Development of Long-Range,Low-Powered and Smart IoT Device for Detecting Illegal Logging in Forests
作者机构：Centre for Efficiency and Performance EngineeringUniversity of HuddersfieldHuddersfield HD13DHUK School of Computing and Engineering SciencesStrathmore UniversityMadarakaNairobi***Kenya
出 版 物：《Journal of Dynamics, Monitoring and Diagnostics》 (动力学、监测与诊断学报（英文）)
年 卷 期：2024年第3卷第3期
页 面：190-198页
学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学]
基 金：funded by Climate Change AI(2023 innovation grant-https://www.climatechange.ai/innovation_grants)
主 题：illegal logging forest monitoring internet of things nodes TinyML sound classification
摘 要：Forests promote the conservation of biodiversity and also play a crucial role in safeguarding theenvironment against erosion,landslides,and climate ***,illegal logging remains a significant threatworldwide,necessitating the development of automatic logging detection systems in *** paper proposesthe use of long-range,low-powered,and smart Internet of Things(IoT)nodes to enhance forest *** 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 *** 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 *** 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 *** model outperforms LSTM and CNNmodels when used independently and also gave 88%accuracy after ***,this solutiondemonstrates cost efficiency and high potential for scalability.
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