Beast Sentinel: Smart IoT-Based Wildlife Tracking and Intrusion System

Abstract

Human-wildlife conflict has emerged as a serious concern in regions adjacent to wildlife sanctuaries, particularly where predators such as tigers and leopards stray into human settlements. These encounters often result in loss of life, property damage, and retaliatory harm to wildlife. Traditional methods of monitoring, such as manual patrolling and camera traps, are often inefficient and reactive, lacking the capability for real-time alerts. To address this critical issue, our project proposes a smart IoT-based wildlife tracking and intrusion alert system. The system utilizes GPS and motion sensor-enabled collars fitted on wild animals to track their movements continuously. When a predator enters a predefined danger zone, such as within 2 kilometres of a village, the system automatically sends SMS alerts to villagers and forest authorities. The solution ensures early detection, real-time tracking, and timely intervention, significantly reducing the risk of human-wildlife conflict. The design also includes features like solar-powered operation for remote areas and cloud-based data logging for further analysis. This project aims to promote safer human-wildlife coexistence through the power of modern technology.

Introduction

The proposed system is an IoT-powered solution designed to mitigate human-wildlife conflict near forested villages. It uses smart GPS-enabled tracking collars fitted on wild predators (e.g., tigers, leopards) to monitor their real-time location and behavior. When an animal enters a predefined danger zone (e.g., within 2 km of a village), the system triggers automated alerts (via SMS or phone calls) to villagers and forest officials, allowing prompt, preventive action.

A centralized dashboard provides real-time tracking and historical movement data, supporting both immediate responses and long-term wildlife management.

Literature Survey Highlights

Prior research has explored various IoT applications in wildlife conservation:

• Choudhary (2020): Focused on sensor-based collars and challenges like weather and connectivity.

• Sree et al. (2023): Introduced virtual fencing and deforestation monitoring with satellite imagery.

• Ronoh et al. (2022): Used AI (YOLO) and low-power sensors for early warning in Tanzanian parks.

• Liu et al.: Described wildlife IoT architecture, communication tech (GSM, LTE), and power-efficient trackers.

Methodology

The system involves:

• Smart collar design: Includes GPS (NEO-7M), GSM (SIM800L), motion sensors (PIR/accelerometer), powered by a solar-charged Li-ion battery and managed by an ESP32 microcontroller.

• Virtual geofencing: Defines danger zones using GPS coordinates. If an animal crosses the boundary, alerts are triggered.

• Alert mechanisms: Real-time notifications via SMS/calls and a mobile dashboard/app for officials.

• Cloud integration: Logs data to Firebase or Google Cloud for analysis and remote access.

Modeling and Analysis

Hardware Components:

• ESP32: Main processor handling GPS data, alerts, and communication.

• GPS Module (NEO-7M): Tracks animal’s location and supports geofencing.

• GSM Module (SIM800L): Sends alerts via SMS/phone calls.

Software Components:

• Written in C/C++ using Arduino IDE.

• Implements geofence monitoring with point-in-polygon algorithm.

• Includes error handling for GPS/GSM failures and power management.

Mobile App:

• Built with Flutter for Android/iOS.

• Uses Google Maps API for visualization.

• Communicates with the collar system via HTTP over GSM.

Conclusion

The integration of GPS and GSM technologies through a collar-based tracking system offers a practical and scalable solution for mitigating human-wildlife conflict. By enabling real-time location monitoring of predators such as tigers or leopards, the system ensures timely alerts to forest officials when animals approach or enter human settlements. The geofencing mechanism, coupled with SMS notifications, empowers authorities to take swift preventive actions, thereby enhancing both wildlife conservation efforts and public safety. The accompanying mobile application provides an intuitive and secure interface for officers to track animal movement, manage geofence zones, and respond to incidents efficiently. With the use of accessible components such as the NEO-7M GPS module, SIM800L GSM module, and a flexible power system, the design remains cost-effective and field-deployable. Overall, the system demonstrates a viable and impactful approach to wildlife monitoring and conflict prevention, and can be further enhanced in the future through solar optimization, long-range communication (e.g., LoRa or 4G/5G), and AI-driven behavior prediction.

References

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Copyright

Copyright © 2025 Rajas Tarapure, Sarthak Thote, Sujal Dange, Aayush Jibhkate, Sandhya Dhage. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.