An Improved Data Compression Framework for Wireless Sensor Networks Using Stacked Convolutional Autoencoder (S-CAE)

Data compression is crucial in the networks as there is limited energy which is accessible to sensor nodes in wireless sensor networks (WSNs). The sensor nodes lifetime is extended enormously by reducing the data reception and transmission. We introduce a stacked convolutional RBM auto-encoder (stac...

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Veröffentlicht in:	SN computer science Jg. 4; H. 4; S. 419
Hauptverfasser:	Kumble, Lithin, Patil, Kiran Kumari
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 01.07.2023 Springer Nature B.V
Schlagworte:	Accuracy Advances in Computational Approaches for Image Processing Cloud Applications and Network Security Compression ratio Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data collection Data compression Data Structures and Information Theory Energy consumption Energy storage Information Systems and Communication Service Nodes Optimization techniques Original Research Pattern Recognition and Graphics Pressure ratio Reduction Sensors Software Engineering/Programming and Operating Systems Vision Wireless Networks Wireless sensor networks Energy, consumption optimization Stacked-CAE Transfer learning Data compression
ISSN:	2661-8907, 2662-995X, 2661-8907
Online-Zugang:	Volltext
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