Data Reduction Techniques for Sensor Networks

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We are inevitably moving into a realm where small and inexpensive wireless devices would be seamlessly embedded in the physical world and form a wireless sensor network in order to perform complex monitoring and computational tasks. Such networks pose new challenges in data processing and dissemination due to the conflict between (i) the abundance of information that can be collected and processed in a distributed fashion among thousands of nodes and (ii) the limited resources (bandwidth, energy) that such devices possess. In this paper we propose a new data reduction technique that exploits the correlation and redundancy among multiple measurements on the same sensor and achieves high degree of data reduction while managing to capture even the smallest details of the recorded measurements. The key to our technique is the base signal, a series of values extracted from the real measurements, used for encoding piece-wise linear correlations among the collected data values. We provide efficient algorithms for extracting the base signal features from the data and for encoding the measurements using these features. Our experiments demonstrate that our method by far outperforms standard approximation techniques like Wavelets, Histograms and the Discrete Cosine Transform, on a variety of error metrics and for real datasets from
different domains. (UMIACS-TR-2003-80)