Impacts of Human Mobility in Mobile Data Offloading

Due to the limited coverage of WiFi APs, users' mobility has a severe impact on the performance of mobile offloading systems. The present study is a contribution in this context as offloading zones are identified and characterized from individual GPS trajectories when small offloading time windows are considered. The results show that (i) attending to users mobility, ten seconds is the minimum offloading time window that can be considered; (ii) offloading predictive methods can have variable performance according to the period of the day; and (iii) per-user opportunistic decision models can determine offloading system design and performance.

[1]  Man Hon Cheung,et al.  DAWN: Delay-Aware Wi-Fi Offloading and Network Selection , 2015, IEEE Journal on Selected Areas in Communications.

[2]  Thad Starner,et al.  Using GPS to learn significant locations and predict movement across multiple users , 2003, Personal and Ubiquitous Computing.

[3]  Gian Paolo Rossi,et al.  On the properties of human mobility , 2016, Comput. Commun..

[4]  Prasant Mohapatra,et al.  Characterizing WiFi connection and its impact on mobile users: practical insights , 2013, WiNTECH '13.

[5]  Xing Xie,et al.  Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.

[6]  Sabhia Firdaus,et al.  A Survey on Clustering Algorithms and Complexity Analysis , 2015 .

[7]  Carlo Ratti,et al.  Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome , 2011, IEEE Transactions on Intelligent Transportation Systems.

[8]  Shashi Shekhar,et al.  Mining Personally Important Places from GPS Tracks , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[9]  Trupti M. Kodinariya,et al.  Review on determining number of Cluster in K-Means Clustering , 2013 .

[10]  Xing Xie,et al.  Learning transportation mode from raw gps data for geographic applications on the web , 2008, WWW.

[11]  Joseph Ferreira,et al.  Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore , 2017, IEEE Transactions on Big Data.

[12]  Paulo Carvalho,et al.  Offloading Surrogates Characterization via Mobile Crowdsensing , 2017, CrowdSenSys@SenSys.

[13]  Christian S. Jensen,et al.  Mining significant semantic locations from GPS data , 2010, Proc. VLDB Endow..