Illegal Logging Listeners Using IoT Networks

Abstract

Protecting and increasing worldwide green space have been an international effort. Individuals and organizations are encouraged to plant urban trees and to get involved in many reforestation and restoration projects. Offsetting these much needed plans to save the forests is illegal logging. Trees that have grown for many years, some are protected resources inside restricted areas, are felled and the wood is smuggled. Watching for these illegal activities is very difficult and also very dangerous. It is quite impossible for rangers to patrol every entry and exit point of forests that cover thousands of squared kilometers. Applying Internet of Things technology to ecological forestry, we are proposing integrating sound acquisition networks and acoustic signal analyzers to enhance the robustness of an already successful camera-based surveillance solution that is also equipped with a global positioning system tracker. Our listener devices record sounds of the forest and periodically send it to a cloud storage over cellular networks. The device is affordable, the system is small and portable, and the network is flexibly extensible. From the data, acoustic features are extracted and visualized. The Mel-frequency cepstral coefficients of the signals have exhibited promising distinctiveness for detection of illegal chainsaw activities in the wild.