Presentation | 2012-12-14 Mobile device prediction for location-based cloud service Haibo Yan, Masato Kitakami, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Mobility prediction is one of the most essential issues which need to be explored for management in a location-based cloud service. A good prediction algorithm can not only reduce power consumption and waiting time, but also increase dependability of mobile devices. Existing prediction methods are generally based on mass history data. However, they can`t work well on the prediction of a device which has the features of short moving time and elusive moving tendency. Taking into account these characteristics, we proposed a prediction method based on an Adaptive Markov Chain Monte Carlo method for mobile device prediction. Simulation results show that the proposed algorithm has more efficiency than traditional algorithm. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Mobile device / prediction / Monte Carlo method / Adaptive Markov Chain Monte Carlo(Adaptive MCMC) |
Paper # | DC2012-73 |
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Conference Information | |
Committee | DC |
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Conference Date | 2012/12/7(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Dependable Computing (DC) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Mobile device prediction for location-based cloud service |
Sub Title (in English) | |
Keyword(1) | Mobile device |
Keyword(2) | prediction |
Keyword(3) | Monte Carlo method |
Keyword(4) | Adaptive Markov Chain Monte Carlo(Adaptive MCMC) |
1st Author's Name | Haibo Yan |
1st Author's Affiliation | Graduate School of Advanced Integration Science Chiba University() |
2nd Author's Name | Masato Kitakami |
2nd Author's Affiliation | Graduate School of Advanced Integration Science Chiba University |
Date | 2012-12-14 |
Paper # | DC2012-73 |
Volume (vol) | vol.112 |
Number (no) | 362 |
Page | pp.pp.- |
#Pages | 4 |
Date of Issue |