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Comparative study of compression algorithms on time series data for IoT devices

Bjärås, Fredrik LU (2019) EITM01 20191
Department of Electrical and Information Technology
Abstract
This Master’s thesis evaluates the performance of lightweight compression algorithm
aimed for IoT sensor devices. These devices are most often battery driven
and produce large amounts of sensor data which is sent wirelessly. Compressing the
data could be a tool in decreasing the power usage of these devices. The algorithms
were evaluated based on their compression ratio, memory consumption and CPU
usage. The results showed that the algorithms DRH, improved RLBE and A-LZSS
performed the best according to different criteria. DRH gave the most balanced
performance on all metrics, while improved RLBE is aimed towards datasets with
steeper changes, and A-LZSS towards datasets with reoccurring sequences.
Please use this url to cite or link to this publication:
author
Bjärås, Fredrik LU
supervisor
organization
course
EITM01 20191
year
type
H2 - Master's Degree (Two Years)
subject
report number
LU/LTH-EIT 2019-710
language
English
id
8989654
date added to LUP
2019-08-16 13:33:55
date last changed
2019-08-16 13:33:55
@misc{8989654,
  abstract     = {This Master’s thesis evaluates the performance of lightweight compression algorithm
aimed for IoT sensor devices. These devices are most often battery driven
and produce large amounts of sensor data which is sent wirelessly. Compressing the
data could be a tool in decreasing the power usage of these devices. The algorithms
were evaluated based on their compression ratio, memory consumption and CPU
usage. The results showed that the algorithms DRH, improved RLBE and A-LZSS
performed the best according to different criteria. DRH gave the most balanced
performance on all metrics, while improved RLBE is aimed towards datasets with
steeper changes, and A-LZSS towards datasets with reoccurring sequences.},
  author       = {Bjärås, Fredrik},
  language     = {eng},
  note         = {Student Paper},
  title        = {Comparative study of compression algorithms on time series data for IoT devices},
  year         = {2019},
}