{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T21:11:26Z","timestamp":1783631486353,"version":"3.55.0"},"reference-count":57,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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%.<\/jats:p>","DOI":"10.3390\/s21227593","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T09:16:11Z","timestamp":1637140571000},"page":"7593","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Monitoring Illegal Tree Cutting through Ultra-Low-Power Smart IoT Devices"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3178-6168","authenticated-orcid":false,"given":"Alessandro","family":"Andreadis","sequence":"first","affiliation":[{"name":"Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5756-4603","authenticated-orcid":false,"given":"Giovanni","family":"Giambene","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6944-6858","authenticated-orcid":false,"given":"Riccardo","family":"Zambon","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Okia, C.A. (2012). Deforestation: Causes, Effects and Control. Strategies. Global Perspectives on Sustainable Forest Management, InTech.","DOI":"10.5772\/2634"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Moutinho, P. (2012). Sustainable forest management techniques. Deforestation around the World, IntechOpen.","DOI":"10.5772\/1979"},{"key":"ref_3","first-page":"302","article-title":"Wireless sensor network for illegal logging application: A systematic literature review","volume":"97","author":"Mutiara","year":"2019","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"231","DOI":"10.5626\/JCSE.2013.7.4.231","article-title":"A Survey on Communication Protocols for Wireless Sensor Networks","volume":"7","author":"Jang","year":"2013","journal-title":"J. Comput. Sci. Eng."},{"key":"ref_5","first-page":"1529","article-title":"WSN nodes power consumption using multihop routing protocol for illegal cutting forest","volume":"18","author":"Mutiara","year":"2020","journal-title":"Telkomnika Telecommun. Comput. Electron. Control"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zourmand, A., Kun Hing, A.L., Wai Hung, C., and AbdulRehman, M. (2019, January 29). Internet of things (IoT) using LoRa technology. Proceedings of the IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), Selangor, Malaysia.","DOI":"10.1109\/I2CACIS.2019.8825008"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mutiara, G.A., Herman, N.S., and Mohd, O. (2020). Using long-range wireless sensor network to track the illegal cutting log. Appl. Sci., 10.","DOI":"10.3390\/app10196992"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Merenda, M., Porcaro, C., and Iero, D. (2020). Edge machine learning for AI-enabled IoT devices: A review. Sensors, 20.","DOI":"10.3390\/s20092533"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"\u015ea\u015fmaz, E., and Tek, F.B. (2018, January 20\u201323). Animal sound classification using a convolutional neural network. Proceedings of the 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia and Herzegovina.","DOI":"10.1109\/UBMK.2018.8566449"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Das, J.K., Ghosh, A., Pal, A.K., Dutta, S., and Chakrabarty, A. (2020, January 21\u201323). Urban sound classification using convolutional neural network and long short term memory based on multiple features. Proceedings of the Fourth International Conference on Intelligent Computing in Data Sciences (ICDS), Fez, Morocco.","DOI":"10.1109\/ICDS50568.2020.9268723"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Piczak, K.J. (2015, January 17\u201320). Environmental sound classification with convolutional neural networks. Proceedings of the 25th International Workshop on Machine Learning for Signal Processing (MLSP), Boston, MA, USA.","DOI":"10.1109\/MLSP.2015.7324337"},{"key":"ref_12","unstructured":"(2021, July 13). Raspberry Pi Foundation. Available online: https:\/\/www.raspberrypi.org\/."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, J.T., Lin, C.B., Liaw, J.J., and Chen, Y.Y. (2018, January 26\u201328). Improving the implementation of sensor nodes for illegal logging detection. Proceedings of the International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Sendai, Japan.","DOI":"10.1007\/978-3-030-03748-2_26"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"164","DOI":"10.18517\/ijaseit.10.1.8849","article-title":"Multiple sensor on clustering wireless sensor network to tackle illegal cutting","volume":"10","author":"Mutiara","year":"2020","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, Y.-Y., and Liaw, J.