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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic


eISSN: 1178-5608



VOLUME 9 , ISSUE 3 (September 2016) > List of articles


Zhihao Cui / Jize Yan

Keywords : Mobile Phone, 5G, User Behaviour, Cloud Sensing, Wi-Fi, WWAN, Data Analysis.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 3, Pages 1,189-1,219, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 03-June-2016 / Accepted: 05-July-2016 / Published Online: 01-September-2016



4G has been widely commercialised, and 5G is currently under development. The expected
data bandwidth for 5G is 100 times faster than 4G and 500 times faster than 3G; however, the evolution
of telecommunication technologies involves both a boost in speed and the enhancement of user
experience. The key word used to describe 5G is ‘user-centric’, rather than ‘service-centric’ for 4G, and
thus user behaviours of mobile data usage should be further investigated. On the other hand, the
testing equipment currently being used for base stations is limited to hardware devices, such as
spectrum analysers and power meters. These testing methods do not include the considerable potential
variations in data demands due to changes in user behaviours, which could be resolved by presuming
that all data resources could be dynamically allocated by real-time events.
A complete system has been designed and implemented in this study to investigate current user
behaviours regarding mobile data usage. The system consists of three individual parts, including a user
iOS application, a web server and an administrative iOS application. Ten devices were tested within the two-month data collection period. Although the sample size was too small to produce any statistical results, it was found that data usage behaviours differ from user to user, with the exception of using
more than 10 times the Wi-Fi over WWAN data at all times. The data also proved that some of the
usage case families, which are described in the NGMN 5G white paper, do have strong demands, which
could not be fulfilled using current telecommunication technologies due to technological gaps.
This paper shows that the system proposed is a feasible method to investigate user behaviours of mobile
data usage. If the sample size of users involved could be increased in the future, it would be possible to
develop a model for real-time simulations of mobile users in specific areas so that limited connection
resources could be dynamically allocated. Moreover, the basic communication infra-structures, such as
base stations, should be well-planned and developed in advance to fulfill the potential 5G demand.

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[1] GSMA. The mobile economy 2014.
[2] NGMNA. NGMN 5G white paper.
[3] A.H. Khan, M.A. Qadeer, J.A. Ansari, and S. Waheed. 4G as a next generation wireless
network. In Future Computer and Communication, 2009. ICFCC 2009. International Conference
on, pages 334-338, April 2009.
[4] Anritsu MT8221B High Performance Base Station Analyzer Manual, 2011.
[5] Rohde-Schwarz ROMES4 Drive Test Software - Product Brochure, Nov 2011.
[6] R. Mizouni and M. El Barachi. Mobile phone sensing as a service: Business model and use
cases. In Next Generation Mobile Apps, Services and Technologies (NGMAST), 2013 Seventh
International Conference on, pages 116-121, Sept 2013.
[7] P. Leakkaw and S. Panichpapiboon. Speed estimation through mobile sensing. In TENCON
2014 - 2014 IEEE Region 10 Conference, pages 1-5, Oct 2014.
[8] E. Miluzzo, N. D. Lane, K. Fodor, R. Peterson, H. Lu, M. Musolesi, S. B. Eisenman, X.
Zheng, and A. T. Campbell. Sensing meets mobile social networks: The design, implementation
and evaluation of the cenceme application. In Proceedings of the 6th ACM Conference on
Embedded Network Sensor Systems, SenSys '08, pages 337-350, New York, NY, USA, 2008.
[9] M. Mun, S. Reddy, K. Shilton, N. Yau, J. Burke, D. Estrin, M. Hansen, E. Howard, R. West,
and P. Boda. Peir, the personal environmental impact report, as a platform for participatory
sensing systems research. In Proceedings of the 7th International Conference on Mobile Systems,
Applications, and Services, MobiSys '09, pages 55-68, New York, NY, USA, 2009. ACM.
[10] B. P. L. Lo, S. Thiemjarus, R. King, and G. Z. Yang. Body Sensor Network A Wireless
Sensor Platform For Pervasive Healthcare Monitoring. In Proceedings of the 3rd International
Conference on Pervasive Computing (PerCom), May 2005.
[11] S. Consolvo, D. W. McDonald, T. Toscos, M. Y. Chen, J. Froehlich, B. Harrison, P. Klasnja,
A. LaMarca, L. LeGrand, R. Libby, I. Smith, and J. A. Landay. Activity sensing in the wild: A
field trial of ubi t garden. In Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems, CHI '08, pages 1797-1806, New York, NY, USA, 2008. ACM.
[12] O. Postolache, P.S. Girao, P. Sinha, A. Anand, and G. Postolache. Health status monitor
based on embedded photoplethysmography and smart phone. In Medical Measurements and
Applications, 2008. MeMeA 2008. IEEE International Workshop on, pages 39-43, May 2008.
[13] N. Nawka, A.K. Maguliri, D. Sharma, and P. Saluja. SESGARH: A scalable extensible
smart-phone based mobile gateway and application for remote health monitoring. In Internet
Multimedia Systems Architecture and Application (IMSAA), 2011 IEEE 5th International
Conference on, pages 1-6, Dec 2011.
[14] A. Sieber, X. Yong, A. L'Abbate, and R. Bedini. Cardiac sentinel: A smart GSM based
embedded solution for continuous remote monitoring of cardiac patients. In Intelligent Solutions
in Embedded Systems, 2008 International Workshop on, pages 1-11, July 2008.
[15] N.D. Lane, E. Miluzzo, Hong Lu, D. Peebles, T. Choudhury, and A.T. Campbell. A survey
of mobile phone sensing. Communications Magazine, IEEE, 48(9):140-150, Sept 2010.
[16] J. Yang, Y. Qiao, X. Zhang, H. He, F. Liu, and G. Cheng. Characterizing user behavior in
mobile internet. Emerging Topics in Computing, IEEE Transactions on, PP(99):1-1, 2014.
[17] M. Vojnovic. On mobile user behaviour patterns. In Communications, 2008 IEEE
International Zurich Seminar on, pages 26-29, March 2008.
[18] T. Yamakami. Toward understanding the mobile internet user behavior: a method-ology for
user clustering with aging analysis. In Parallel and Distributed Computing, Applications and
Technologies, 2003. PDCAT'2003. Proceedings of the Fourth Inter-national Conference on,
pages 85-89, Aug 2003.
[19] Apple. iPhone6 technology.
[20] R.A. Finkel and J.L. Bentley. Quad trees a data structure for retrieval on composite keys.
Acta Informatica, 4(1):1-9, 1974.
[21] N. Balasubramanian, A. Balasubramanian, and A. Venkataramani. En-ergy consumption in
mobile phones: A measurement study and implications for network applications. In Proceedings
of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, IMC '09, pages
280-293, New York, NY, USA, 2009. ACM.