{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:25:32Z","timestamp":1750307132791,"version":"3.41.0"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2012,1,11]],"date-time":"2012-01-11T00:00:00Z","timestamp":1326240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGOPS Oper. Syst. Rev."],"published-print":{"date-parts":[[2012,1,11]]},"abstract":"<jats:p>\n            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\n            <jats:italic>handoff<\/jats:italic>\n            . 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.\n          <\/jats:p>","DOI":"10.1145\/2094091.2094109","type":"journal-article","created":{"date-parts":[[2012,1,17]],"date-time":"2012-01-17T17:21:44Z","timestamp":1326820904000},"page":"65-70","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Predicting handoffs in 3G networks"],"prefix":"10.1145","volume":"45","author":[{"given":"Umar","family":"Javed","sequence":"first","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongsu","family":"Han","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramon","family":"Caceres","sequence":"additional","affiliation":[{"name":"AT&amp;T Labs - Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey","family":"Pang","sequence":"additional","affiliation":[{"name":"AT&amp;T Labs - Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Srinivasan","family":"Seshan","sequence":"additional","affiliation":[{"name":"Carnegie Mellon Univeristy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Varshavsky","sequence":"additional","affiliation":[{"name":"AT&amp;T Labs - Research"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,1,11]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proc. 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Fast handoff scheme based on mobility prediction in public wireless LAN systems. pages 489--495, 2004."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1879141.1879159"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007649029923"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1859995.1860006"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/11853565_13"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2006.171"},{"key":"e_1_2_1_13_1","volume-title":"McGraw Hill","author":"Su S.-F.","year":"2007","unstructured":"S.-F. Su . The UMTS Air-Interface in RF Engineering . McGraw Hill , 2007 . S.-F. Su. The UMTS Air-Interface in RF Engineering. McGraw Hill, 2007."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1860093.1860105"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2007.07.004"},{"key":"e_1_2_1_16_1","volume-title":"Proc. NIPS","author":"Viola P.","year":"2001","unstructured":"P. Viola and M. Jones . Fast and robust classification using asymmetric AdaBoost and a detector cascade . In Proc. NIPS , 2001 . P. Viola and M. Jones. Fast and robust classification using asymmetric AdaBoost and a detector cascade. In Proc. 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