Academic Journal
Resource-efficient data transmission for WiFi-capable bio-loggers based on machine learning.
| Τίτλος: | Resource-efficient data transmission for WiFi-capable bio-loggers based on machine learning. |
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| Συγγραφείς: | Kerle-Malcharek W; Department of Computer and Information Science, University of Konstanz, Konstanz, Germany., Klein K; Department of Computer and Information Science, University of Konstanz, Konstanz, Germany., Wikelski M; Department of Migration, Max Planck Institute of Animal Behavior, Radolfzell, Germany.; Department of Biology, University of Konstanz, Konstanz, Germany., Schreiber F; Department of Computer and Information Science, University of Konstanz, Konstanz, Germany.; Faculty of Information Technology, Monash University, Clayton, Australia., Wild TA; Department of Migration, Max Planck Institute of Animal Behavior, Radolfzell, Germany. |
| Πηγή: | PloS one [PLoS One] 2026 Jul 24; Vol. 21 (7), pp. e0354146. Date of Electronic Publication: 2026 Jul 24 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| Ιατρικοί όροι (MeSH): | Data Collection*/methods , Machine Learning*, Animals ; Decision Trees |
| Περίληψη: | Bio-logging is a popular method for data collection in animal research, especially for hard-to-observe animals. Newer bio-logger generations utilise WiFi technology, enabling researchers to collect high-resolution data at the cost of higher energy expenditure of the devices. In this study, we elaborate on how state-of-the-art loggers can benefit from even the simplest methods to reduce transmission costs. We employ machine learning techniques, specifically small decision trees, to enable a bio-logger to recognise a chosen behaviour based on its sensor readings. Based on the recognised behaviour, the logger filters which data to transmit, reducing transmission time and, thus, the logger's overall energy consumption. Using a controlled dataset, we exemplify the training and evaluation of such decision trees. Using those, we evaluate the reduction of energy consumption based on a state-of-the-art bio-logger, the WildFi tag. We demonstrate that for WiFi-enabled bio-loggers, decision trees are highly beneficial when used as a data filter. We illustrate that filtering with decision trees yields energy savings of 14.68% in realistic scenarios for transmitting data. We provide a full pipeline from data collection to deployable software to holistically elaborate on how to use off-the-shelf solutions to achieve practical gains for animal behaviour. Our results suggest that decision trees can be an effective tool for enabling bio-loggers to detect specific behaviours. Lastly, we emphasise that our approach highly benefits from the use of gyroscopes, a sensor type that mostly sees use for off-board instead of on-board labour. We contribute an investigation of energy consumption reduction of WiFi-enabled bio-loggers through the utilisation of controlled data transmission using machine learning. We offer a promising pathway for enhancing the longevity of such a state-of-the-art bio-logger, maintaining WiFi benefits. Ultimately, we support more efficient and, thus, more sustainable wildlife monitoring practices on the example of the WildFi tag. (Copyright: © 2026 Kerle-Malcharek et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Competing Interests: | The authors have declared that no competing interests exist. |
| Entry Date(s): | Date Created: 20260724 Date Completed: 20260724 Latest Revision: 20260727 |
| Update Code: | 20260727 |
| PubMed Central ID: | PMC13399348 |
| DOI: | 10.1371/journal.pone.0354146 |
| PMID: | 42497112 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0354146 |