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Special Issue "Sensors for Distributed Monitoring"

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

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editors

Prof.Dr. Nicola Giaquinto
E-Mail Website
Guest Editor
Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Bari, Italy
Interests: sensors; measurement uncertainty; signal processing; statistical methods; instrumentation
Prof.Dr. Francesco Adamo
E-Mail Website
Guest Editor
Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Bari, Italy
Interests: Measurement Science
Dr. Maurizio Spadavecchia
E-Mail Website
Guest Editor
Department of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy
Interests: smart sensors; measurements on power systems; smart grid; wide area measurements; signal processing; distributed measurement system; energy harvesting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Distributed monitoring has progressively gained in popularity given the growing need for continuous measurements of large structures or regions, e.g., cultivated fields, pipelines, tunnels, and viaducts. The output of this kind of monitoring is a distribution of the physical quantity of the itme of interest (like temperature, strain, moisture, etc.) along the entire structure, or the detection and location of anomalous values of the quantity at any point of the structure.

There are basically two ways to perform distributed monitoring. The first is distributed sensing, which uses cable-like elements (e.g. ,optical fibers) sensitive at every point along their length. The second is distributed sensor networks, which use a large number of sensor nodes with wired or wireless communication to obtain the desired measurements.

This Special Issue is addressed to both types of distributed monitoring. A quality contribution should illustrate a particularly effective solution using one of the two methods and highlights the validity of the proposed methodology for general problems or for a specific application. The reason for preferring either menthod in the discussed case should be outlined.

Prof. Nicola Giaquinto
Prof. Francesco Adamo
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 2200 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

  • Distributed sensing
  • Reflectometric techniques
  • Sensors networks
  • Sensor swarms
  • IoT measurements

Published Papers (2 papers)

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Research

Article
Image Processing Technique for Improving the Sensitivity of Mechanical Register Water Meters to Very Small Leaks
Sensors 2021, 21(21), 7251; https://doi.org/10.3390/s21217251 - 30 Oct 2021
Viewed by 379
Abstract
Discovering very small water leaks at the household level is one of the most challenging goals of smart metering. While many solutions for sudden leakage detection have been proposed to date, the small leaks are still giving researchers a hard time. Even if [...] Read more.
Discovering very small water leaks at the household level is one of the most challenging goals of smart metering. While many solutions for sudden leakage detection have been proposed to date, the small leaks are still giving researchers a hard time. Even if some devices can be found on the market, their capability to detect a water leakage barely reaches the sensitivity of the employed mechanical water meter, which was not designed for detecting small water leakages. This paper proposes a technique for improving the sensitivity of the mechanical register water meters. By implementing this technique in a suitable electronic add-on device, the improved sensitivity could detect very small leaks. This add-on device continuously acquires the mechanical register’s digital images and, thanks to suitable image processing techniques and metrics, allows very small flows to be detected even if lower than the meter starting flow rate. Experimental tests were performed on two types of mechanical water meters, multijet and piston, whose starting flow rates are 8 L/h and 1 L/h, respectively. Results were very interesting in the leakage range of [1.0, 10.0] L/h for the multijet and even in the range [0.25, 1.00] L/h for the piston meter. Full article
(This article belongs to the Special Issue Sensors for Distributed Monitoring)
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Article
Long-Term Performance of Distributed Optical Fiber Sensors Embedded in Reinforced Concrete Beams under Sustained Deflection and Cyclic Loading
Sensors 2021, 21(19), 6338; https://doi.org/10.3390/s21196338 - 22 Sep 2021
Viewed by 510
Abstract
This paper explores the performance of distributed optical fiber sensors based on Rayleigh backscattering for the monitoring of strains in reinforced concrete elements subjected to different types of long-term external loading. In particular, the reliability and accuracy of robust fiber optic cables with [...] Read more.
This paper explores the performance of distributed optical fiber sensors based on Rayleigh backscattering for the monitoring of strains in reinforced concrete elements subjected to different types of long-term external loading. In particular, the reliability and accuracy of robust fiber optic cables with an inner steel tube and an external protective polymeric cladding were investigated through a series of laboratory experiments involving large-scale reinforced concrete beams subjected to either sustained deflection or cyclic loading for 96 days. The unmatched spatial resolution of the strain measurements provided by the sensors allows for a level of detail that leads to new insights in the understanding of the structural behavior of reinforced concrete specimens. Moreover, the accuracy and stability of the sensors enabled the monitoring of subtle strain variations, both in the short-term due to changes of the external load and in the long-term due to time-dependent effects such as creep. Moreover, a comparison with Digital Image Correlation measurements revealed that the strain measurements and the calculation of deflection and crack widths derived thereof remain accurate over time. Therefore, the study concluded that this type of fiber optic has great potential to be used in real long-term monitoring applications in reinforced concrete structures. Full article
(This article belongs to the Special Issue Sensors for Distributed Monitoring)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Detection and Characterization of Discontinuities in Cables with TDR and Neural Networks
Authors: Marco Scarpetta, Maurizio Spadavecchia, Francesco Adamo and Nicola Giaquinto
Affiliation: Department of Electrics and Electronics, Polytechnic of Bari
Abstract: In this paper, a convolutional neural network model for the detection and characterization of capacitive faults in coaxial cables is presented. The neural network analyzes time domain reflectometry signals and outputs predictions of discontinuity points, composed of a class (capacitive fault or line termination), an estimated position and an estimated capacity (only if a capacitive fault is detected). The neural network was trained using a great number of simulated signals, obtained using a transmission line simulator. The transmission line model used in simulations was calibrated using data obtained from stepped-frequency waveform reflectometry measurements, following a novel procedure presented in the paper. After the training procedure, the neural network model was tested on both simulated signals and measured signals, and its detection and accuracy performance were assessed.

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