IoT Sensor Network for Wild-Animal Detection near Roads.

Area of Science:

• Ecology
• Environmental Science
• Computer Science

Background:

• Wildlife monitoring is crucial for understanding animal behavior and mitigating human-wildlife conflict.
• Existing methods for wildlife detection can be limited in scope and energy efficiency.
• The increasing need for real-time wildlife data in diverse environments necessitates innovative solutions.

Purpose of the Study:

• To develop and evaluate an innovative Internet of Things (IoT) sensor network for wild animal detection.
• To implement a solar-powered system for sustainable and continuous wildlife monitoring.
• To assess the efficacy of the proposed sensor network in detecting wildlife near roadways.

Main Methods:

• An embedded system was designed, incorporating passive infrared sensors and a long-range (LoRa) module for data transmission.
• A solar panel was integrated into each sensor node for energy harvesting, ensuring autonomous operation.
• A small-scale IoT sensor network with eight nodes and a gateway was deployed near a road, with data verified by a thermal vision camera.

Main Results:

• The implemented IoT sensor network successfully detected wild animals in the monitored area.
• The solar energy harvesting system provided a sustainable power source for the sensor nodes.
• Data transmission via the LoRa module enabled efficient collection of detection events at the gateway.

Conclusions:

• The developed solar-powered IoT sensor network offers a viable and sustainable solution for wild animal detection.
• This technology can significantly enhance wildlife monitoring efforts for research and road safety.
• The embedded system design demonstrates the potential for scalable and cost-effective wildlife sensing solutions.