Real-Time Surveillance and Early Warning System Using IoT and Machine Learning for Critical Environmental Zones

Abstract

Natural ecosystems often face serious challenges due to the interaction between wild animals and human living spaces. Animals sometimes move outside their natural regions, which creates risks for both humans and wildlife. Farmers lose crops, travellers face danger, and in many cases, conflicts lead to harm on both sides. This project focuses on building an intelligent monitoring system that can observe animal movement in real time and respond immediately when required. The system uses advanced camera technology along with embedded processing to continuously monitor forest boundary areas. A trained machine learning model running on an embedded device identifies whether the detected object is a wild animal, a domestic animal, or any other movement. Based on this identification, the system takes automatic action without human intervention. If a wild animal is detected crossing into a human-inhabited area, the system immediately sends alerts to a control center and nearby people. At the same time, it produces a specific sound designed to scare the animal and guide it back to its natural zone. This reduces direct human-animal conflict and helps protect both sides. The system also supports continuous monitoring and can detect illegal activities, unauthorized entry, and environmental disturbances. Overall, this project provides a smart and automated solution for wildlife protection and human safety.