A new lossless neighborhood indexing sequence (NIS) algorithm for data compression in wireless sensor networks

In the recent years, wireless sensor networks (WSN) has been deployed in different real time applications. Energy efficiency is the critical issue in the design and deployment of WSN since the sensor nodes are powered by batteries with limited capacity. As data transmission is the main power consumi...

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Vydáno v:Ad hoc networks Ročník 83; s. 149 - 157
Hlavní autoři: J, Uthayakumar, T, Vengattaraman, P, Dhavachelvan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.02.2019
Témata:
Character encoding
Data compression
Energy efficiency
Robustness
Wireless sensor networks
Character encoding
Wireless sensor networks
Energy efficiency
Robustness
Data compression
ISSN:1570-8705, 1570-8713
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Shrnutí:In the recent years, wireless sensor networks (WSN) has been deployed in different real time applications. Energy efficiency is the critical issue in the design and deployment of WSN since the sensor nodes are powered by batteries with limited capacity. As data transmission is the main power consuming process in WSN, several energy efficient techniques have been proposed. Data compression is a popular energy efficient technique which helps to reduce the amount of data to be transmitted in the network resulting in significant power saving. This paper proposes a new algorithm called neighborhood indexing sequence (NIS) for data compression in WSN. The proposed NIS algorithm dynamically assigns shorter length codewords to each character in the input sequence by exploiting the occurrence of neighboring bits. Using the real world WSN dataset, it is shown that the compression performance of the NIS algorithm is superior to existing compression algorithms. Compared with existing methods, the proposed compression algorithm is not only efficient but also highly robust for different WSN dataset. The proposed algorithm attains a compression ratio of 89.13 with the bit rate of 1.74 per sample. Moreover, it achieved power savings up to 87.57% for the applied WSN dataset.
ISSN:1570-8705
1570-8713
DOI:10.1016/j.adhoc.2018.09.009