LoRaWAN Implementation for Acoustic Localization of Coyotes
Wild coyotes in the United States cause serious damage to pets, livestock, and even humans. Currently, there are solutions to this problem like wildlife image detection [1]. However, a benefit to detecting coyotes remotely with sound is prevention: users can detect coyotes before they attack. This p...
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| Published in: | 2023 23rd International Conference on Control, Automation and Systems (ICCAS) pp. 782 - 787 |
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| Main Authors: | , , , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
ICROS
17-10-2023
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Wild coyotes in the United States cause serious damage to pets, livestock, and even humans. Currently, there are solutions to this problem like wildlife image detection [1]. However, a benefit to detecting coyotes remotely with sound is prevention: users can detect coyotes before they attack. This project aims to detect, classify, and locate wild coyotes with acoustic sensors within a certain area, to avoid damage from coyotes in a cost-effective manner. The acoustic sensors were deployed on a Long Range Wide Area Network (LoRaWAN) to cover a large area. Acoustic localization was utilized to locate detected coyotes. After classification and localization, the results are visualized on a map by the Unity engine, which is intuitive and user-friendly. This solution utilizes a machine learning model to differentiate coyote sounds from other sounds, which has been illustrated in other publications. The goal of this research is to create a simple LoRaWAN network architecture with low-cost sensors that help people protect their land from coyotes. The solution is cost-effective, effective over a large area, and energy efficient. |
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| ISSN: | 2642-3901 |
| DOI: | 10.23919/ICCAS59377.2023.10316781 |