Paper Info
Title
Monitoring Illegal Tree Cutting through Ultra-Low-Power Smart IoT Devices
Abstract
Forests play a fundamental role in preserving the environment and fighting global warming. Unfortunately, they are continuously reduced by human interventions such as deforestation, fires, etc. This paper proposes and evaluates a framework for automatically detecting illegal tree-cutting activity in forests through audio event classification. We envisage ultra-low-power tiny devices, embedding edge-computing microcontrollers and long-range wireless communication to cover vast areas in the forest. To reduce the energy footprint and resource consumption for effective and pervasive detection of illegal tree cutting, an efficient and accurate audio classification solution based on convolutional neural networks is proposed, designed specifically for resource-constrained wireless edge devices. With respect to previous works, the proposed system allows for recognizing a wider range of threats related to deforestation through a distributed and pervasive edge-computing technique. Different pre-processing techniques have been evaluated, focusing on a trade-off between classification accuracy with respect to computational resources, memory, and energy footprint. Furthermore, experimental long-range communication tests have been conducted in real environments. Data obtained from the experimental results show that the proposed solution can detect and notify tree-cutting events for efficient and cost-effective forest monitoring through smart IoT, with an accuracy of 85%.
Year
DOI
Venue
2021
10.3390/s21227593
SENSORS
Keywords
DocType
Volume
convolutional neural networks, internet of things, edge computing, sound classification, low power, LoRa, deforestation, illegal tree cutting, experimental tests
Journal
21
Issue
ISSN
Citations 
22
1424-8220
1
PageRank 
References 
Authors
0.40
0
3
Name
Order
Citations
PageRank
Alessandro Andreadis13710.36
Giovanni Giambene210.40
Riccardo Zambon310.40