-J. (2017, January 8\u201310). A novel real-time monitoring system for illegal logging events based on vibration and audio. Proceedings of the 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), Taichung, Taiwan.","DOI":"10.1109\/ICAwST.2017.8256503"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Prasetyo, D.C., Mutiara, G.A., and Handayani, R. (2018, January 5\u20137). Chainsaw sound and vibration detector system for illegal logging. Proceedings of the 2018 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), Bali, Indonesia.","DOI":"10.1109\/ICCEREC.2018.8712091"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"012065","DOI":"10.1088\/1755-1315\/467\/1\/012065","article-title":"Chainsaw location finding based on travelling of sound wave in air and ground","volume":"467","author":"Jubjainai","year":"2020","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mporas, I., Perikos, I., Kelefouras, V., and Paraskevas, M. (2020). Illegal logging detection based on acoustic surveillance of forest. Appl. Sci., 10.","DOI":"10.3390\/app10207379"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.eswa.2018.08.052","article-title":"Assessing the performances of different neural network architectures for the detection of screams and shouts in public transportation","volume":"117","author":"Laffitte","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Piczak, K.J. (2015, January 26\u201330). ESC: Dataset for environmental sound classification. Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia.","DOI":"10.1145\/2733373.2806390"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Salamon, J., Jacoby, C., and Bello, J.P. (2014, January 3\u20137). A dataset and taxonomy for urban sound research. Proceedings of the ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2655045"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/LSP.2017.2657381","article-title":"Deep convolutional neural networks and data augmentation for environmental sound classification","volume":"24","author":"Salamon","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zou, Y., and Shi, W. (2017, January 23\u201325). Dilated convolution neural network with LeakyReLU for environmental sound classification. Proceedings of the IEEE 22nd International Conference on Digital Signal Processing (DSP), London, UK.","DOI":"10.1109\/ICDSP.2017.8096153"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102013","DOI":"10.1016\/j.simpat.2019.102013","article-title":"Using cloud and fog computing for large scale IoT-based urban sound classification","volume":"101","author":"Baucas","year":"2020","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.eswa.2019.06.040","article-title":"End-to-end environmental sound classification using a 1D convolutional neural network","volume":"136","author":"Abdoli","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1109\/JSTSP.2020.2969775","article-title":"Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms","volume":"14","author":"Cerutti","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_27","unstructured":"Lai, L., Suda, N., and Chandra, V. (2018). CMSIS-NN: Efficient neural network kernels for arm cortex-M CPUs. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gaita, A., Nicolae, G., Radoi, A., and Burileanu, C. (2018, January 16\u201319). Chainsaw sound detection based on spectral Haar coeffluents. Proceedings of the IEEE 2018 International Symposium ELMAR, Zadar, Croatia.","DOI":"10.23919\/ELMAR.2018.8534594"},{"key":"ref_29","first-page":"1208","article-title":"Automatic detection of tree cutting in forests using acoustic properties","volume":"32","author":"Ahmad","year":"2019","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kalhara, P.G., Jayasinghearachchd, V.D., Dias, A.H.A.T., Ratnayake, V.C., Jayawardena, C., and Kuruwitaarachchi, N. (2017, January 6\u20138). TreeSpirit: Illegal logging detection and alerting system using audio identification over an IoT network. Proceedings of the IEEE 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Malabe, Sri Lanka.","DOI":"10.1109\/SKIMA.2017.8294127"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1109\/COMST.2017.2652320","article-title":"Low power wide area networks: An overview","volume":"19","author":"Raza","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Leonardi, L., Bello, L.L., Battaglia, F., and Patti, G. (2020). Comparative assessment of the LoRaWAN medium access control protocols for IoT: Does listen before talk perform better than ALOHA?. Electronics, 9.","DOI":"10.3390\/electronics9040553"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kodali, R.K., Borra, K.Y., Sai, G.N.S., and Domma, H.J. (2018, January 18\u201320). An IoT Based Smart Parking System Using LoRa. Proceedings of the 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC, Zhengzhou, China.","DOI":"10.1109\/CyberC.2018.00039"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Queralta, J.P., Gia, T.N., Tenhunen, H., and Westerlund, T. (2019, January 1\u20133). Edge-AI in LoRa-based health monitoring: Fall detection system with fog computing and LSTM recurrent neural networks. Proceedings of the IEEE 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), Budapest, Hungary.","DOI":"10.1109\/TSP.2019.8768883"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mdhaffar, A., Chaari, T., Larbi, K., Jmaiel, M., and Freisleben, B. (2017, January 6\u20138). IoT-based health monitoring via LoRaWAN. Proceedings of the IEEE EUROCON 2017\u201417th International Conference on Smart Technologies, Ohrid, Macedonia.","DOI":"10.1109\/EUROCON.2017.8011165"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, Y., Xu, H., and Teo, K.L. (2018, January 9\u201311). Forest fire monitoring, detection and decision making systems by wireless sensor network. Proceedings of the IEEE 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China.","DOI":"10.1109\/CCDC.2018.8408086"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Baharudin, A.M., and Yan, W. (2016, January 29\u201330). Long-range wireless sensor networks for geo-location tracking: Design and evaluation. Proceedings of the IEEE 2016 International Electronics Symposium (IES), Denpasar, Indonesia.","DOI":"10.1109\/ELECSYM.2016.7860979"},{"key":"ref_38","first-page":"3050","article-title":"Agriculture irrigation water demand forecasting using LORA technology","volume":"6","author":"Nisa","year":"2019","journal-title":"Int. Res. J. Eng. