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Edge Sensing-Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

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journal contribution
posted on 06.07.2021, 22:08 by Rongbo Zhu, Mai Yu, Yuanli Li, Jun Wang, Lu Liu
Due to the defects caused by limited energy, storage capacity, and computing ability, the increasing amount of sensing data has become a challenge in wireless sensor networks (WSNs). To decrease the additional power consumption and extend the lifetime of a WSN, a multistage hierarchical clustering deredundancy algorithm is proposed. In the first stage, a dual-metric distance is employed, and redundant nodes are preliminarily identified by the improved k-means algorithm to obtain clusters of similar nodes. Then, a Gaussian hybrid clustering classification algorithm is presented to implement data similarity clustering for edge sensing data in the second stage. In the third stage, the clustered sensing data is randomly weighted to deduplicate the spatial correlation data. Detailed experimental results show that, compared with the existing schemes, the proposed deredundancy algorithm can achieve better performance in terms of redundant data ratio, energy consumption, and network lifetime.

Funding

This work is supported by the National Science Foundation of China (No. 61772562, 62062019), the Key Project of Hubei Provincial Science and Technology Innovation Foundation of China (No. 2018ABB1485), the Hubei Provincial Natural Science Foundation of China (No. 2019CFB815), the Fundamental Research Funds for the Central Universities (No. CZP19004), and the Youth Elite Project of State Ethnic Affairs Commission of China (No. 2016-3-08).

History

Citation

Wireless Communications and Mobile Computing (2021), vol. 2021, Article ID 6664324

Author affiliation

School of Informatics

Version

VoR (Version of Record)

Published in

Wireless Communications and Mobile Computing

Volume

2021

Issue

.

Publisher

Hindawi

issn

1530-8669

eissn

1530-8677

Acceptance date

22/02/2021

Copyright date

2021

Available date

06/07/2021

Language

English