Enriching location information: an energy-efficient approach

Off-the-shelf modern mobile devices come with a number of inbuilt sensors, e.g., GPS, WiFi, GSM, accelerometer, compass, gyroscope, NFC and Bluetooth. Equipped with all these sensors and internet connectivity, modern mobile phones are enabling continuous sensing and increasingly many emergent mobile applications are using sensed context on the phone to understand users' needs and improve usability. However, limited battery power is a big hindrance to the deployment of continuous sensing on mobile devices and without any intelligent sensor management, the battery lasts only few hours. In this research, we emphasize on location-awareness and address the challenges in developing ubiquitous positioning solutions, cross-device indoor localization, position and trajectory tracking and inferring high-level contexts using machine-learning techniques on sensor data in an energy-efficient way.

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