A4 Article in conference proceedings
Linear Approximation Based Compression Algorithms Efficiency to Compress Environmental Data Sets (2020)
Väänänen, O., Zolotukhin, M., & Hämäläinen, T. (2020). Linear Approximation Based Compression Algorithms Efficiency to Compress Environmental Data Sets. In L. Barolli, F. Amato, F. Moscato, T. Enokido, & M. Takizawa (Eds.), Web, Artificial Intelligence and Network Applications : Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications (WAINA-2020) (pp. 110-121). Springer. Advances in Intelligent Systems and Computing, 1150. https://doi.org/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:
- Advanced information networking and applications workshops
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: https://doi.org/10.1007/978-3-030-44038-1_11
Publication open access: Not open
Publication channel open access:
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
JUFO rating: 0