2009 Third International Conference on Sensor Technologies and Applications 2009
DOI: 10.1109/sensorcomm.2009.84
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K-RLE: A New Data Compression Algorithm for Wireless Sensor Network

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Cited by 64 publications
(38 citation statements)
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“…Some lossy data compression methods have also been researched. 15,16 In contrast to lossless data compression, lossy data compression tolerates a certain level of inaccuracy induced by the data compression, but the significantly higher compression ratio can be provided. Capo-Chichi et al 15 have proposed a lossy data compression algorithm called K-RLE,where K is a parameter of precision and RLE means run-length encoding.…”
Section: Standalone Compression At a Single Nodementioning
confidence: 99%
See 1 more Smart Citation
“…Some lossy data compression methods have also been researched. 15,16 In contrast to lossless data compression, lossy data compression tolerates a certain level of inaccuracy induced by the data compression, but the significantly higher compression ratio can be provided. Capo-Chichi et al 15 have proposed a lossy data compression algorithm called K-RLE,where K is a parameter of precision and RLE means run-length encoding.…”
Section: Standalone Compression At a Single Nodementioning
confidence: 99%
“…15,16 In contrast to lossless data compression, lossy data compression tolerates a certain level of inaccuracy induced by the data compression, but the significantly higher compression ratio can be provided. Capo-Chichi et al 15 have proposed a lossy data compression algorithm called K-RLE,where K is a parameter of precision and RLE means run-length encoding. The main idea of K-RLE algorithm is that the consecutive sequence of the same bits b or bits in the range [K À b, K + b] are compressed as a combination of the bit b and its count n. Simulation results show that K-RLE provides higher compression ratio than RLE, but it consumes more energy to compress and decompress.…”
Section: Standalone Compression At a Single Nodementioning
confidence: 99%
“…The results showed that some algorithms are more appropriate when considering time-critical or energy-efficient applications, while others are more appropriate as they consume less SRAM memory. Capo-Chichi et al [24] evaluated and compared the execution of data compression algorithms when using an ultra low-power micro-controller, known as MSP430, from Texas Instruments. The study aimed to evaluate the trade-off between energy consumption and compression efficiency.…”
Section: Related Workmentioning
confidence: 99%
“…In order to reduce the power associated while transmission various compression schemes such as K-RLE [4], Adaptive Huffman coding [5] and many more [6], in this paper we implement the compression algorithm using a low-cost system-on-chip (SoC) architecture proposed in [7,8].…”
Section: Related Workmentioning
confidence: 99%