Development of Long-Range, Low-Powered and Smart IoT Device for Detecting Illegal Logging in Forests

Abstract

Forests promote the conservation of biodiversity and also play a crucial role in safeguarding the environment against erosion, landslides, and climate change. However, illegal logging remains a significant threat worldwide, necessitating the development of automatic logging detection systems in forests. This paper proposes the use of long-range, low-powered, and smart Internet of Things (IoT) nodes to enhance forest monitoring capabilities. The research framework involves developing IoT devices for forest sound classification and transmitting each node’s status to a gateway at the forest base station, which further sends the obtained data through cellular connectivity to a cloud server. The key issues addressed in this work include sensor and board selection, Machine Learning (ML) model development for audio classification, TinyML implementation on a microcontroller, choice of communication protocol, gateway selection, and power consumption optimization. Unlike the existing solutions, the developed node prototype uses an array of two microphone sensors for redundancy, and an ensemble network consisting of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models for improved classification accuracy. The model outperforms LSTM and CNN models when used independently and also gave 88% accuracy after quantization. Notably, this solution demonstrates cost efficiency and high potential for scalability.