Lossless Data Compression with Multi-resolution Temporal and Spatial Coding in Wireless Sensor Networks
Date Issued
2010
Date
2010
Author(s)
Lin, Chih-Chung
Abstract
In some WSN (Wireless Sensor Network) applications which require high-accuracy measurements, sensor nodes are used to do long-term lossless measurement and query. However, the limited power makes the power saving become a critical issue of studies.
In the existing methods, sensed data can be reduced by lossless data compression or querying rough overview of the sensed area. To the best of our knowledge, since none of previous method takes both temporal and spatial correlation of sensed data into consideration for lossless data compression, they usually cannot obtain a good compression ratio. Therefore, these requirements motivate us to propose the LMTSC (Lossless Data Compression with Multi-resolution Temporal and Spatial Coding) method.
LMTSC regards each sensed data as a pixel of an image, and the whole sensed data as sequential images. Using temporal correlation of pixels and spatial correlation of images can reduce the data transmitted and the power consumed efficiently. Besides, we propose a dynamic sampling method which uses various sample rates and lossless data compression to provide different resolution data query.
In this paper, we use the real-world sensed data to evaluate LMTSC and make a comparison with MEC. The simulation results reveal that LMTSC has a good compression ratio than MEC without any data training. As the high compression ratio, LMTSC saves 26% power consumption than MEC. For reaching the identical sample point (power consumption), the dynamic sampling method can tolerate a smaller error bound. For reaching the same error bound, the dynamic sampling method can save more than 20% power consumption than static sampling method. Since LMTSC can make a significant power saving, it is very suitable for WSN.
Subjects
Wireless sensor network
lossless data compression
temporal correlation
spatial correlation
multi-resolution data query
Type
thesis
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