A4 Article in conference proceedings
Linear Approximation Based Compression Algorithms Efficiency to Compress Environmental Data Sets (2020)


Väänänen, Olli; Zolotukhin, Mikhail; Hämäläinen, Timo (2020). Linear Approximation Based Compression Algorithms Efficiency to Compress Environmental Data Sets. In Barolli, Leonard; Amato, Flora; Moscato, Francesco; Enokido, Tomoya; Takizawa, Makoto (Eds.) Web, Artificial Intelligence and Network Applications : Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications (WAINA-2020), Advances in Intelligent Systems and Computing, 1150. Cham: Springer, 110-121. DOI: 10.1007/978-3-030-44038-1_11


JYU authors or editors


Publication details

All authors or editors: Väänänen, Olli; Zolotukhin, Mikhail; Hämäläinen, Timo

Parent publication: Web, Artificial Intelligence and Network Applications : Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications (WAINA-2020)

Parent publication editors: Barolli, Leonard; Amato, Flora; Moscato, Francesco; Enokido, Tomoya; Takizawa, Makoto

Conference:

International Symposium on Frontiers of Information Systems and Network Applications

Place and date of conference: Caserta, Italy, 15.-17.4.2020

ISBN: 978-3-030-44037-4

eISBN: 978-3-030-44038-1

Journal or series: Advances in Intelligent Systems and Computing

ISSN: 2194-5357

eISSN: 2194-5365

Publication year: 2020

Number in series: 1150

Pages range: 110-121

Number of pages in the book: 1433

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: http://doi.org/10.1007/978-3-030-44038-1_11

Open Access: Publication channel is not openly available

Additional information: Part of the WAINA-2020 sub-conference: The 16th International Symposium on Frontiers of Information Systems and Network Applications (FINA-2020)


Abstract

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.


Keywords: sensor networks; data compression; algorithms


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

Preliminary JUFO rating: 1


Last updated on 2020-09-07 at 23:11