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Towards a Predictive Model for Mobile Internet Quality


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Reference/Citation to a paper: Katarzyna Wac, Gerardo Pinar, Mattia Gustarini, Jerome Marchanoff, More Mobile & Not so Well-connected yet: Users' Mobility Inference Model and 6 Month Field Study, 7th International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT 2015), Brno, Czech Republic, October 2015.

Reference/Citation to a poster: Daniel Weibel, Katarzyna Wac, Towards a Predictive Model for Mobile Internet Quality, International Workshop on Traffic Monitoring and Analysis (TMA), PhD Poster Session, Barcelona, Spain, May 2015.

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Towards a Predictive Model for Mobile Internet Quality

  1. 1. Towards a Predictive Model for Mobile Internet Quality Daniel Weibel & Assoc. Prof. Katarzyna Wac {daniel.weibel, katarzyna.wac} Introduction Mobile Internet (MI) means the use of the Internet from mobile devices (e.g. smartphone, laptop) over the cellular network. Mobility and the diversity of technologies induce a variable Quality of Service (QoS) of MI. Our goal is to predict the quality of MI for its users in specific contexts. We focus in particular on the MI-quality on public transport trajectories. Our method is to investigate the availability of certain services, such as Skype or games, in these contexts based on their round-trip time (RTT) requirements. These investigations are based on data that we have collected through our QoSIS and mQoL projects over an extended period of time. QoSIS Quality of Service Information System QoS measures ● Used applications ● Phone state ● Mobility ● Cell ID ● RTT ● Signal strength QoSIS Archiving/ analysis Push data User interaction Mobile network Ping app QoSIS is an Android OS application logging information on a smartphone in 1-minute in- tervals, or on event. This information includes QoS measurements and user actions. mQoL Mobile Communications for Quality of Life −→ Living lab with QoSIS users • Each participant owns a Nexus 5 phone running the QoSIS Ping app • To date 48 study participants located in the Geneva region • Running since March 2012 Study: QoS on Public Transport Lines Pilot study for trajectory Geneva–Neuchâtel • Journey including tram, bus, train of approximately 2 hours (130 km) • Data from one mQoL user (participating since November 2013) • Analysed data – 29 traces Geneva–Neuchâtel – 25 traces Neuchâtel–Geneva – Collected between November 2014 and March 2015 • A trace is a sequence of cell IDs and corresponding measured RTTs • Results apply to one of the three Swiss mobile network operators Used cells along Geneva–Neuchâtel trajectory (approximate cell locations from OpenCellID). RTT Requirements Skype ≤ 150 ms: good ≤ 400 ms: acceptable > 400 ms: unacceptable Quake ≤ 100 ms Google Docs ≤ 150 ms Web browsing ≤ 2000 ms E-mail ≤ 3000 ms Maps ≤ 3000 ms Service Availability: Skype Minutes after start RTT[ms] 5010020050010002000 1 20 40 60 80 100 Geneva Neuchâtel 150 ms 400 ms Skype unacceptable (RTT > 400 ms) Skype acceptable (RTT 151−400 ms) Skype good (RTT ≤ 150 ms) Minutes after start RTT[ms] 5010020050010002000 1 20 40 60 80 100 120 Neuchâtel Geneva 150 ms 400 ms Skype unacceptable (RTT > 400 ms) Skype acceptable (RTT 151−400 ms) Skype good (RTT ≤ 150 ms) Measured RTTs for two sample traces in both directions, and the expected Skype quality. Percent Measures Median RTT Mean RTT Std. dev. RTT RTT ≤ 150 ms 91.4% 3506 46 ms 46.2 ms 8.7 ms 150 ms < RTT ≤ 400 ms 2% 77 295 ms 287.6 ms 75.2 ms RTT > 400 ms 6.6% 252 989 ms 1398.0 ms 1100.0 ms LTE (4G) cells 92% 3529 46 ms 50.3 ms 73.2 ms EDGE (2.5G) cells 8% 306 744 ms 1173.0 ms 1089.7 ms Aggregated analysis of the 29 Geneva–Neuchâtel trips. Total 3835 RTT measurements. Analysing RTT sequence reveals a pattern: 15 minutes with unacceptable or medium Skype quality near Neuchâtel. The reason is that in this sector the user has only EDGE connection whereas on rest of the trajectory there is LTE connection. Future Work We have shown that RTT measurements allow to identify patterns of QoS on public transport lines. QoS variations are mostly induced by the used network type (e.g. RTT mean of EDGE is around 23 times higher than LTE). Future work will expand the investigations to more mQoL users, larger time periods of data, additional network operators, and additional public transport trajectories. The ways of aggregating the data for enabling QoS prediction for specific public transport trajectories have to be investigated further. Ultimately, means of effectively communicating the consequences of the expected QoS to the user have to be devised. Collaboration & Sponsoring References [1] K. Wac, G. Pinar, M. Gustarini, J. Marchanoff. Smartphone Users’ Mobile Network’s Qual- ity Provision and VoLTE Intend: Six-months Field Study. In IEEE Symposium on a World of Wireless Mobile and Multimedia Networks. June 2015. [2] K. Wac, M. Fiordelli, M. Gustarini, H. Rivas. Quality of Life Technologies: Experiences from the Field and Key Research Challenges. In IEEE Internet Computing, Special Issue: Personalized Digital Health. August 2015. [3] K. Wac. Quality of Service Information System: Get to Know the Performance of Your Mobile Network Operator Anywhere-Anytime. In Mobile Computing, 23. 2013. CENTER FOR INFORMATICS (CUI) Quality of Life Group (QoL)