Paper Info
Title
Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications
Abstract
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.
Year
DOI
Venue
2019
10.1155/2019/5498606
COMPLEXITY
Field
DocType
Volume
Autoencoder,Cache,Network packet,Computer network,Hash function,Artificial intelligence,Deep learning,Software-defined networking,Wireless sensor network,Mathematics,Machine learning,Network model
Journal
2019
ISSN
Citations 
PageRank 
1076-2787
1
0.36
References 
Authors
0
4
Name
Order
Citations
PageRank
Fangyuan Lei120.73
Jun Cai237339.29
Qingyun Dai314823.91
Huimin Zhao420623.43