Umar Javed
Ramon Caceres
ABSTRACT
Consumers all over the world are increasingly using their smartphones on the go and expect consistent, high quality connectivity at all times. A key network primitive that enables continuous connectivity in cellular networks is handoff. Although handoffs are necessary for mobile devices to maintain connectivity, they can also cause short-term disruptions in application performance. Thus, applications could benefit from the ability to predict impending handoffs with reasonable accuracy, and modify their behavior to counter the performance degradation that accompanies handoffs. In this paper, we study whether attributes relating to the cellular network conditions measured at handsets can accurately predict handoffs. In particular, we develop a machine learning framework to predict handoffs in the near future. An evaluation on handoff traces from a large US cellular carrier shows that our approach can achieve 80% accuracy -- 27% better than a naive predictor.
- Y. Freund. Schapire: Experiments with a new boosting algorithm. In Proc. ICML, 1996.Google Scholar
- L. Liao, D. Fox, and H. Kautz. Location-based activity recognition using relational markov networks. In Proceedings of the 19th international joint conference on Artificial intelligence, pages 773--778, San Francisco, CA, USA, 2005. Morgan Kaufmann Publishers Inc. Google ScholarDigital Library
- L.-L. Lu and J.-L. C. Wu. Handoff prediction by mobility characteristics in wireless broadband networks. In Proc. IEEE WOWMOM, pages 469--471, 2005. Google ScholarDigital Library
- P. Lunden, J. Aijanen, K. Aho, and T. Ristaniemi. Performance of VoIP over HSDPA in mobility scenarios. In Proc. IEEE Vehicular Technology Conference, 2008.Google ScholarCross Ref
- H. Masnadi-shirazi and N. Vasconcelos. Asymmetric boosting. In Proc. ICML, 2007. Google ScholarDigital Library
- A. J. Nicholson and B. D. Noble. Breadcrumbs: forecasting mobile connectivity. In Proc. MobiCom, pages 46--57, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- S. Pack and Y. Choi. Fast handoff scheme based on mobility prediction in public wireless LAN systems. pages 489--495, 2004.Google Scholar
- F. Qian, Z. Wang, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck. Characterizing radio resource allocation for 3G networks. In Proc. IMC, 2010. Google ScholarDigital Library
- R. E. Schapire and Y. Singer. Boostexter: A boosting-based system for text categorization. In Machine Learning, pages 135--168, 2000. Google ScholarDigital Library
- A. Schulman, V. Navda, R. Ramjee, N. Spring, P. Deshpande, C. Grunewald, K. Jain, and V. N. Padmanabhan. Bartendr: a practical approach to energy-aware cellular data scheduling. In Proc. MobiCom, 2010. Google ScholarDigital Library
- T. Sohn, A. Varshavsky, A. Lamarca, M. Y. Chen, T. Choudhury, I. Smith, S. Consolvo, J. Hightower, W. G. Griswold, and E. D. Lara. Mobility detection using everyday GSM traces. In Proc. Ubicomp, pages 212--224. Springer, 2006. Google Scholar
- L. Song, U. Deshpande, U. C. Kozat, D. Kotz, and R. Jain. Predictability of wlan mobility and its effects on bandwidth provisioning. In Proc. INFOCOM, 2006.Google ScholarCross Ref
- S.-F. Su. The UMTS Air-Interface in RF Engineering. McGraw Hill, 2007.Google Scholar
- F. P. Tso, J. Teng, W. Jia, and D. Xuan. Mobility: a double-edged sword for HSPA networks: a large-scale test on Hong Kong mobile hspa networks. In Proc. MobiHoc, 2010. Google ScholarDigital Library
- A. Varshavsky, E. de Lara, J. Hightower, A. LaMarca, and V. Otsason. GSM indoor localization. Pervasive Mob. Comput., 3:698--720, December 2007. Google ScholarDigital Library
- P. Viola and M. Jones. Fast and robust classification using asymmetric AdaBoost and a detector cascade. In Proc. NIPS, 2001.Google Scholar
- Y. Zheng, Y. Chen, Q. Li, X. Xie, and W.-Y. Ma. Understanding transportation modes based on GPS data for web applications. TWEB, 4(1), 2010. Google ScholarDigital Library
Index Terms
- Predicting handoffs in 3G networks
Recommendations
Predicting handoffs in 3G networks
Consumers all over the world are increasingly using their smartphones on the go and expect consistent, high quality connectivity at all times. A key network primitive that enables continuous connectivity in cellular networks is handoff. Although ...
Mobility management across hybrid wireless networks: Trends and challenges
Future generation wireless networks are envisioned to be a combination of diverse but complementary access technologies. Internetworking these types of networks will provide mobile users with ubiquitous connectivity across a wide range of networking ...
Performance Analysis of UMTS and WLAN Interworking with Multi-service Load
ARTCOM '10: Proceedings of the 2010 International Conference on Advances in Recent Technologies in Communication and ComputingFuture generation wireless networks are expected to support multiple wireless access technologies, each with different access bandwidth and coverage range. Two of these technologies include Universal Mobile Telecommunications System (UMTS) and IEEE ...
Comments