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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Computer Communicati...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Computer Communications
Article . 2020 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
DBLP
Article
Data sources: DBLP
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Analysis and implementation of novel Rice Golomb coding algorithm for wireless sensor networks

Authors: S. Kalaivani; C. Tharini;

Analysis and implementation of novel Rice Golomb coding algorithm for wireless sensor networks

Abstract

Abstract Wireless sensor networks (WSN) comprises of several sensor nodes scattered wirelessly to accomplish a particular task. Each sensor node is empowered by a battery. The various functions of the node namely sensing, computing, storage and transmission/reception of data consumes power from the battery with limited capacity. As these batteries do not last for a long time, an efficient algorithm is required to extend its life time. Data compression algorithm is a unique method adopted to minimize the amount of data being sent or received and thereby reduces the power consumed during communication. This would further increase the lifetime of node and also the network. In this paper a simple lossless compression algorithm is proposed and is also compared with the existing Adaptive Huffman coding algorithm that is been widely used in wireless sensor network applications. The comparative analysis is based on different compression parameters like compression ratio, compression factor, saving percentage, RMSE and encoding & decoding time. The data set for comparison is acquired using a temperature sensor interfaced with NI 3202 programmable sensing node. The comparative analysis is performed and the results are simulated using MATLAB software. The NI WSN nodes are used to execute the algorithm for instantaneous data. The analysis of number of packets transmitted during wireless communication, both before and after compression is performed using Wireshark network analyzer tool. The simulation result shows that the proposed lossless compression algorithm performs better than the existing one. The hardware implementation has proven that the amount of data traffic is reduced after compression which will help in reducing the transmission power and thereby saves the lifetime of the node in a wireless sensor network.

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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Top 10%
Top 10%
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