Comparison of local lossless compression algorithms for Wireless Sensor Networks

New Lossless Compression Method using Cyclic Reversible Low Contrast Mapping (CRLCM)

“…Recently, some researches in lossless compression methods commonly aim to optimize existing compression method for specific data type [7][8][9][10][11][12][13][14][15][16][17][18] or to improve the existing compression method by transforming data to other form before compression process or by combining several compression method [19][20][21][22]. One of novel researches in compression method is Asymmetric Numerical System (ANS) [23][24].…”

RAKE: A simple and efficient lossless compression algorithm for the Internet of Things

“…Recently, a simple lossless compression technique that combines the above approaches has been proposed in [9]. The algorithm, named MinDiff*, exploits the fact that a simple prediction can be done locally, directly on sensor nodes, using the range of the actual set of data.…”

“…In the case of the Willow Creek Tower database, outdoor temperature and humidity data have been collected every 30 minutes, so large variations exist among consecutive samples. More detailed statistical information about the above data sets are reported in [9].…”

“…As done in [17] and [9], compression efficiencies have been evaluated considering, for each physical variable, a set of 10000 data divided into 100 blocks of 100 words each and represented with w = 16 bits. The achieved compression efficiencies are reported in Fig.…”

“…This is the case of wireless sensor networks (WSN), one of the key enablers of the IoT paradigm, where nodes are based on low-speed microcontrollers with just a few kilobytes of memory [9].…”