Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications
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 predi...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/5498606 |
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| _version_ | 1832550735016886272 |
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| author | Fangyuan Lei Jun Cai Qingyun Dai Huimin Zhao |
| author_facet | Fangyuan Lei Jun Cai Qingyun Dai Huimin Zhao |
| author_sort | Fangyuan Lei |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eb1e1fd252c1420b81cb552682dbae26 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-eb1e1fd252c1420b81cb552682dbae262025-02-03T06:06:08ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/54986065498606Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision ApplicationsFangyuan Lei0Jun Cai1Qingyun Dai2Huimin Zhao3School of Electronic and Information, Guangdong Polytechnic Normal University, Guangzhou 510640, ChinaSchool of Electronic and Information, Guangdong Polytechnic Normal University, Guangzhou 510640, ChinaSchool of Electronic and Information, Guangdong Polytechnic Normal University, Guangzhou 510640, ChinaSchool of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510640, ChinaWireless 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.http://dx.doi.org/10.1155/2019/5498606 |
| spellingShingle | Fangyuan Lei Jun Cai Qingyun Dai Huimin Zhao Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications Complexity |
| title | Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications |
| title_full | Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications |
| title_fullStr | Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications |
| title_full_unstemmed | Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications |
| title_short | Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications |
| title_sort | deep learning based proactive caching for effective wsn enabled vision applications |
| url | http://dx.doi.org/10.1155/2019/5498606 |
| work_keys_str_mv | AT fangyuanlei deeplearningbasedproactivecachingforeffectivewsnenabledvisionapplications AT juncai deeplearningbasedproactivecachingforeffectivewsnenabledvisionapplications AT qingyundai deeplearningbasedproactivecachingforeffectivewsnenabledvisionapplications AT huiminzhao deeplearningbasedproactivecachingforeffectivewsnenabledvisionapplications |