyzed the process of calculating the displacement that occurs because of the vibration in each of the frequencies in the range of interest. We analyzed the problems associated with very low frequencies by means of simulation, finding the most critical vibrations below 20 Hz, since they are the ones that generate greater displacements. The work also relates previous studies based on simulation with the real measurements of the vibration of the telescope taken remotely when it is subjected to different positioning movements (right ascension and/or declination) or when it performs movement actions such as those related to filter trays or mirror cover. The obtained results allow us to design a remote alarm system to detect invalid images (taken with excess vibration). Full article
(This article belongs to the Special Issue Sensors Application on Early Warning System)
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26 pages, 7239 KiB  
Article
Flash Flood Early Warning System in Colima, Mexico
by José Ibarreche, Raúl Aquino, R. M. Edwards, Víctor Rangel, Ismael Pérez, Miguel Martínez, Esli Castellanos, Elisa Álvarez, Saul Jimenez, Raúl Rentería, Arthur Edwards and Omar Álvarez
Sensors 2020, 20(18), 5231; https://doi.org/10.3390/s20185231 - 14 Sep 2020
Cited by 17 | Viewed by 10461
Abstract
This paper presents a system of sensors used in flash flood prediction that offers critical real-time information used to provide early warnings that can provide the minutes needed for persons to evacuate before imminent events. Flooding is one of the most serious natural [...] Read more.
This paper presents a system of sensors used in flash flood prediction that offers critical real-time information used to provide early warnings that can provide the minutes needed for persons to evacuate before imminent events. Flooding is one of the most serious natural disasters humans confront in terms of loss of life and results in long-term effects, which often have severely adverse social consequences. However, flash floods are potentially more dangerous to life because there is often little or no forewarning of the impending disaster. The Emergency Water Information Network (EWIN) offers a solution that integrates an early warning system, notifications, and real-time monitoring of flash flood risks. The platform has been implemented in Colima, Mexico covering the Colima and Villa de Alvarez metropolitan area. This platform consists of eight fixed riverside hydrological monitoring stations, eight meteorological stations, nomadic mobile monitoring stations called “drifters” used in the flow, and a sniffer with data muling capability. The results show that this platform effectively compiles and forwards information to decision-makers, government officials, and the general public, potentially providing valuable minutes for people to evacuate dangerous areas. Full article
(This article belongs to the Special Issue Sensors Application on Early Warning System)
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16 pages, 521 KiB  
Article
Extended Target Echo Detection Based on KLD and Wigner Matrices
by Dingsu Xie, Fei Wang and Jun Chen
Sensors 2019, 19(24), 5385; https://doi.org/10.3390/s19245385 - 6 Dec 2019
Cited by 4 | Viewed by 2595
Abstract
With the development of airborne radar radio frequency stealth (RFS) technology, the method of improving the RFS performance of airborne radar by optimizing target detection performance has been extensively studied. However, for wideband radar signals, the traditional point target model appears as an [...] Read more.
With the development of airborne radar radio frequency stealth (RFS) technology, the method of improving the RFS performance of airborne radar by optimizing target detection performance has been extensively studied. However, for wideband radar signals, the traditional point target model appears as an extended target model in the range-dimension, which is unfavorable to the detection of target echoes. To overcome the existing drawbacks, this paper devises an efficient echo detection algorithm from the perspective of information theory and random matrix. Firstly, aperiodic agile wideband radar signals are utilized to observe targets. Then, one frame of echo signals in the same range gate is reconstructed into a data form conforming to the Wigner matrix spectral decomposition. Finally, according to the signal detection theory, Kullback-Leibler Divergence (KLD) is used as the test statistic to complete the echo detection of the stealthy extended targets. By statistical analysis and comparison with other established echo detection algorithms, simulation results manifest that the proposed algorithm has superior detection performance and strong robustness, which not only makes up for the deficiency of traditional narrowband radar detection algorithms, but also increases the detection probability of radar system when it is faced with stealthy extended targets. Full article
(This article belongs to the Special Issue Sensors Application on Early Warning System)
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29 pages, 7399 KiB  
Article
An Internet of Things (IoT) Application on Volcano Monitoring
by Shadia Awadallah, David Moure and Pedro Torres-González
Sensors 2019, 19(21), 4651; https://doi.org/10.3390/s19214651 - 26 Oct 2019
Cited by 36 | Viewed by 6377
Abstract
In the last few years, there has been a huge interest in the Internet of Things (hereinafter IoT) field. Among the large number of IoT technologies, the low-power wide-area network (hereinafter LPWAN) has emerged providing low power, low data-rate communication over long distances, [...] Read more.
