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  1. Proceedings of the 3rd ACM SOSP Workshop on Networking, Systems, and Applications on Mobile Handhelds (MobiHeld '11)
  2. Predicting handoffs in 3G networks
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Predicting handoffs in 3G networks

Content Provider ACM Digital Library
Author Varshavsky, Alexander Caceres, Ramon Seshan, Srinivasan Javed, Umar Han, Dongsu Pang, Jeffrey
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.
Starting Page 1
Ending Page 6
Page Count 6
File Format PDF
ISBN 9781450309806
DOI 10.1145/2043106.2043114
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-10-23
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Measurement Wireless Handoff 3g Prediction Mobility Umts
Content Type Text
Resource Type Article
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