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Advanced Environmental Sensing Towards Acoustic Monitoring and Modeling: Applications and Challenges

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 1579

Special Issue Editor

Dr. Elena Ascari

E-Mail Website
Guest Editor
Institute for Chemical-Physical Processes of the Italian Research Council (CNR-IPCF), Via Giuseppe Moruzzi 1, 56124 Pisa, Italy
Interests: environmental acoustics; noise mapping and action planning; people exposure to noise and annoyance; road traffic noise
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, we have seen a growing interest in advanced environmental sensing for acoustic monitoring. Acoustic monitoring with innovative methods and sensors is challenging but offers a large variety of applications and new approaches in several fields of acoustics, from eco and bio acoustic to environmental noise pollution monitoring. Advances in AI and ML methods have enhanced the use of sensors towards the optimization of costs and computational efforts. To this aim, the edge–cloud continuum is still a challenge with few experiences in the field of acoustics.

This Special Issue therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of environmental sensors for acoustic monitoring.

Potential topics include, but are not limited to, the following:

  • Acoustic sensors for bioacoustics;
  • Acoustic sensors for and ecoacoustics;
  • Acoustic sensors for noise pollution;
  • Sensor network and event detection;
  • Sensor network and event classification;
  • AI and ML methods in acoustic sensor network;
  • Low-cost sensors and citizen science approach;
  • Cloud–edge computing for environmental monitoring.

Dr. Elena Ascari
Guest Editor

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

  • environmental sensing
  • acoustic monitoring
  • bioacoustics
  • ecoacoustics
  • noise pollution monitoring
  • acoustic sensors

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Published Papers (3 papers)

