Dependable Wildlife DTN: Wearable Animal Resource Optimization for Sustainable Long-Term Monitoring

Nakagawa, Keijiro; Shimotoku, Daisuké; Kawase, Junya; Kobayashi, Hill Hiroki

doi:10.1109/dependsys51298.2020.00016

Cited by 3 publications

(2 citation statements)

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“…Other types of systems can use cables buried underground; seismic, acoustic, magnetic, ultrasonic, and passive infrared sensors; and the methodology of systems worn on the body of an animal [10][11][12][13][14]. In cable-based systems, double-cable sensors of a certain length (several hundred meters) are dug underground and are used to detect large and medium-sized animals crossing the cable along its entire length [10].…”

Section: Introductionmentioning

confidence: 99%

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IoT Sensor Network for Wild-Animal Detection near Roads

Knyva,

Gailius,

Balčiūnas

et al. 2023

Sensors

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The monitoring and detection of wild animals is a significant topic for researchers who study the behavior, lifestyle, and environment of wild animals, as well as for people who encounter wildlife both in residential areas and near roads while traveling. An innovative wild-animal detection internet-of-things (IoT) sensor network running on harvested solar energy and detection methodology is described in this article. The sensor-networks node is implemented via the principle of an embedded system incorporating passive infrared sensors, a long-range (LoRa) module, and a solar panel for energy harvesting. For experimental purposes, a small IoT sensor network was implemented near the road. The network consists of eight nodes placed near the road with a distance of 50 m between nodes, a gateway for gathering detection data from the nodes, and a thermo-vision camera for verification of the received data.

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Section: Introductionmentioning

confidence: 99%

“…The received data are processed and used to determine the location of the animal by sending signals wirelessly [15]. In addition, to increase the accuracy of such systems, the number of animals carrying the devices in a conventional area of their residence is periodically updated [14].…”

Section: Introductionmentioning

confidence: 99%