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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic


eISSN: 1178-5608



VOLUME 8 , ISSUE 4 (December 2015) > List of articles


Ahadul Imam * / Justin Chi * / Mohammad Mozumdar *

Keywords : wireless sensor networks, data compression, data visualization, compression algorithm, runtime analysis, relative compression rate, block density.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 4, Pages 2,083-2,115, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 12-October-2015 / Accepted: 10-November-2015 / Published Online: 01-December-2015



A basic tenet of wireless sensor networks is that processing of data is less expensive in terms of power than transmitting data. A data compression method is proposed to limit the amount of data transmitted within the network. In this paper, we propose a novel data compression algorithm suitable for low power computing devices. In our method, a data point density algorithm is used to determine which points to discard in a given data region. This algorithm is applied to uniform sections throughout the entirety of the data set. Regions with the highest data point density will be represented by a single point. The resulting data points then form the compressed data set. The transmission and subsequent processing of this compressed data set will cause less strain on the network than the original data set, while still maintaining the required information of the original data set. A tool is developed to test the method and compare it with other methods.

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