LoRaFence

LoRaFence: Revolutionizing animal tracking and virtual fencing with smart geofencing and stimuli feedback empowering conservation with precision and care.

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Inspiration

Residing near Chitwan National Park, we've observed frequent human-wildlife interactions that pose risks to both communities and animals. These encounters highlighted the need for a solution to manage and monitor wildlife movements effectively, inspiring the development of LoRaFence with cost effective solution.

What it does

LoRaFence is a system designed for:

  • Animal Tracking: Utilizing GPS technology to monitor wildlife movements in real-time with multiple trackers display.
  • Virtual Fencing: Establishing geofenced areas with two threshold zones:
    • First Zone: Triggers audible alerts to deter animals.
    • Second Zone: Activates haptic feedback to prevent boundary breaches.

How we built it

  1. Hardware Integration:

    • Employed LoRa modules for long-range communication.
    • Developed animal collars equipped with GPS, haptic sensors, and sound emitters.
    • Integrated Raspberry Pi 5 with display modules for monitoring in ranger vehicles.

  2. Software Development:

    • Implemented geofencing algorithms to define and monitor virtual boundaries.
    • Developed a user interface for real-time tracking and management.

  3. Testing and Iteration:

    • Conducted field tests to assess system performance near hackathon area's.
    • Refined the system based on feedback and observed challenges.

Challenges we ran into

  • Hardware Constraints: Ensuring durability and reliability of devices in diverse environmental conditions specially dealing with GPS erros.
  • Signal Interference: Maintaining consistent communication in areas with potential signal disruptions.
  • User Interface Design: Creating an intuitive interface for users with varying levels of technical expertise.

Accomplishments that we're proud of

  • Developed a functional prototype that integrates animal tracking with virtual fencing capabilities.
  • Achieved real-time monitoring and feedback mechanisms to manage wildlife movements effectively.
  • Established a foundation for scalable solutions applicable to various conservation efforts.

What we learned

  • The importance of user-centric design in technology solutions for wildlife management.
  • Challenges associated with hardware deployment in natural environments.
  • The need for iterative development and testing to refine system functionality.
  • Animal Human interaction and their impact in rural society as well as near conservation area's

What's next for LoRaFence

  • Extended Field Testing: Plan to conduct comprehensive trials in diverse environments to evaluate system robustness.
  • Feature Enhancement: Explore additional functionalities, such as integrating predictive analytics for animal movement patterns using TinyML.
  • Community Collaboration: Engage with local communities and conservationists to tailor the system to specific needs and ensure effective implementation.

For more details and updates, visit our GitHub Repository.

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