BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Handoff Prediction by Mobility Characteristics in Wireless Broadband Networks
WOWMOM '05 Proceedings of the Sixth IEEE International Symposium on World of Wireless Mobile and Multimedia Networks
Proceedings of the 24th international conference on Machine learning
Pervasive and Mobile Computing
BreadCrumbs: forecasting mobile connectivity
Proceedings of the 14th ACM international conference on Mobile computing and networking
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Understanding transportation modes based on GPS data for web applications
ACM Transactions on the Web (TWEB)
Bartendr: a practical approach to energy-aware cellular data scheduling
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Proceedings of the eleventh ACM international symposium on Mobile ad hoc networking and computing
Characterizing radio resource allocation for 3G networks
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Mobility detection using everyday GSM traces
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
| Hi-index | 0.00 |
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.