Lightweight Acoustic Detection of Logging in Wireless Sensor Networks

In our paper we investigate methods to detect the acoustic signals of logging with the help of lightweight sensors in a wireless sensor network. The main advantage of using such sensors is their small size, low cost, low power consumption, rare and cheap maintenance, and reasonable detection performance. These properties make such acoustic sensors competitive to video based systems in large area surveillance. Besides specifying the hardware platform and the used test databases we detail the signal processing methods for acoustic feature detection and classification and also discuss the performance of single node and multi sensors information fusion.

[1]  Mei Zhou,et al.  Using MODIS land surface temperature to evaluate forest fire risk of northeast China , 2004, IEEE Geosci. Remote. Sens. Lett..

[2]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[3]  Chensheng Wang,et al.  Forest smoke detection in video images based on constant speed rotating platform , 2013, Other Conferences.

[4]  Yoav Freund,et al.  The Alternating Decision Tree Learning Algorithm , 1999, ICML.

[5]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[6]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Geoffrey I. Webb.  Decision Tree Grafting From the All Tests But One Partition , 1999, IJCAI.

[9]  Charalambos Kontoes,et al.  Wildfire monitoring using satellite images, ontologies and linked geospatial data , 2014, J. Web Semant..

[10]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[11]  Geoff Holmes,et al.  Multiclass Alternating Decision Trees , 2002, ECML.

[12]  Bernhard Rinner,et al.  Single Sensor Acoustic Feature Extraction for Embedded Realtime Vehicle Classification , 2009, 2009 International Conference on Parallel and Distributed Computing, Applications and Technologies.

[13]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[14]  Khoirul Anwar,et al.  Low Complexity Fourier Transforms using Multiple Square Waves , 2011 .

[15]  Haijia Shi.  Best-first Decision Tree Learning , 2007 .

[16]  Miguel Garcia,et al.  A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification , 2009, Sensors.

[17]  Jozef Papán,et al.  WSN for forest monitoring to prevent illegal logging , 2012, 2012 Federated Conference on Computer Science and Information Systems (FedCSIS).

[18]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[19]  Hongcheng Wang,et al.  Video-based Smoke Detection : Possibilities , Techniques , and Challenges , 2007 .

[20]  Byung-rak Son,et al.  A Design and Implementation of Forest-Fires Surveillance System based on Wireless Sensor Networks for South Korea Mountains , 2006 .

[21]  Janto Skowronek,et al.  Automatic surveillance of the acoustic activity in our living environment , 2005, 2005 IEEE International Conference on Multimedia and Expo.