Distributed data classification in sensor networks

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Distributed data classification in sensor networks
Low overhead analysis of large distributed data sets is necessary for current data centers and for future sensor networks. In such systems, each node holds some data value, e.g., a local sensor read, and a concise picture of the global system state needs to be obtained. To this end, we define the distributed classification problem, in which numerous interconnected nodes compute a classification of their data, i.e., partition these values into multiple collections, and describe each collection concisely. We present a generic algorithm that solves the distributed classification problem and may be implemented in various topologies, using different classification types. For example, the generic algorithm can be instantiated to classify values according to distance, like the famous k-means classification algorithm. However, the distance criterion is often not sufficient to provide good classification results. We present an instantiation of the generic algorithm that describes the values as...
Ittay Eyal, Idit Keidar, Raphael Rom
Added 14 Oct 2010
Updated 14 Oct 2010
Type Conference
Year 2010
Where PODC
Authors Ittay Eyal, Idit Keidar, Raphael Rom
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