Location Found
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.
I. INTRODUCTION
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 [1], [2]. These services make use of a temporary user location that is obtained at a certain period of time by manual request. However, emerging
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.
I. INTRODUCTION
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 [1], [2]. These services make use of a temporary user location that is obtained at a certain period of time by manual request. However, emerging
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