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Animal Borne Sensor Applications
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Animal Borne Sensor Applications

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

Deadline for manuscript submissions: closed (15 October 2020) | Viewed by 7726

Special Issue Editors

Dr. Andrew Markham

E-Mail Website
Guest Editor
Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford OX1 3QD, UK
Interests: embedded systems; Internet of things; machine learning; animal tracking
Dr. Michele Thums

E-Mail Website
Guest Editor
Australian Institute of Marine Science, Indian Ocean Marine Research Centre (M096), University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
Interests: animal movement behaviour; foraging ecology; marine megafauna; spatial and ecological modelling
Dr. Amir Patel

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Cape Town, Cape Town 7701, South Africa
Interests: bio-inspired robotics; trajectory optimisation; sensor fusion

Special Issue Information

Dear Colleagues,

Advances in sensor technology, through increased miniaturisation, reduction in cost, and exponential gains in computational capacity and memory, have led to a revolution in measuring the natural world with increasing spatial and temporal resolution. This is particularly apparent in the field of animal-borne sensing, where stringent constraints on size, weight, and hydrodynamic/aerodynamic drag have previously limited the applicability to larger species. New sensor modalities, ranging from miniature cameras to wearable microphones and accelerometers, are enabling rich and diverse measurements to be acquired, providing new insights into animal behaviour in the wild. Advances in microcontroller architecture and design are reducing power consumption whilst simultaneously increasing processing capability. Novel energy-harvesting approaches are allowing sensors to run for longer periods in the wild. Wireless transceivers are allowing more data to be gathered from sensors in the field, shifting from a reliance on archival loggers to real-time information. Coupled with this increasing capability is a deluge of data, which requires new algorithmic approaches to make sense of it. These devices have the capability to provide unparalleled information of animal behaviour, allowing for new science and more informed conservation and management decisions to be made.

This Special Issue is open to all researchers. Articles may include, but are not limited to, the following topics:

  • New sensor modalities for animal-borne applications;
  • New frontiers for animal-borne sensing applications;
  • Hardware and firmware optimisation and considerations;
  • Energy harvesting for long-term animal-borne deployments;
  • Wireless networks (IoT, mesh, satellite, etc.) for animal-borne applications;
  • Onboard and real-time considerations for data analysis, compression, and processing;
  • Machine learning and other novel techniques for deriving insights from animal-borne datasets;
  • Frameworks and architectures for animal-borne applications;
  • New applications for animal-borne sensing

Dr. Andrew Markham
Dr. Michele Thums
Dr. Amir Patel
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

  • animal-borne sensing
  • Internet of things
  • embedded systems
  • energy harvesting
  • pattern recognition
  • machine learning
  • deep learning
  • marine sensing
  • terrestrial sensing
  • positioning
  • activity recognition
  • biologging
  • animal movement

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Published Papers (2 papers)

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Research

14 pages, 753 KiB  
Article
Design of Deployment Strategies to Monitor the Movement of Animals with Passive Electronic Devices
by Laila D. Kazimierski, Jorge P. Rodríguez and Víctor M. Eguíluz
Sensors 2021, 21(2), 326; https://doi.org/10.3390/s21020326 - 6 Jan 2021
Viewed by 2224
Abstract
Current animal monitoring systems have improved our knowledge of quantitative animal ecology. There are many electronic tracking technologies such as VHF/UHF telemetry, light-level geolocation, ARGOS satellite telemetry and GPS tracking. To reach the desired level of information retrieval requires the planning of adequate [...] Read more.
Current animal monitoring systems have improved our knowledge of quantitative animal ecology. There are many electronic tracking technologies such as VHF/UHF telemetry, light-level geolocation, ARGOS satellite telemetry and GPS tracking. To reach the desired level of information retrieval requires the planning of adequate equipment effort and coverage, which depends on the properties of the system. We propose an equipment arrangement model consisting of a given number of receiver stations in a two-dimensional space in which the animals move according to a central place movement model. The objective is to characterize how the transmission of tracking data depends on the movement of the animals and the design of the equipment deployment: quantity and location of the receiver stations and their associated reception radius. We also implement the model using real trajectories of southern elephant seals and Australian sea lions publicly available online and tracked during the years 2010–2012. We characterize the data transmission based on different equipment configurations and we obtained analogous results to the theoretical model. Full article
(This article belongs to the Special Issue Animal Borne Sensor Applications)
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19 pages, 5324 KiB  
Article
Assessment of Machine Learning Models to Identify Port Jackson Shark Behaviours Using Tri-Axial Accelerometers
by Julianna P. Kadar, Monique A. Ladds, Joanna Day, Brianne Lyall and Culum Brown
Sensors 2020, 20(24), 7096; https://doi.org/10.3390/s20247096 - 11 Dec 2020
Cited by 12 | Viewed by 4890
Abstract
Movement ecology has traditionally focused on the movements of animals over large time scales, but, with advancements in sensor technology, the focus can become increasingly fine scale. Accelerometers are commonly applied to quantify animal behaviours and can elucidate fine-scale (<2 s) behaviours. Machine [...] Read more.
Movement ecology has traditionally focused on the movements of animals over large time scales, but, with advancements in sensor technology, the focus can become increasingly fine scale. Accelerometers are commonly applied to quantify animal behaviours and can elucidate fine-scale (<2 s) behaviours. Machine learning methods are commonly applied to animal accelerometry data; however, they require the trial of multiple methods to find an ideal solution. We used tri-axial accelerometers (10 Hz) to quantify four behaviours in Port Jackson sharks (Heterodontus portusjacksoni): two fine-scale behaviours (<2 s)—(1) vertical swimming and (2) chewing as proxy for foraging, and two broad-scale behaviours (>2 s–mins)—(3) resting and (4) swimming. We used validated data to calculate 66 summary statistics from tri-axial accelerometry and assessed the most important features that allowed for differentiation between the behaviours. One and two second epoch testing sets were created consisting of 10 and 20 samples from each behaviour event, respectively. We developed eight machine learning models to assess their overall accuracy and behaviour-specific accuracy (one classification tree, five ensemble learners and two neural networks). The support vector machine model classified the four behaviours better when using the longer 2 s time epoch (F-measure 89%; macro-averaged F-measure: 90%). Here, we show that this support vector machine (SVM) model can reliably classify both fine- and broad-scale behaviours in Port Jackson sharks. Full article
(This article belongs to the Special Issue Animal Borne Sensor Applications)
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