Tree-Structured Linear Approximation for Data Compression over WSNs

Wang, Chu-Ming; Yen, Clive H.; Yang, Wen-Yen; Wang, Jia-Shung

doi:10.1109/dcoss.2016.37

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Some methods are very simple based on constant approximation and some methods are slightly more complex [4]. Very simple methods suitable for constrained sensor nodes in PSNs are for example Piecewise Constant Approximation (PCA), Adaptive Piecewise Constant Approximation (APCA), Poor man's compression and Piecewise Linear Histogram (PWLH) [6][7][8] [9][10] [11]. These methods are so called model-based methods [1].…”

Section: Linearity Based Compression Algorithms and Their Suitabimentioning

confidence: 99%

Sensor Data Stream on-line Compression with Linearity-based Methods

Väänänen

Hämäläinen

2020

2020 IEEE International Conference on Smart Computing (SMARTCOMP)

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The escalation of the Internet of Things applications has put on display the different sensor data processing methods. The sensor data compression is one of the fundamental methods to reduce the amount of data needed to transmit from the sensor node which is often battery powered and operates wirelessly. Reducing the amount of data in wireless transmission is an effective way to reduce overall energy consumption in wireless sensor nodes. The methods presented and tested are suitable for constrained sensor nodes with limited computational power and limited energy resources. The methods presented are compared with each other using compression ratio and inherent latency. Latency is an important parameter in on-line applications. The improved variation of the linear regression-based method called RT-LRbTC is tested and it has proved to be a potential method to be used in a wireless sensor node with a fixed and predictable latency. The compression efficiency of the compression algorithms is tested with real measurement data sets.

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