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

作　　者：Samuel Ayankoso Zuolu Wang Dawei Shi Wenxian Yang Allan Vikiru Solomon Kamau Henry Muchiri Fengshou Gu 

机构地区：[1]Centre for Efficiency and Performance Engineering,University of Huddersfield,Huddersfield HD13DH,UK [2]School of Computing and Engineering Sciences,Strathmore University,Madaraka,Nairobi,5985700200,Kenya

出　　处：《Journal of Dynamics, Monitoring and Diagnostics》2024年第3期190-198,共9页动力学、监测与诊断学报（英文）

基　　金：funded by Climate Change AI(2023 innovation grant-https://www.climatechange.ai/innovation_grants).

摘　　要：Forests promote the conservation of biodiversity and also play a crucial role in safeguarding theenvironment against erosion,landslides,and climate change.However,illegal logging remains a significant threatworldwide,necessitating the development of automatic logging detection systems in forests.This paper proposesthe use of long-range,low-powered,and smart Internet of Things(IoT)nodes to enhance forest monitoringcapabilities.The research framework involves developing IoT devices for forest sound classification andtransmitting each node’s status to a gateway at the forest base station,which further sends the obtained datathrough cellular connectivity to a cloud server.The key issues addressed in this work include sensor and boardselection,Machine Learning(ML)model development for audio classification,TinyML implementation on amicrocontroller,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 forredundancy,and an ensemble network consisting of Long Short-Term Memory(LSTM)and ConvolutionalNeural Network(CNN)models for improved classification accuracy.The model outperforms LSTM and CNNmodels when used independently and also gave 88%accuracy after quantization.Notably,this solutiondemonstrates cost efficiency and high potential for scalability.

关 键 词：illegal logging forest monitoring internet of things NODES TinyML sound classification 

分 类 号：S76[农业科学—森林保护学] TP3[农业科学—林学]