In the last few years, there has been a huge interest in the Internet of Things (hereinafter IoT) field. Among the large number of IoT technologies, the low-power wide-area network (hereinafter LPWAN) has emerged providing low power, low data-rate communication over long distances, enabling battery-operated devices to operate for long time periods. This paper introduces an application of long-range (hereinafter LoRa) technology, one of the most popular LPWANs, to volcanic surveillance. The first low-power and low-cost wireless network based on LoRa to monitor the soil temperature in thermal anomaly zones in volcanic areas has been developed. A total of eight thermometers (end devices) have been deployed on a Teide volcano in Tenerife (Canary Islands). In addition, a repeater device was developed to extend the network range when the gateway did not have a line of sight connection with the thermometers. Combining LoRa communication capabilities with microchip microcontrollers (end devices and repeater) and a Raspberry Pi board (gateway), three main milestones have been achieved: (i) extreme low-power consumption, (ii) real-time and proper temperature acquisition, and (iii) a reliable network operation. The first results are shown. These results provide enough quality for a proper volcanic surveillance. Full article
(This article belongs to the Special Issue Sensors Application on Early Warning System)
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25 pages, 11434 KiB  
Article
Robust Construction Safety System (RCSS) for Collision Accidents Prevention on Construction Sites
by Byung-Wan Jo, Yun-Sung Lee, Rana Muhammad Asad Khan, Jung-Hoon Kim and Do-Keun Kim
Sensors 2019, 19(4), 932; https://doi.org/10.3390/s19040932 - 22 Feb 2019
Cited by 23 | Viewed by 7394
Abstract
A proximity warning system to detect the presence of a worker/workers and to warn heavy equipment operators is highly needed to prevent collision accidents at construction sites. In this paper, we developed a robust construction safety system (RCSS), which can activate warning devices [...] Read more.
A proximity warning system to detect the presence of a worker/workers and to warn heavy equipment operators is highly needed to prevent collision accidents at construction sites. In this paper, we developed a robust construction safety system (RCSS), which can activate warning devices and automatically halt heavy equipment, simultaneously, to prevent possible collision accidents. The proximity detection of this proposed system mainly relies on ultra-wideband (UWB) sensing technologies, which enable instantaneous and simultaneous alarms on (a) a worker’s personal safety (personal protection unit (PPU)) device and (b) hazard area device (zone alert unit (ZAU)). This system also communicates with electronic control sensors (ECSs) installed on the heavy equipment to stop its maneuvering. Moreover, the RCSS has been interfaced with a global positioning system communication unit (GCU) to acquire real-time information of construction site resources and warning events. This enables effective management of construction site resources using an online user interface. The performance and effectiveness of the RCSS have been validated at laboratory scale as well as at real field (construction site and steel factory). Conclusively, the RCSS can significantly enhance construction site safety by pro-actively preventing collision of a worker/workers with heavy equipment. Full article
(This article belongs to the Special Issue Sensors Application on Early Warning System)
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Review

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39 pages, 1357 KiB  
Review
Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
by Marco Esposito, Lorenzo Palma, Alberto Belli, Luisiana Sabbatini and Paola Pierleoni
Sensors 2022, 22(6), 2124; https://doi.org/10.3390/s22062124 - 9 Mar 2022
Cited by 72 | Viewed by 14665
Abstract
Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have [...] Read more.
Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures. Full article
(This article belongs to the Special Issue Sensors Application on Early Warning System)
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