More mobile & not so well-connected yet: users' mobility inference model and 6 month field study

Katarzyna Wac; Gerardo Pinar; Mattia Gustarini; Jerome Marchanoff

doi:10.1109/ICUMT.2015.7382411

More mobile & not so well-connected yet: users' mobility inference model and 6 month field study

Katarzyna Wac, Gerardo Pinar, Mattia Gustarini, Jerome Marchanoff

Department of Computer Science

1 Citation (Scopus)

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).

Original language	English
Title of host publication	2015 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
Number of pages	9
Publisher	IEEE
Publication date	13 Jan 2016
Pages	91-99
ISBN (Electronic)	978-1-4673-9283-9
DOIs	https://doi.org/10.1109/ICUMT.2015.7382411
Publication status	Published - 13 Jan 2016
Event	7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops - Brno, Czech Republic Duration: 6 Oct 2015 → 8 Oct 2015 Conference number: 7

Conference

Conference	7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops
Number	7
Country/Territory	Czech Republic
City	Brno
Period	06/10/2015 → 08/10/2015

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10.1109/ICUMT.2015.7382411

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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 et al.
2015 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). IEEE, 2016. p. 91-99.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Wac, K, Pinar, G, Gustarini, M & Marchanoff, J 2016, More mobile & not so well-connected yet: users' mobility inference model and 6 month field study. in 2015 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). IEEE, pp. 91-99, 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, Brno, Czech Republic, 06/10/2015. https://doi.org/10.1109/ICUMT.2015.7382411

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title = "More mobile & not so well-connected yet: users' mobility inference model and 6 month field study",

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).",

keywords = "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",

author = "Katarzyna Wac and Gerardo Pinar and Mattia Gustarini and Jerome Marchanoff",

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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).

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KW - 4G mobile communication

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KW - Quality of service

KW - Radiation detectors

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KW - Quality of Experience

KW - Quality of Service

KW - Wireless communication

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DO - 10.1109/ICUMT.2015.7382411

M3 - Article in proceedings

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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 -

More mobile & not so well-connected yet: users' mobility inference model and 6 month field study

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