Data Compression And Visualization For Wireless Sensor Networks

Abstract

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

A Novel Hybrid Localization Method for Wireless Sensor Network

“…Step 5: According to Equation (8) and Equation ( 9), the speed and position of each particle are updated.…”

“…Using wireless sensor network instead of GPS makes indoor localization possible. Over the years, many researchers have proposed many different methods for this problem [7][8][9]. There are two kinds of localization algorithms: range-based algorithm and range-free algorithm.…”

Image Fusion Based on Joint Nonsubsampled Contourlet and Overcomplete Brushlet Transforms

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Multi-Scale Compressed Sensing Based On Split Augmented Lagrangian Shrinkage Algorithm For Image Super-Resolution Reconstruction

“…In MCSS_SR approach, we first improve the measurement matrix by using the down-sampling and blur matrice to ensure that the modified matrix satisfies restricted isometry property (RIP) [8][9][10] , and apply it to the high passband of Shearlet domain to obtain the observations. In the processing of reconstruction, the wavelets are deployed in each directional subband to further increase the sparsity.…”