Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
C4.5: programs for machine learning
C4.5: programs for machine learning
Versatile low power media access for wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Understanding the causes of packet delivery success and failure in dense wireless sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Improving wireless simulation through noise modeling
Proceedings of the 6th international conference on Information processing in sensor networks
Minimising the effect of WiFi interference in 802.15.4 wireless sensor networks
International Journal of Sensor Networks
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
An empirical study of low-power wireless
ACM Transactions on Sensor Networks (TOSN)
Physically-based models of low-power wireless links using signal power simulation
Computer Networks: The International Journal of Computer and Telecommunications Networking
A survey of spectrum sensing algorithms for cognitive radio applications
IEEE Communications Surveys & Tutorials
Impulsive interference avoidance in dense wireless sensor networks
ADHOC-NOW'12 Proceedings of the 11th international conference on Ad-hoc, Mobile, and Wireless Networks
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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.