Edge Sensing-Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

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 d...

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Veröffentlicht in:Wireless communications and mobile computing Jg. 2021; H. 1
Hauptverfasser: Zhu, Rongbo, Yu, Mai, Li, Yuanli, Wang, Jun, Liu, Lu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Hindawi 2021
John Wiley & Sons, Inc
Schlagworte:
Accuracy
Algorithms
Big Data
Cluster analysis
Clustering
Data collection
Data compression
Data processing
Data transmission
Energy consumption
Energy storage
Lifetime
Nodes
Parameter estimation
Performance evaluation
Power consumption
Semantics
Sensors
Similarity measures
Spatial data
Storage capacity
Wavelet transforms
Wireless sensor networks
ISSN:1530-8669, 1530-8677
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1530-8669
1530-8677
DOI:10.1155/2021/6664324