Analysis of Big Data and Quality-of-Experience in High-Density Wireless Network

The proliferation of smart devices, along with the availability of bandwidth-intensive applications are generating huge volumes of data that create challenges to IT industries. Data handling becomes more troublesome when mobile user's gather in tens and thousands of quantity at confine locations and generates Big Data. Analysis and storing of this huge, varied and complex data put great challenges to the Network Service Providers (NSP). Thus, service providers are facing problems in managing big data in dense environment and maintaining user's Quality of Experience (QoE). However, big data also provide great opportunities to NSP. The accurate analysis of big data in real-time reveals the user experience of network services which helps the service providers to take timely action to improve user QoE. Thus, this paper presents an overview of Big Data and QoE in High-Density Wireless Network (HDWN) environment.

[1]  Antonio Liotta,et al.  Predicting quality of experience in multimedia streaming , 2009, MoMM.

[2]  Markus Fiedler,et al.  A generic quantitative relationship between quality of experience and quality of service , 2010, IEEE Network.

[3]  Gerardo Rubino,et al.  Controlling Multimedia QoS in the Future Home Network Using the PSQA Metric , 2006, Comput. J..

[4]  Abraham O. Fapojuwo,et al.  On modeling and measuring quality of experience performance in IEEE 802.11n wireless networks , 2014, 2014 IEEE International Conference on Communications (ICC).

[5]  Olga Galinina,et al.  Predicting user QoE satisfaction in current mobile networks , 2014, 2014 IEEE International Conference on Communications (ICC).

[6]  Lea Skorin-Kapov,et al.  Survey and Challenges of QoE Management Issues in Wireless Networks , 2013, J. Comput. Networks Commun..

[7]  Christian Timmerer,et al.  Survey of web-based crowdsourcing frameworks for subjective quality assessment , 2014, 2014 IEEE 16th International Workshop on Multimedia Signal Processing (MMSP).

[8]  Selim Ickin,et al.  Studying the experience of mobile applications used in different contexts of daily life , 2011, W-MUST '11.

[9]  Peter Reichl,et al.  Logarithmic laws in service quality perception: where microeconomics meets psychophysics and quality of experience , 2013, Telecommun. Syst..

[10]  Jeffrey J. Spiess,et al.  Using Big Data to Improve Customer Experience and Business Performance , 2014, Bell Labs Tech. J..

[11]  Seyed A. Shahrestani,et al.  A fuzzy logic approach for Quality of Service quantification in wireless and mobile networks , 2014, 39th Annual IEEE Conference on Local Computer Networks Workshops.

[12]  Yan Huang,et al.  Management and application of mobile big data , 2015, Int. J. Embed. Syst..

[13]  P. Leonhardt.  Wireless at the "Connected Games": How the London 2012 Olympic and Paralympic Games Utilized the Latest Wi-Fi Technology , 2013 .

[14]  Luc Martens,et al.  Linking Users' Subjective QoE Evaluation to Signal Strength in an IEEE 802.11b/g Wireless LAN Environment , 2010, EURASIP J. Wirel. Commun. Netw..

[15]  Markus Fiedler,et al.  Quality of Experience from user and network perspectives , 2010, Ann. des Télécommunications.

[16]  Peter Schelkens,et al.  Qualinet White Paper on Definitions of Quality of Experience , 2013 .

[17]  Lingfen Sun,et al.  QoE Prediction Model and its Application in Video Quality Adaptation Over UMTS Networks , 2012, IEEE Transactions on Multimedia.

[18]  Gabi Dreo Rodosek,et al.  Towards evaluating type of service related Quality-of-Experience on mobile networks , 2014, 2014 7th IFIP Wireless and Mobile Networking Conference (WMNC).

[19]  Peter Reichl,et al.  The Logarithmic Nature of QoE and the Role of the Weber-Fechner Law in QoE Assessment , 2010, 2010 IEEE International Conference on Communications.