Intelligent NB-IoT and Emotion-Aware Monitoring System for Proactive Wildlife Security

Conventional wildlife protection systems primarily rely on fixed geofencing and basic GPS tracking, which are limited in their ability to capture the behavioral and emotional states of animals under threat. This paper proposes a smart, AI-powered monitoring framework that integrates emotion recognition and adaptive geofencing to enhance wildlife security. The system identifies early warning indicators of fear, distress, or pain by analyzing multimodal data such as animal movement, acoustic signals, and biometric readings. To complement this, edge computing is leveraged with NB-IoT-enabled ground robots that patrol forest areas, process data locally, and provide real-time behavioural explanations. These robots not only detect anomalous events but also interpret activities, offering meaningful context to conservation teams. A key feature of the framework is the use of Explainable AI, which ensures transparency by justifying alerts raised and enabling forest rangers to make rapid, informed decisions. Unlike traditional static boundaries, the proposed adaptive geofencing dynamically adjusts virtual barriers based on animal movement patterns, environmental risks, and time-specific factors. Furthermore, the system incorporates an intelligent energy-saving mechanism, reducing unnecessary data transmission and extending the lifespan of field-deployed devices. By combining emotional intelligence, robotic surveillance, and explainable decision-making, the proposed model advances wildlife conservation from reactive poacher detection to proactive animal-centric protection. This holistic approach enables not only the safeguarding of endangered species but also a deeper understanding of their behavioural responses, ultimately fostering smarter and more sustainable conservation practices.