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Research

19 pages, 6732 KiB  
Article
Improvement and Validation of a Smart Road Traffic Noise Model Based on Vehicles Tracking Using Image Recognition: EAgLE 3.0
by Claudio Guarnaccia, Ulysse Catherin, Aurora Mascolo and Domenico Rossi
Sensors 2025, 25(6), 1750; https://doi.org/10.3390/s25061750 - 12 Mar 2025
Viewed by 418
Abstract
Noise coming from road traffic represents a major contributor to the high levels of noise to which people are continuously exposed—especially in urban areas—throughout all of Europe. Since it represents a very detrimental pollutant, the assessment of such noise is an important procedure. [...] Read more.
Noise coming from road traffic represents a major contributor to the high levels of noise to which people are continuously exposed—especially in urban areas—throughout all of Europe. Since it represents a very detrimental pollutant, the assessment of such noise is an important procedure. Noise levels can be measured or simulated, and, in this second case, for the building of a valid model, a proper collection of input data cannot be left out of consideration. In this paper, the authors present the development of a methodology for the collection of the main inputs for a road traffic noise model, i.e., vehicle number, category, and speed, from a video recording of traffic on an Italian highway. Starting from a counting and recognition tool already available in the literature, a self-written Python routine based on image inference has been developed for the instantaneous detection of the position and speed of vehicles, together with the categorization of vehicles (light or heavy). The obtained data are coupled with the CNOSSOS-EU model to estimate the noise power level of a single vehicle and, ultimately, the noise impact of traffic on the selected road. The results indicate good performance from the proposed model, with a mean error of −1.0 dBA and a mean absolute error (MAE) of 3.6 dBA. Full article
(This article belongs to the Special Issue Advanced Environmental Sensing Towards Acoustic Monitoring and Modeling: Applications and Challenges)
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23 pages, 5098 KiB  
Article
Rhythms, Patterns and Styles in the Jaw Movement Activity of Beef Cattle on Rangeland as Revealed by Acoustic Monitoring
by Eugene David Ungar and Ynon Nevo
Sensors 2025, 25(4), 1210; https://doi.org/10.3390/s25041210 - 17 Feb 2025
Viewed by 423
Abstract
Grazing shapes rangelands globally, but it is difficult to study. Acoustic monitoring enables grazing to be described in terms of jaw movements, which are fundamental to how herbivores interact with their foraging environment. In an observational study on Mediterranean herbaceous rangeland, 10 beef [...] Read more.
Grazing shapes rangelands globally, but it is difficult to study. Acoustic monitoring enables grazing to be described in terms of jaw movements, which are fundamental to how herbivores interact with their foraging environment. In an observational study on Mediterranean herbaceous rangeland, 10 beef cattle cows were monitored continuously over multiple days in two seasons. The algorithm used to analyze the acoustic signal furnished (without classification) a data sample of ≈5 M ingestive and ruminatory jaw movements. These were analyzed as between-event intervals and as minutely rates. The rumination displayed a consistent, strong rhythm and pattern of jaw movements. In contrast, there was no single “signature” jaw movement pattern for grazing (i.e., non-rumination). Although the underlying natural rhythm of rumination dominated non-rumination, it was intermittently and irregularly interrupted by longer intervals, whose size scaled logarithmically. There was evidence of further substructure, with a degree of separation between “grazing” and “resting” in the conventional sense. Three broad grazing styles emerged. In the “intense” style, animals sustained long runs of jaw movements in the natural rhythm, with relatively few interruptions. In the “regular” style, comprising the majority of non-rumination jaw activity, the natural rhythm still dominated, but was punctuated at irregular intervals by eruptions of somewhat longer intervals. The “diffuse” style comprised shorter runs in the natural rhythm, punctuated by highly erratic intervals spanning orders of magnitude. When the jaw movement events were viewed as minutely rates, the non-rumination population showed strong bimodality in the distribution of non-zero rates, with peaks at ≈60 and ≈15 jaw movements min−1, suggesting two modes of grazing. The results strongly support the notion of behavioral grazing intensity and call into question the approach of viewing grazing as a binary state or expecting measures of grazing time to be strongly indicative of intake rate. Rate- and interval-based analyses of information at the jaw movement level can yield a penetrating profile of how an animal interacts with its foraging environment, epitomized in a graphical formulation termed the time accumulation curve. These results strengthen the case for the further development of this sensor technology. Full article
(This article belongs to the Special Issue Advanced Environmental Sensing Towards Acoustic Monitoring and Modeling: Applications and Challenges)
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21 pages, 4025 KiB  
Article
What Is Grazing Time? Insights from the Acoustic Signature of Goat Jaw Activity in Wooded Landscapes
by Eugene David Ungar and Reuven Horn
Sensors 2025, 25(1), 8; https://doi.org/10.3390/s25010008 - 24 Dec 2024
Cited by 1 | Viewed by 542
Abstract
Acoustic monitoring facilitates the detailed study of herbivore grazing by generating a timeline of sound bursts associated with jaw movements (JMs) that perform bite or chew actions. The unclassified stream of JM events was used here in an observational study to explore the [...] Read more.
Acoustic monitoring facilitates the detailed study of herbivore grazing by generating a timeline of sound bursts associated with jaw movements (JMs) that perform bite or chew actions. The unclassified stream of JM events was used here in an observational study to explore the notion of “grazing time”. Working with shepherded goat herds in a wooded landscape, a horn-based acoustic sensor with a vibration-type microphone was deployed on a volunteer animal along each of 12 foraging routes. The software-generated timeline of unclassified JMs contained a total of 334,582 events. After excluding rumination bouts, minutely JM rates showed a broad, non-normal distribution, with an overall mean of 61 JM min−1. The frequency distribution of inter-JM interval values scaled logarithmically, with a peak in the region of 0.43 s representing a baseline interval that generates the unconstrained, more-or-less regular, rhythm of jaw movement (≈140 JM min−1). This rhythm was punctuated by interruptions, for which duration scaled logarithmically, and which were primarily related to the search phase of the intake process. The empirical time accumulation curve shows the contribution of the inter-JM interval to the total foraging time and provides a penetrating profile of how the animal interacted with the foraging environment. The sum total of time along a foraging route spent at a near-potential JM rate was only ≈1 h, whereas sub-potential rates containing intervals as long as ≈30 s accounted for the bulk of the foraging route. The dimensionless behavioral grazing intensity was defined as the product of the number of ingestive JMs performed and the baseline interval, divided by the duration of the foraging route (excluding rumination). Values were mostly <0.5 for the foraging routes examined. This has implications for how animal presence should be translated to grazing pressure and for how long animals need to forage to meet their nutritional requirements. Full article
(This article belongs to the Special Issue Advanced Environmental Sensing Towards Acoustic Monitoring and Modeling: Applications and Challenges)
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