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Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications
Joint Authors
Lei, Fangyuan
Cai, Jun
Dai, Qingyun
Zhao, Huimin
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-02
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Wireless Sensor Networks (WSNs) have a wide range of applications scenarios in computer vision, from pedestrian detection to robotic visual navigation.
In response to the growing visual data services in WSNs, we propose a proactive caching strategy based on Stacked Sparse Autoencoder (SSAE) to predict content popularity (PCDS2AW).
Firstly, based on Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies, a distributed deep learning network SSAE is constructed in the sink nodes and control nodes of the WSN network.
Then, the SSAE network structure parameters and network model parameters are optimized through training.
The proactive cache strategy implementation procedure is divided into four steps.
(1) The SDN controller is responsible for dynamically collecting user request data package information in the WSNs network.
(2) The SSAEs predicts the packet popularity based on the SDN controller obtaining user request data.
(3) The SDN controller generates a corresponding proactive cache strategy according to the popularity prediction result.
(4) Implement the proactive caching strategy at the WSNs cache node.
In the simulation, we compare the influence of spatiotemporal data on the SSAE network structure.
Compared with the classic caching strategy Hash + LRU, Betw + LRU, and classic prediction algorithms SVM and BPNN, the proposed PCDS2AW proactive caching strategy can significantly improve WSN performance.
American Psychological Association (APA)
Lei, Fangyuan& Cai, Jun& Dai, Qingyun& Zhao, Huimin. 2019. Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications. Complexity،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1132144
Modern Language Association (MLA)
Lei, Fangyuan…[et al.]. Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications. Complexity No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1132144
American Medical Association (AMA)
Lei, Fangyuan& Cai, Jun& Dai, Qingyun& Zhao, Huimin. Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications. Complexity. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1132144
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132144