Affiliation:
1. Grant MacEwan University and University of Alberta, Alberta, Canada
2. University of Alberta, Alberta, Canada
3. Olsonet Communications Corporation, Canada
Abstract
The low-powered transmissions in a wireless sensor network (WSN) are highly susceptible to interference from external sources. Our work is a step towards enabling WSN devices to better understand the interference in their environment so that they can adapt to it and communicate more efficiently. We extend our previous work in which we collected received signal strength traces using mote-class synchronized receivers at sample rates that are, to the best of our knowledge, higher than previously described in the literature. These traces contain distinct interference patterns, each with a different potential for being exploited by cognitive radio strategies. In order to exploit a pattern, however, a node must first recognize it. Given the energy and space constraints of a node, we explore succinct decision tree classifiers for the two most disruptive patterns. We expand on a basic feature set to incorporate attributes based on the dip statistic and the Lomb periodogram, both of which address specific, empirically observed behaviour, and we show their positive impact on both the decision tree structure and the overall classification performance. Moreover, we present an approximation of the periodogram that makes its construction feasible for mote-class devices, and we describe the simplification's impact on classification performance.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
Reference30 articles.
1. Akhmetshina E. Gburzynski P. and Vizeacoumar F. 2003. PicOS: A tiny operating system for extremely small embedded platforms. In Embedded Systems and Applications H. R. Arabnia and L. T. Yang Eds. CSREA Press 116--122. Akhmetshina E. Gburzynski P. and Vizeacoumar F. 2003. PicOS: A tiny operating system for extremely small embedded platforms. In Embedded Systems and Applications H. R. Arabnia and L. T. Yang Eds. CSREA Press 116--122.
2. Assessing the accuracy of prediction algorithms for classification: an overview
3. Measurements of radio impulsive noise from various sources in an indoor environment at 900 MHz and 1800 MHz
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