{"title":"PredictiveMobility Models based on Kth Markov Models","authors":"F. Lassabe, P. Canalda, P. Chatonnay, F. Spies","doi":"10.1109/PERSER.2006.1652248","DOIUrl":null,"url":null,"abstract":"With the massive arrival of wireless networks, the mobility of the terminals increases with the interconnections. New problems, such as mobile multimedia content streaming, arise with the emergence of new mobile multimedia services. In this paper, we present a mobility model based on the Markov models, especially the all-Kth Markov model. We present three predictive models: the K-past model, the K-to-J past model and its improvement, the K-to-1 past* model. The whole are pertinent solutions to tackle mobility patterns. We validate our approach firstly with various realistic benchmarks on data related to indoor WiFi positioning systems","PeriodicalId":377064,"journal":{"name":"2006 ACS/IEEE International Conference on Pervasive Services","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 ACS/IEEE International Conference on Pervasive Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PERSER.2006.1652248","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
With the massive arrival of wireless networks, the mobility of the terminals increases with the interconnections. New problems, such as mobile multimedia content streaming, arise with the emergence of new mobile multimedia services. In this paper, we present a mobility model based on the Markov models, especially the all-Kth Markov model. We present three predictive models: the K-past model, the K-to-J past model and its improvement, the K-to-1 past* model. The whole are pertinent solutions to tackle mobility patterns. We validate our approach firstly with various realistic benchmarks on data related to indoor WiFi positioning systems