1 College of Smart City, Beijing Union University, Beijing 100101, China. like@buu.edu.cn.
2 College of Smart City, Beijing Union University, Beijing 100101, China. whyxdt@163.com.
3 College of Smart City, Beijing Union University, Beijing 100101, China. 151081210202@buu.edu.cn.
4 College of Robotics, Beijing Union University, Beijing 100101, China. duyu@buu.edu.cn.
5 College of Robotics, Beijing Union University, Beijing 100101, China. yuansheng@buu.edu.cn.
6 Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G IM8, Canada. omair@ece.concordia.ca.

A Crowdsensing Based Analytical Framework for Perceptional Degradation of OTT Web Browsing.

Sensors (Basel). 2018 May 15;18(5):

Authors: Li K, Wang H, Xu X, Du Y, Liu Y, Ahmad MO

Abstract

Service perception analysis is crucial for understanding both user experiences and network quality as well as for maintaining and optimizing of mobile networks. Given the rapid development of mobile Internet and over-the-top (OTT) services, the conventional network-centric mode of network operation and maintenance is no longer effective. Therefore, developing an approach to evaluate and optimizing users' service perceptions has become increasingly important. Meanwhile, the development of a new sensing paradigm, mobile crowdsensing (MCS), makes it possible to evaluate and analyze the user's OTT service perception from end-user's point of view other than from the network side. In this paper, the key factors that impact users' end-to-end OTT web browsing service perception are analyzed by monitoring crowdsourced user perceptions. The intrinsic relationships among the key factors and the interactions between key quality indicators (KQI) are evaluated from several perspectives. Moreover, an analytical framework of perceptional degradation and a detailed algorithm are proposed whose goal is to identify the major factors that impact the perceptional degradation of web browsing service as well as their significance of contribution. Finally, a case study is presented to show the effectiveness of the proposed method using a dataset crowdsensed from a large number of smartphone users in a real mobile network. The proposed analytical framework forms a valuable solution for mobile network maintenance and optimization and can help improve web browsing service perception and network quality.

PMID: 29762493 [PubMed]