QOE Forecast Under the WSN Internet of Things
Internet of Things and Cloud Computing
Volume 5, Issue 2, April 2017, Pages: 29-37
Received: Feb. 21, 2017; Accepted: Apr. 13, 2017; Published: Jun. 7, 2017
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Authors
Yibin Hou, School of Software Engineering, Department of Information, Beijing University of Technology, Beijing, China
Jin Wang, School of Software Engineering, Department of Information, Beijing University of Technology, Beijing, China; Computer Science and Technology, Shijiazhuang Railway University, Shijiazhuang, China; Computer Center, Navy General Hospital, Beijing, China
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Abstract
The Internet of things, including Internet technology, including wired and wireless networks. In this paper, we investigate on the QOE because QOE is important in the network and packet loss rate is the key point in many papers. In order to study the QOE forecast under the Internet of things, building a NS2+MyEvalvid simulation platform, by the method of modifying QoS parameters to simulate different degrees of packet loss, focus on the QOE forecast under the Internet of things. Experimental results show that, QOE forecast under the Internet of things have many methods and is very important. SVM+PCA is an important method in the field of Internet of things, the Internet of things, including Internet technology, WSN networks, RFID can be part of the WSN network. High thinking turn thinking and intelligence to do system and intelligent housing system. The application of intelligent transportation and intelligent building and intelligent engineering system and Intelligent farmand JSP sponge in the Internet of things is the future direction of development.
Keywords
QOE, Forecast, Internet of Things
To cite this article
Yibin Hou, Jin Wang, QOE Forecast Under the WSN Internet of Things, Internet of Things and Cloud Computing. Vol. 5, No. 2, 2017, pp. 29-37. doi: 10.11648/j.iotcc.20170502.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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