Compression methods for microclimate data based on linear approximation of sensor data
Väänänen, Olli; Hämäläinen, Timo (2019)
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Väänänen O., Hämäläinen T., (2019). Compression methods for microclimate data based on linear approximation of sensor data. Galinina O., Andreev S., Balandin S., Koucheryavy Y. (Eds.)., Internet of Things, Smart Spaces, and Next Generation Networks and Systems. 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, St. Petersburg, Russia, August 26–28, 2019, Proceedings., Springer.
Edge computing is currently one of the main research topics in the field of Internet of Things. Edge computing requires lightweight and computationally simple algorithms for sensor data analytics. Sensing edge devices are often battery powered and have a wireless connection. In designing edge devices the energy efficiency needs to be taken into account. Pre-processing the data locally in the edge device reduces the amount of data and thus decreases the energy consumption of wireless data transmission. Sensor data compression algorithms presented in this paper are mainly based on data linearity. Microclimate data is near linear in short time window and thus simple linear approximation based compression algorithms can achieve rather good compression ratios with low computational complexity. Using these kind of simple compression algorithms can significantly improve the battery and thus the edge device lifetime. In this paper linear approximation based compression algorithms are tested to compress microclimate data.