Personal mobile devices such as cellular phones, smart phones and PMPs have advanced incredibly in the past decade. The mobile technologies make research on the life log and user-context awareness feasible. In other words, sensors in mobile devices can collect the variety of user's information, and various works have been conducted using that information. Most of works used a user's location information as the most useful clue to recognize the user context. However, the location information in the conventional works usually depends on a GPS receiver that has limited function, because it cannot localize a person in a building and thus lowers the performance of the user-context awareness. This paper develops a system to solve such problems and to infer a user's hidden information more accurately using Bayesian network and indoor-location information. Also, this paper presents a new technique for localization in a building using a decision tree and signals for the Wireless LAN because the decision tree has many advantages which outweigh other localization techniques.
%0 Journal Article
%1 noauthororeditor
%A Noh, Hyun-Yong
%A Lee, Jin-Hyung
%A Oh, Sae-Won
%A Hwang, Keum-Sung
%A Cho, Sung-Bae
%D 2012
%J Information Processing and Management
%K information informationretrieval mobile retrieval search
%N 1
%P 1-12
%T Exploiting indoor location and mobile information for context-awareness service
%V 48
%X Personal mobile devices such as cellular phones, smart phones and PMPs have advanced incredibly in the past decade. The mobile technologies make research on the life log and user-context awareness feasible. In other words, sensors in mobile devices can collect the variety of user's information, and various works have been conducted using that information. Most of works used a user's location information as the most useful clue to recognize the user context. However, the location information in the conventional works usually depends on a GPS receiver that has limited function, because it cannot localize a person in a building and thus lowers the performance of the user-context awareness. This paper develops a system to solve such problems and to infer a user's hidden information more accurately using Bayesian network and indoor-location information. Also, this paper presents a new technique for localization in a building using a decision tree and signals for the Wireless LAN because the decision tree has many advantages which outweigh other localization techniques.
@article{noauthororeditor,
abstract = {Personal mobile devices such as cellular phones, smart phones and PMPs have advanced incredibly in the past decade. The mobile technologies make research on the life log and user-context awareness feasible. In other words, sensors in mobile devices can collect the variety of user's information, and various works have been conducted using that information. Most of works used a user's location information as the most useful clue to recognize the user context. However, the location information in the conventional works usually depends on a GPS receiver that has limited function, because it cannot localize a person in a building and thus lowers the performance of the user-context awareness. This paper develops a system to solve such problems and to infer a user's hidden information more accurately using Bayesian network and indoor-location information. Also, this paper presents a new technique for localization in a building using a decision tree and signals for the Wireless LAN because the decision tree has many advantages which outweigh other localization techniques. },
added-at = {2012-06-16T20:01:41.000+0200},
author = {Noh, Hyun-Yong and Lee, Jin-Hyung and Oh, Sae-Won and Hwang, Keum-Sung and Cho, Sung-Bae},
biburl = {https://www.bibsonomy.org/bibtex/28e26466f0feda66d6c8d2a1a6273408d/nadinepietras},
interhash = {b6d6c97e149506e06fc78758854451c1},
intrahash = {8e26466f0feda66d6c8d2a1a6273408d},
journal = {Information Processing and Management},
keywords = {information informationretrieval mobile retrieval search},
month = {January},
number = 1,
pages = {1-12},
timestamp = {2012-06-16T20:01:41.000+0200},
title = {Exploiting indoor location and mobile information for context-awareness service},
volume = 48,
year = 2012
}