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Acoustic Source Localization in Wireless Sensor Networks
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Acoustic Source Localization in Wireless Sensor Networks

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

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 10334

Special Issue Editors

Prof. Dr. An Braeken

E-Mail Website
Guest Editor
Department of Engineering Technology (INDI), Vrije Universiteit Brussel, Brussels, Belgium
Interests: wireless sensor networks; Internet of Things; localization; security; privacy; cloud technology
Prof. Dr. Abdellah Touhafi

E-Mail Website
Guest Editor
Department of Engineering Sciences and Technology (INDI), Vrije Universiteit Brussel, 1050 Brussels, Belgium
Interests: IoT; reconfigurable computing; remote sensing; environmental monitoring; IoT for education; embedded energy harvesting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sound source detection, localization, and tracking have many applications in a variety of fields, like defense, robotics, industry, and security. Classical examples are, for instance, sniper localization, automatic tracking of speakers in a meeting, intruder detection, etc.

The main advantage of using acoustic sound sources for these applications is that it is a passive method, which does not require the collaboration or awareness of the object under study. Often, the methods are also used as a complement to the classical surveillance methods by means of cameras in case the visibility is limited or even optically not visible.

Wireless sensor networks consisting of nodes, which incorporate one or more acoustic transducers and a wireless communication module, and are able to process and manipulate audio signals, are also called wireless acoustic sensor networks. They have a very high added value in providing acoustic source localization, as they enable the offering of good coverage, which results in good results for the accuracy and robustness of the obtained measurements. The actual development of a wireless acoustic sensor network entails several challenges with respect to bandwidth, computational requirements at the nodes, synchronization issues among the nodes, etc. This Special Issue aims at collecting high-quality research papers and review articles focusing on recent advances in techniques, architectures, prototypes, applications, and evaluations of wireless acoustic source localization. Original, high quality contributions that have not been published before and are not currently under review by other journals or conferences are sought.

Potential topics of interest include but are not limited to the following:

  • Methods for acoustic source localization based on received signal strength (RSS);
  • Methods for acoustic source localization based on angle of arrival (AOA);
  • Methods for acoustic source localization based on time of arrival (TOA) or time difference of arrival (TDOA);
  • Methods for acoustic source localization based on the steered response power (SRP);
  • Techniques to handle multiple source localization;
  • Techniques for source counting;
  • Self-localization of acoustic sensor nodes;
  • Techniques for synchronization among the nodes;
  • Prototypes for enabling acoustic source detection, ranging, localization, and tracking;
  • Underwater acoustic source localization;
  • Evaluation/comparison of existing systems;
  • Applications and application domains for acoustic source localization;
  • Visualization (2D and 3D) for acoustic source localization;
  • Machine learning-based techniques to enhance acoustic source localization;
  • Threats, security issues, and mechanisms in acoustic source localization;
  • Routing protocols for acoustic source localization;
  • Low-power acoustic sensor array design for sensor networks;
  • Self-powering acoustic sensors for ultra-low-power acoustic sensing.

Prof. Dr. An Braeken
Prof. Dr. Abdellah Touhafi
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

  • Wireless acoustic sensor networks
  • Localization, ranging, and detection
  • RSS, AOA, TOA, TDOA, and SRP
  • Machine learning.

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Published Papers (3 papers)

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16 pages, 4160 KiB  
Article
Use of PVDF Wire Sensors for Leakage Localization in a Fluid-Filled Pipe
by Pingling Sun, Yan Gao, Boao Jin and Michael J. Brennan
Sensors 2020, 20(3), 692; https://doi.org/10.3390/s20030692 - 27 Jan 2020
Cited by 12 | Viewed by 4608
Abstract
The detection and location of pipeline leakage can be deduced from the time difference between the arrival leak signals measured by sensors placed at the pipe access points on either side of a suspected leak. Progress has been made in this area to [...] Read more.
The detection and location of pipeline leakage can be deduced from the time difference between the arrival leak signals measured by sensors placed at the pipe access points on either side of a suspected leak. Progress has been made in this area to offer a potential improvement over the conventional cross-correlation method for time delay estimation. This paper is concerned with identifying suitable sensors that can be easily deployed to monitor the pipe vibration due to the propagation of leak noise along the pipeline. In response to this, based on the low-frequency propagation characteristics of leak noise in our previous study, polyvinylidene fluoride (PVDF) wire sensors are proposed as a potential solution to detect the pipeline leak signals. Experimental investigations were carried out at a leak detection pipe rig built in the Chinese Academy of Sciences. Their performances for leak detection were shown in comparison with hydrophones. It is suggested that with special considerations given to aspects pertaining to non-intrusive deployment and low cost, the PVDF wire sensors are of particular interest and may lead to a promising replacement for commercial leak noise transducers. Full article
(This article belongs to the Special Issue Acoustic Source Localization in Wireless Sensor Networks)
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Other

