Data Reduction Techniques for Sensor Networks
Data Reduction Techniques for Sensor Networks
Loading...
Files
Publication or External Link
Date
2003-08-01
Authors
Deligiannakis, Antonios
Kotidis, Yannis
Roussopoulos, Nick
Advisor
Citation
DRUM DOI
Abstract
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)