More mobile & not so well-connected yet: users' mobility inference model and 6 month field study
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More mobile & not so well-connected yet : users' mobility inference model and 6 month field study. / Wac, Katarzyna; Pinar, Gerardo; Gustarini, Mattia; Marchanoff, Jerome.
2015 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). IEEE, 2015. p. 91-99.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - More mobile & not so well-connected yet
T2 - 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops
AU - Wac, Katarzyna
AU - Pinar, Gerardo
AU - Gustarini, Mattia
AU - Marchanoff, Jerome
N1 - Conference code: 7
PY - 2015
Y1 - 2015
N2 - 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).
AB - 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).
KW - 3G mobile communication
KW - 4G mobile communication
KW - learning (artificial intelligence)
KW - mobile computing
KW - mobility management (mobile radio)
KW - quality of experience
KW - smart phones
KW - 2.5G connectivity
KW - 3G network
KW - 4G network
KW - OP
KW - QoE
KW - Swiss operator
KW - machine learning-based model
KW - mobility inference model
KW - smartphone
KW - tree-based model
KW - Context
KW - Current measurement
KW - Global Positioning System
KW - Mobile communication
KW - Mobile computing
KW - Quality of service
KW - Radiation detectors
KW - Mobility
KW - Quality of Experience
KW - Quality of Service
KW - Wireless communication
U2 - 10.1109/ICUMT.2015.7382411
DO - 10.1109/ICUMT.2015.7382411
M3 - Article in proceedings
SP - 91
EP - 99
BT - 2015 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
PB - IEEE
Y2 - 6 October 2015 through 8 October 2015
ER -
ID: 155831018