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17 pages, 13984 KiB  
Letter
A New Method for Acoustic Priority Vehicle Detection Based on a Self-Powering Triboelectric Acoustic Sensor Suitable for Low-Power Wireless Sensor Networks
by Quentin Quevy, Gianluca Cornetta and Abdellah Touhafi
Sensors 2021, 21(1), 158; https://doi.org/10.3390/s21010158 - 29 Dec 2020
Cited by 5 | Viewed by 2643
Abstract
Traffic congestion is, on a daily basis, responsible for a significant amount of economic and social costs. One of the critical examples is the obstruction of priority vehicles during fast trajectories, which potentially costs lives and property in case of delay that is [...] Read more.
Traffic congestion is, on a daily basis, responsible for a significant amount of economic and social costs. One of the critical examples is the obstruction of priority vehicles during fast trajectories, which potentially costs lives and property in case of delay that is too great. By means of visual sensing methods, solutions and schedules have already been proposed for adjusting traffic light sequences depending on a priority vehicle’s position. However, these mechanisms are computation and power intensive. Deploying and powering a large-scale network will have a crucial economical cost. Furthermore, these devices will not always have access to sufficient power. To provide a solution, we developed an acoustic and self-powered device that can detect priority vehicles and can be cost effectively deployed to define a sensor network. The device combines the detection of priority vehicles and the harvesting of sound energy through triboelectrification. This paper will introduce the use of triboelectric energy harvesting, specifically in a self-powered wireless sensor network for priority vehicle detection. Furthermore, it shows how to increase the power performance of such a generator. Finally, the results are analyzed. Full article
(This article belongs to the Special Issue Acoustic Source Localization in Wireless Sensor Networks)
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10 pages, 810 KiB  
Letter
Configuration-Invariant Sound Localization Technique Using Azimuth-Frequency Representation and Convolutional Neural Networks
by Chanjun Chun, Kwang Myung Jeon and Wooyeol Choi
Sensors 2020, 20(13), 3768; https://doi.org/10.3390/s20133768 - 5 Jul 2020
Cited by 3 | Viewed by 2353
Abstract
Deep neural networks (DNNs) have achieved significant advancements in speech processing, and numerous types of DNN architectures have been proposed in the field of sound localization. When a DNN model is deployed for sound localization, a fixed input size is required. This is [...] Read more.
Deep neural networks (DNNs) have achieved significant advancements in speech processing, and numerous types of DNN architectures have been proposed in the field of sound localization. When a DNN model is deployed for sound localization, a fixed input size is required. This is generally determined by the number of microphones, the fast Fourier transform size, and the frame size. if the numbers or configurations of the microphones change, the DNN model should be retrained because the size of the input features changes. in this paper, we propose a configuration-invariant sound localization technique using the azimuth-frequency representation and convolutional neural networks (CNNs). the proposed CNN model receives the azimuth-frequency representation instead of time-frequency features as the input features. the proposed model was evaluated in different environments from the microphone configuration in which it was originally trained. for evaluation, single sound source is simulated using the image method. Through the evaluations, it was confirmed that the localization performance was superior to the conventional steered response power phase transform (SRP-PHAT) and multiple signal classification (MUSIC) methods. Full article
(This article belongs to the Special Issue Acoustic Source Localization in Wireless Sensor Networks)
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