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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 7/2019

24.01.2019

A novel data aggregation scheme based on self-organized map for WSN

verfasst von: Ihsan Ullah, Hee Yong Youn

Erschienen in: The Journal of Supercomputing | Ausgabe 7/2019

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Abstract

Wireless sensor network allows efficient data collection and transmission in IoT environment. Since it usually consists of a large number of sensor nodes, a significant amount of redundant data and outliers are generated which deteriorate the network performance. In this paper, a novel data aggregation scheme is proposed which is based on self-organized map neural network to reduce redundant data and eliminate outliers. In addition, cosine similarity is used to improve the clustering process of sensor nodes based on the density and similarity of the data, and interquartile analysis is adopted to remove outliers. It allows to significantly reduce the energy consumption and enhance the network performance. Extensive simulation with real dataset shows that the proposed scheme consistently outperforms the existing representative data aggregation schemes in term of data reduction rate, network lifetime, and energy efficiency.
Vorheriger Artikel Sleepy-LRU: extending the lifetime of non-volatile caches by reducing activity of age bits

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Metadaten
Titel
A novel data aggregation scheme based on self-organized map for WSN
verfasst von
Ihsan Ullah
Hee Yong Youn
Publikationsdatum
24.01.2019
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 7/2019
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-018-2642-9

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