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.
Highlights
According to the latest Cisco release of the Visual Network Index (VNI) [1] forecast, the number of devices connected to the Internet of Things (IoT) is projected to expand to somewhere between 20 and 46 billion by 2021
The main contributions of this paper are as follows: (1) in the wireless sensor networks (WSNs), we propose a method for constructing a distributed stack sparse autoencoder deep learning network; (2) Sparse Autoencoder (SSAE) use the spatiotemporal information of user request data packets to predict the data packet popularity; (3) under the cooperation of Software Defined Network (SDN) controller, cache nodes implement the proactive cache and replacement of the data packet content of the whole network, which makes the utilization of cache resources more reasonable; (4) simulation results show that, compared with Betw [14], Hash [15], and Opportunistic [16], the proposed proactive caching strategy could improve the performance of WSNs
We propose a simple structure of SDN / Network Function Virtualization (NFV) combined with SSAEs to solve the challenges of WSN, especially in traffic load and congestion management issues
Summary
According to the latest Cisco release of the Visual Network Index (VNI) [1] forecast, the number of devices connected to the Internet of Things (IoT) is projected to expand to somewhere between 20 and 46 billion by 2021. While billions of new devices connect to the network in a short period of time in WSNs, there will have huge data traffic. With the solidified resource management mode, when the upper-layer application requirements change, it is very difficult to apply flexible changes according to the new requirements. It is seriously wasting resources without realizing dynamic perception
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