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Barro, P.A., Zennaro, M., Degila, J., and Pietrosemoli, E. (2019). A smart cities LoRaWAN network based on autonomous base stations (BS) for some countries with limited internet access. Future Internet, 11.","DOI":"10.3390\/fi11040093"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ratasuk, R., Vejlgaard, B., Mangalvedhe, N., and Ghosh, A. (2016, January 3\u20136). NB-IoT system for M2M communication. Proceedings of the IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Doha, Qatar.","DOI":"10.1109\/WCNCW.2016.7552737"},{"key":"ref_41","unstructured":"(2021, November 14). Microsoft, \u2018WAVE Specifications, Version 1.0, 1991\u201308\u2019. Available online: http:\/\/www-mmsp.ece.mcgill.ca\/Documents\/AudioFormats\/WAVE\/WAVE.html."},{"key":"ref_42","unstructured":"(2021, July 13). FLAC Project Homepage (Free Lossless Audio Codec). Available online: https:\/\/xiph.org\/flac\/."},{"key":"ref_43","unstructured":"Smith, J.O. (2011). Spectral Audio Signal Processing, W3K Publishing."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10772-010-9088-7","article-title":"Front end analysis of speech recognition: A review","volume":"14","author":"Anusuya","year":"2011","journal-title":"Int. J. Speech Technol."},{"key":"ref_45","unstructured":"Shahrin, M., and Huzaifah, M. (2017). Comparison of time-frequency representations for environmental sound classification using convolutional neural networks. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e488","DOI":"10.7717\/peerj.488","article-title":"Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning","volume":"2","author":"Stowell","year":"2014","journal-title":"PeerJ"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Portelo, J., Bugalho, M., Trancoso, I., Neto, J., Abad, A., and Serralheiro, A. (2009, January 19\u201324). Non-speech audio event detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taipei, Taiwan.","DOI":"10.1109\/ICASSP.2009.4959998"},{"key":"ref_48","unstructured":"Radhakrishnan, R., Divakaran, A., and Smaragdis, P. (2005, January 16\u201319). Audio analysis for surveillance applications. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA."},{"key":"ref_49","unstructured":"Gouda, S.K., Kanetkar, S., Harrison, D., and Warmuth, M.K. (2018). Speech recognition: Keyword spotting through image recognition. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Graham-Harper-Cater, J., Metcalfe, B., and Wilson, P. (2018). An analytical comparison of locally-connected reconfigurable neural network architectures using a C. elegans locomotive model. Computers, 7.","DOI":"10.3390\/computers7030043"},{"key":"ref_51","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Cerutti, G., Prasad, R., and Farella, E. (2019, January 12\u201317). Convolutional neural network on embedded platform for people presence detection in low resolution thermal images. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682998"},{"key":"ref_53","unstructured":"Lin, D., Talathi, S., and Annapureddy, S. (2016, January 19\u201324). Fixed point quantization of deep convolutional networks. Proceedings of the 33rd International Conference on International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_54","unstructured":"Zhang, Y., Suda, N., Lai, L., and Chandra, V. (2017). Hello edge: Keyword spotting on microcontrollers. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Marom, N.D., Rokach, L., and Shmilovici, A. (2010, January 17\u201320). Using the confusion matrix for improving ensemble classifiers. Proceedings of the IEEE 26th Convention of Electrical and Electronics Engineers in Israel, Eilat, Israel.","DOI":"10.1109\/EEEI.2010.5662159"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/JIOT.2018.2828867","article-title":"LoRaWAN: Evaluation of link-and system-level performance","volume":"5","author":"Feltrin","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_57","unstructured":"(2021, July 13). Attenuation in Vegetation, Recommendation ITU-R P.833-9. Available online: https:\/\/www.itu.int\/rec\/R-REC-P.833\/en."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7593\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:30:55Z","timestamp":1760167855000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7593"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,16]]},"references-count":57,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21227593"],"URL":"https:\/\/doi.org\/10.3390\/s21227593","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,16]]}}}