Performance Analysis of Data Compression Algorithms for Energy Efficient Wireless Sensor Networks
N. Vini Antony Grace1, A. Chilambuchelvan2, G. Karthika3

1N. Vini Antony Grace, Department of EIE, R.M.D Engineering College, Thiruvallur (Tamil Nadu), India.

2Dr. A. Chilambuchelvan, Department of EIE, R.M.D Engineering College, Thiruvallur (Tamil Nadu), India.

3G. Karthika, Department of EIE, R.M.D Engineering College, Thiruvallur (Tamil Nadu), India.

Manuscript received on 27 November 2019 | Revised Manuscript received on 07 December 2019 | Manuscript Published on 14 December 2019 | PP: 404-408 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10801191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1080.1191S19

Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (

Abstract: Wireless Sensor Networks (WSN), is an intensive area of research which is often used for monitoring, sensing and tracking various environmental conditions. It consists of a number of sensor nodes that are powered with fixed low powered batteries. These batteries cannot be changed often as most of the WSN will be in remote areas. Life time of WSN mainly depends on the energy consumed by the sensor nodes. In order to prolong the networks life time, the energy consumption has to be reduced. Different energy saving schemes has been proposed over the years. Data compression is one among the proposed schemes that can scale down the amount of data transferred between nodes and results in energy saving. In this paper, an attempt is made to analyze the performances of three different data compression algorithms viz. Light Weight Temporal Compression (LTC), Piecewise Linear Approximation with Minimum Number of Line Segments (PLAMLIS) and Univariate Least Absolute Selection and Shrinkage Operator (ULASSO). These algorithms are tested on standard univariate datasets and evaluated using assessment metrics like Mean Square Error (MSE), compression ratio and energy consumption. The results show that the ULASSO algorithm outperforms other algorithms in all three metrics and contributes more towards energy consumption.

Keywords: Compression Ratio, Energy Consumption, Mean Square Error, Wireless Sensor Networks.
Scope of the Article: Wireless ad hoc & Sensor Networks