Linear approximation based compression algorithms efficiency to compress environmental data sets
Väänänen, Olli; Zolotukhin, Mikhail; Hämäläinen, Timo (2020)
Väänänen, Olli
Zolotukhin, Mikhail
Hämäläinen, Timo
Editoija
Barolli, Leonard
Amato, Flora
Moscato, Francesco
Enokido, Tomoya
Takizawa, Makoto
Springer
2020
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2020082061190
https://urn.fi/URN:NBN:fi-fe2020082061190
Tiivistelmä
Measuring some environmental magnitudes is a very typical application in the field of Internet of Things. Wireless sensor nodes measuring these environmental magnitudes are often battery powered devices. Thus, the energy efficiency is an important topic in these measuring devices. The most efficient method to reduce energy consumption in wireless devices is to reduce the amount of data needed to transmit via wireless connection. A simple method to reduce the amount of the data is to compress sensor data. Environmental data behaves quasi linearly in short time window and many compression algorithms utilize this data behavior. In this paper the different environmental data sets characteristics and their effect on compression algorithms’ compression ratio are evaluated. The results can be used to evaluate and choose the suitable compression algorithm for the application and to predict the lifetime of the battery powered device.