Vehicle classification in Sensor Networks using time-domain signal processing and Neural Networks

Publication Type:Journal Article
Year of Publication:2007
Authors:G. Mazarakis, Avaritsiotis J.
Journal:Microprocessors and Microsystems
Volume:31
Pagination:381–392
Date Published:September
ISSN:01419331
Keywords:asr, classification, it, neuralnetworks
Abstract:

Vehicle classification is a demanding application of Wireless Sensor Networks. In many cases, sensor nodes detect and classify vehicles from their acoustic and/or seismic signature using spectral or wavelet based feature extraction methods. Such methods, while providing good results are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limited resources. In this work, we investigate the use of a time-domain encoding and feature extraction method, to produce simple, fixed-size matrices from complex acoustic and seismic signatures of vehicles for classification purposes. Classification is accomplished using an Artificial Neural Network and a basic, L1 distance, archetype classifier. Hardware implementation issues on a prototype sensor node, based on an 8-bit microcontroller, are also discussed. For evaluation purposes we use real data from DARPA's SensIt project, which contains various acoustic and seismic signatures from two different vehicle types, a tracked vehicle and a heavy truck.

URL:http://dx.doi.org/10.1016/j.micpro.2007.02.005
DOI:10.1016/j.micpro.2007.02.005
Scratchpads developed and conceived by (alphabetical): Ed Baker, Katherine Bouton Alice Heaton Dimitris Koureas, Laurence Livermore, Dave Roberts, Simon Rycroft, Ben Scott, Vince Smith