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Simple objects that surround us are gaining sensors, computational power, and actuators, and are changing from static, into adaptive and reactive systems. In this talk we discuss issues for knowledge discovery from distributed data streams generated by sensors with limited computational resources.
We present two clustering algorithms for two different tasks: clustering streaming data, which searches for dense regions of the data space, and clustering streaming data sources, which finds groups of sources that behave similarly over time. In the ﬁrst setting, a cluster is deﬁned to be a set of data points. In the second setting, a cluster is deﬁned to be a set of sensors. We conclude the talk by presenting the lessons learned.