Abstract
Smartphones assist their users throughout daily life activities. There is much emphasis on the user's mobility support in the research at large. However, we have a weak understanding about users mobility (are they really moving?) and how well connected are they across their typical day. First, to infer mobility state of users, we derived and evaluated the accuracy of a machine learning-based model, i.e., MobilitySensor, which is based solely on smartphone built-in sensors. It is a tree-based model, defined for each network operator and its average accuracy reaches 91%. Next, we leverage our algorithm to explore the mobility of 34 users served by 3 different Swiss operators (OP) during a period of six months, correlating it with their connectivity. The user study results showed that users are statistically significantly more mobile than we observed in the past (21±7% of the time, i.e., up to 4.3h vs. 13±12%, i.e., 2.7h in 2011) and when they are mobile, 4G network is available to them 38±12% of the time. Furthermore, when mobile, depending on their operator, they may be provided with up to around 10% of the time with 2.5G connectivity (for OP1 and OP2 vs. only 4% OP3), or provided mainly with 3G (49% for OP1 vs. 34% for OP3). Based on the results we provide a set of design implications for application providers, users and operators alike, all striving to improve the mobile users' quality of experience (QoE).
Originalsprog | Engelsk |
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Titel | 2015 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) |
Antal sider | 9 |
Forlag | IEEE |
Publikationsdato | 2015 |
Sider | 91-99 |
ISBN (Elektronisk) | 978-1-4673-9283-9 |
DOI | |
Status | Udgivet - 2015 |
Begivenhed | 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops - Brno, Tjekkiet Varighed: 6 okt. 2015 → 8 okt. 2015 Konferencens nummer: 7 |
Konference
Konference | 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops |
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Nummer | 7 |
Land/Område | Tjekkiet |
By | Brno |
Periode | 06/10/2015 → 08/10/2015 |
Emneord
- 3G mobile communication
- 4G mobile communication
- learning (artificial intelligence)
- mobile computing
- mobility management (mobile radio)
- quality of experience
- smart phones
- 2.5G connectivity
- 3G network
- 4G network
- OP
- QoE
- Swiss operator
- machine learning-based model
- mobility inference model
- smartphone
- tree-based model
- Context
- Current measurement
- Global Positioning System
- Mobile communication
- Mobile computing
- Quality of service
- Radiation detectors
- Mobility
- Quality of Experience
- Quality of Service
- Wireless communication