Transforming Animal Tracking Frameworks Using Wireless Sensor Networks and Machine Learning Algorithm using Decision Tree Algorithm

Abstract

Wildlife monitoring and conservation have become increasingly important due to habitat loss, climate change, and poaching threats. Traditional methods of animal tracking often rely on manual observation or expensive GPS collars, which are not scalable or efficient for large ecosystems. To address these limitations, this project proposes an intelligent animal tracking framework that integrates Wireless Sensor Networks (WSNs) with a Machine Learning-based Decision Tree algorithm to enhance monitoring, behavior prediction, and anomaly detection. In the proposed system, low-power wireless sensor nodes collect real-time data such as location coordinates, temperature, movement speed, and humidity. This data is transmitted to a central base station via WSN protocols like ZigBee. A Decision Tree classifier is then trained on this sensor data to classify the animal’s current activity into categories such as feeding, moving, resting, or abnormal behavior. The use of a Decision Tree algorithm provides interpretable results and requires relatively low computational resources, making it ideal for embedded and edge computing environments. The system improves accuracy, reduces manual workload, and supports scalable deployment across diverse wildlife environments. This intelligent framework demonstrates a cost-effective, energy- efficient, and scalable solution for modern wildlife tracking, making it a valuable tool for researchers, conservationists, and forest management authorities.