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Impacts of Human Mobility in Mobile Data Offloading

Published:01 October 2018Publication History

ABSTRACT

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.

References

  1. Daniel Ashbrook and Thad Starner. 2003. Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous computing 7, 5 (2003), 275--286. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Francesco Calabrese, Massimo Colonna, Piero Lovisolo, Dario Parata, and Carlo Ratti. 2011. Real-time urban monitoring using cell phones: A case study in Rome. IEEE Transactions on Intelligent Transportation Systems 12, 1 (2011), 141--151. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Xin Cao, Gao Cong, and Christian S Jensen. 2010. Mining significant semantic locations from GPS data. Proceedings of the VLDB Endowment 3, 1--2 (2010), 1009--1020. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Man Hon Cheung and Jianwei Huang. 2015. DAWN: Delay-aware Wi-Fi offloading and network selection. IEEE Journal on Selected Areas in Communications 33, 6 (2015), 1214--1223.Google ScholarGoogle ScholarCross RefCross Ref
  5. Sabhia Firdaus and Md Ashraf Uddin. 2015. A Survey on Clustering Algorithms and Complexity Analysis. International Journal of Computer Science Issues (IJCSI) 12, 2 (2015), 62.Google ScholarGoogle Scholar
  6. Cisco Visual Networking Index. 2017. Global Mobile Data Traffic Forecast Update, 2016--2021 White Paper. (2017). https: //www.cisco.com/c/en/us/solutions/collateral/service-provider/ visual-networking-index-vni/mobile-white-paper-c11--520862.html (accessed 01-06--2018).Google ScholarGoogle Scholar
  7. Shan Jiang, Joseph Ferreira, and Marta C Gonzalez. 2017. Activitybased human mobility patterns inferred from mobile phone data: A case study of Singapore. IEEE Transactions on Big Data 3, 2 (2017), 208--219.Google ScholarGoogle ScholarCross RefCross Ref
  8. Trupti M Kodinariya and Prashant R Makwana. 2013. Review on determining number of Cluster in K-Means Clustering. International Journal 1, 6 (2013), 90--95.Google ScholarGoogle Scholar
  9. Emanuel Lima, Ana Aguiar, and Paulo Carvalho. 2017. Offloading Surrogates Characterization via Mobile Crowdsensing. In Proceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications. ACM, 7--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Michela Papandrea, Karim Keramat Jahromi, Matteo Zignani, Sabrina Gaito, Silvia Giordano, and Gian Paolo Rossi. 2016. On the properties of human mobility. Computer Communications 87 (2016), 19--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Suranga Seneviratne, Aruna Seneviratne, Prasant Mohapatra, and Pierre-Ugo Tournoux. 2013. Characterizing wifi connection and its impact on mobile users: practical insights. In Proceedings of the 8th ACM international workshop on Wireless network testbeds, experimental evaluation & characterization. ACM, 81--88. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Yu Zheng, Like Liu, Longhao Wang, and Xing Xie. 2008. Learning transportation mode from raw gps data for geographic applications on the web. In Proceedings of the 17th international conference on World Wide Web. ACM, 247--256. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th international conference on World wide web. ACM, 791--800. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Changqing Zhou, Nupur Bhatnagar, Shashi Shekhar, and Loren Terveen. 2007. Mining personally important places from GPS tracks. In Data Engineering Workshop, 2007 IEEE 23rd International Conference on. IEEE, 517--526. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          CHANTS '18: Proceedings of the 13th Workshop on Challenged Networks
          October 2018
          77 pages
          ISBN:9781450359269
          DOI:10.1145/3264844

          Copyright © 2018 ACM

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          New York, NY, United States

          Publication History

          • Published: 1 October 2018

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          CHANTS '18 Paper Acceptance Rate9of27submissions,33%Overall Acceptance Rate61of159submissions,38%

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