Rosie Wild Life Tracker

Rosie
Case for the device
This is an image of our esp32 with the sensors attached

Inspiration

Our project is named after Rosie, a former stray cat — a reminder that stray animals, while often overlooked, can significantly disrupt local ecosystems. With our device, conservationists can monitor stray cats in neighborhoods to study their behavior, assess their stress levels, and gather essential data to reduce their environmental impact — or even help find them new homes. Beyond strays, our device can also be used to track wild animals like deer, foxes, rabbits, or geese. It records movement, frequented locations, and biometric data such as heart rate and activity levels. This information helps document the effects of climate change and urban development on animal behavior, contributing to smarter conservation and land-use decisions

What it does

The goal of our project is to create a device that helps track the behavior and health of local wildlife, enabling researchers to better understand both human impact on nature and wildlife’s impact on the environment.

How we built it

We used a microcontroller (ESP32/Arduino) connected to several sensors, including a GY-521 (gyroscope and accelerometer), a temperature sensor, a pulse sensor (heartbeat monitor), and a NEO-6M GPS module. The microcontroller collects real-time data on the animal's location, movement, stress level, and general health. This data is transmitted to researchers, scientists, and wildlife conservationists, where it can be analyzed using AI tools and displayed on a centralized, web-based dashboard. This enables a deeper understanding of animal behavior and the environmental factors affecting their well-being.

Challenges we ran into

We encountered difficulties getting the ESP32 to reliably communicate with multiple sensors while also maintaining a stable Wi-Fi connection for sending data to the web platform. Additionally, we faced challenges setting up a WordPress-based website hosted through Oracle Cloud, especially in configuring live data display and integrating with the backend.

Accomplishments that we're proud of

We successfully built a working circuit with all sensors—pulse, motion, temperature, and GPS—integrated and communicating with the ESP32. We also assembled everything into a compact prototype casing, making it portable and field-ready. Despite technical roadblocks, we pushed through and created a solid foundation for real-world wildlife tracking and research applications.

What we learned

We learned the basics of front-end development and how to design user-friendly interfaces for displaying real-time sensor data. We also gained hands-on experience with the intricacies of building and managing large-scale IoT networks, including sensor integration, data transmission, and cloud-based data visualization.

What's next for Rosie Wild Life Tracker

Our next steps are to improve the communication between the microcontroller and the web platform to ensure stable, real-time data transmission. We also plan to integrate AI tools for automatic analysis of the collected data, providing insights into animal behavior and environmental impact. Finally, we aim to fully deploy this system by publishing the processed data to a live, user-friendly website accessible to researchers and conservationists.

Project Summary

Tracking wildlife effectively is essential for conservation efforts, ecological research, and behavioral studies. By using an ESP32 microcontroller in combination with a GY-521 (MPU6050) accelerometer and gyroscope, a pulse heartbeat sensor, and a GPS module, it's possible to build a compact and efficient system that monitors both movement and physiological responses in animals. The ESP32 acts as the central unit, collecting data from each sensor. The GY-521 module captures 3-axis motion and orientation, which helps identify behavior patterns like walking, running, or resting. Meanwhile, the pulse sensor monitors the animal’s heart rate, offering insights into stress levels, excitement, or periods of calm. The GPS module records the animal’s real-time location with timestamps, allowing researchers to map movement patterns over time. This combination of data provides a detailed view of the animal’s physical activity and emotional state in relation to its environment. We are able to collect data from the system wirelessly using Wi-Fi. Its lightweight design makes it suitable for small to medium-sized animals without impeding their natural behavior. Power efficiency is a key feature, with the ESP32 supporting deep-sleep modes to extend battery life in field deployments. This setup can be further enhanced with solar charging, long-range communication options like LoRa, and machine learning algorithms to classify behaviors in real time. Overall, the integration of movement, physiological, and location data provides a powerful tool for modern wildlife monitoring.