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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.<\/jats:p>","DOI":"10.1145\/2379799.2379801","type":"journal-article","created":{"date-parts":[[2012,11,29]],"date-time":"2012-11-29T15:02:27Z","timestamp":1354201347000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Sampling and classifying interference patterns in a wireless sensor network"],"prefix":"10.1145","volume":"9","author":[{"given":"Nicholas M.","family":"Boers","sequence":"first","affiliation":[{"name":"Grant MacEwan University and University of Alberta, Alberta, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioanis","family":"Nikolaidis","sequence":"additional","affiliation":[{"name":"University of Alberta, Alberta, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pawel","family":"Gburzynski","sequence":"additional","affiliation":[{"name":"Olsonet Communications Corporation, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,11,30]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Akhmetshina E. 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M., Chodos, D., Huang, J., Stroulia, E., Gburzynski, P., and Nikolaidis, I. 2009. The Smart Condo: Visualizing independent living environments in a virtual world. In Proceedings from the 3rd International Conference on Pervasive Computing Technologies for Healthcare."},{"volume-title":"Proceedings of the 14th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD'10)","author":"Boers N. M.","key":"e_1_2_1_4_1","unstructured":"Boers , N. M. , Nikolaidis , I. , and Gburzynski , P . 2010. Patterns in the RSSI traces from an indoor urban environment . In Proceedings of the 14th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD'10) . Boers, N. M., Nikolaidis, I., and Gburzynski, P. 2010. Patterns in the RSSI traces from an indoor urban environment. 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L and S bands spectrum survey in the San Francisco Bay area . In Proceedings of the Position Location and Navigation Symposium (PLANS'04) . 566--572. Do, J., Akos, D., and Enge, P. 2004. L and S bands spectrum survey in the San Francisco Bay area. In Proceedings of the Position Location and Navigation Symposium (PLANS'04). 566--572."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.3138\/FM57-6770-U75U-7727"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1656274.1656278"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176346577"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.2307\/2347485"},{"key":"e_1_2_1_13_1","first-page":"249","article-title":"Supervised machine learning: A review of classification techniques. Informatica","volume":"31","author":"Kotsiantis S. B.","year":"2007","unstructured":"Kotsiantis , S. B. 2007 . Supervised machine learning: A review of classification techniques. Informatica : Int. 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