Distributed classification of acoustic targets in wireless audio-sensor networks

Authors:
Baljeet Malhotra;Ioanis Nikolaidis;Janelle Harms
Affiliations:
Computing Science Department, University of Alberta, 221 Athabasca Hall, Edmonton, Alberta, Canada T6G 2E8;Computing Science Department, University of Alberta, 221 Athabasca Hall, Edmonton, Alberta, Canada T6G 2E8;Computing Science Department, University of Alberta, 221 Athabasca Hall, Edmonton, Alberta, Canada T6G 2E8
Venue:
Computer Networks: The International Journal of Computer and Telecommunications Networking
Year:
2008

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Abstract

Target tracking is an important application for wireless sensor networks. One important aspect of tracking is target classification. Classification helps in selecting particular target(s) of interest. In this paper, we address the problem of classification of moving ground vehicles. The basis of classification are the audible signals produced by these vehicles. We present a distributed framework to classify vehicles based on features extracted from acoustic signals of vehicles. The main features used in our study are based on FFT (fast Fourier transform) and PSD (power spectral density). We propose three distributed algorithms for classification that are based on the k-nearest neighbor (k-NN) classification method. An experimental study has been conducted using real acoustic signals of different vehicles recorded in the city of Edmonton. We compare our proposed algorithms with a naive distributed implementation of the k-NN algorithm. Performance results reveal that our proposed algorithms are energy efficient, and thus suitable for sensor network deployment.