Publication: Illegal logging listeners using IoT networks
5
Issued Date
2020-11-16
Resource Type
ISSN
21593450
21593442
21593442
Other identifier(s)
2-s2.0-85098959613
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Mahidol University
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SCOPUS
Bibliographic Citation
IEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol.2020-November, (2020), 1277-1282
Suggested Citation
Ananta Srisuphab, Nopparat Kaakkurivaara, Piyanuch Silapachote, Kitipong Tangkit, Ponthep Meunpong, Thanwadee Sunetnanta Illegal logging listeners using IoT networks. IEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol.2020-November, (2020), 1277-1282. doi:10.1109/TENCON50793.2020.9293935 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/60905
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Title
Illegal logging listeners using IoT networks
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Abstract
© 2020 IEEE. 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.