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Special Issue "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: 15 September 2020.

Special Issue Editors

Dr. Stefan Poslad
Website
Guest Editor
IoT2US Lab Director, Queen Mary University of London Mile End Road, London, E1 4NS, UK
Interests: internet of things; ubiquitous computing; privacy and security
Special Issues and Collections in MDPI journals
Dr. Stuart E. Middleton
Website
Guest Editor
University of Southampton, Electronics and Computer Science. 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
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 papers will be 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 2000 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

Published Papers (3 papers)

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Research

Open AccessArticle
Extended Target Echo Detection Based on KLD and Wigner Matrices
Sensors 2019, 19(24), 5385; https://doi.org/10.3390/s19245385 - 06 Dec 2019
Cited by 1
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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Open AccessArticle
An Internet of Things (IoT) Application on Volcano Monitoring
Sensors 2019, 19(21), 4651; https://doi.org/10.3390/s19214651 - 26 Oct 2019
Cited by 5
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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Open AccessArticle
Robust Construction Safety System (RCSS) for Collision Accidents Prevention on Construction Sites
Sensors 2019, 19(4), 932; https://doi.org/10.3390/s19040932 - 22 Feb 2019
Cited by 2
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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