Autonomous Management of Everyday Places
for a Personalized Location Provider
Abstract—Currently available location technologies such as the global positioning system (GPS) or Wi-Fi ﬁngerprinting are limited, respectively, to outdoor applications or require ofﬂine signal learning. In this paper, we present a smartphone-based autonomous construction and management of a personalized location provider in indoor and outdoor environments. Our system makes use of electronic compass and accelerometer, speciﬁcally for indoor user tracking. We mainly focus on providing point of interest (POI) locations with room-level accuracy in everyday life. We present a practical tracking model to handle noisy sensors and complicated human movements with unconstrained placement. We also employ a room-level ﬁngerprint-based place-learning technique to generate logical location from the properties of pervasive Wi-Fi radio signals. The key concept is to track the physical location of a user by employing inertial sensors in the smartphone and to aggregate identical POIs by matching logical location. The proposed system does not require a priori signal training since each user incrementally constructs his/her own radio map into their daily lives. We implemented the system on Android phones and validated its practical usage in everyday life through real deployment. The extensive experimental results show that our system is indeed acceptable as a fundamental system for various mobile services on a smartphone.
Index Terms—Indoor tracking, inertial sensor, mobile sensing, place learning, smartphone.
OCATION-BASED services are increasingly important
for modern mobile devices such as the smartphone. Navigation, social network services, and sharing photos are common applications that utilize user location , . These services make use of a temporary user location that is obtained at a
certain period of time by manual request. However, emerging
mobile services require an advanced localization scheme that would provide everyday location monitoring instead of temporarily locating the user. Many advanced services are available with information on everyday location monitoring. For example, health monitoring system utilizes a user’s location to estimate the physical state of elderly person or patients . The system reports daily momentum to improve their health. Another example
Manuscript received September 2, 2010; revised March 8, 2011; accepted March 14, 2011. Date of publication April 21, 2011; date of current version June 13, 2012. This work was supported by the National Research Foundation of Korea, funded by the Korean Government, Ministry of Education, Science, and Technology, under Grant 2010-0000405. This paper was recommended by Associate Editor B. Chaib-draa.
The authors are with the Department of Computer Science, Yonsei University, Seoul 120-749, Korea (e-mail: firstname.lastname@example.org; elmurod@cs. yonsei.ac.kr; email@example.com).
Color versions of one or more of the ﬁgures in this paper are available online at http://ieeexplore.ieee.org.
Digital Object Identiﬁer 10.1109/TSMCC.2011.2131129
is an environment-related application that estimates the user’s environmental impact and exposure . People learn how their lives affect the environment and subsequently alter their behavior to protect environment. Smart-home system determines the location of residents and recognizes living patterns to provide appropriate services , . Life-logging has been proposed to visualize life patterns or to provide an automatically generated life diary . Works in  and  also propose useful services that enable users to search for their lost mobile devices. The service uses daily location information of a user to track down lost phones or items. To support these kinds of emerging applications, efﬁcient and accurate location monitoring is essential. However, currently available location technologies cannot...
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