Location-awareness sensing is getting increasingly important for a range of mobile and pervasive applications on nowadays smartphones. In its basic form, location-awareness sensing refers to a computing technology that incorporates information about the current environment of a mobile user to provide more relevant services to the users. This dissertation addresses two key issues that emerge from real location-awareness applications, namely object localization and micro-environment sensing from the perspective of human-centric context and device-centric perception, respectively. We explore built-in sensors on off-the-shelf handhold devices to perceive such ambient context and design two location-awareness sensing platforms: CamLoc and Sherlock. CamLoc leverages phone sensors such...[ Read more ]

Location-awareness sensing is getting increasingly important for a range of mobile and pervasive applications on nowadays smartphones. In its basic form, location-awareness sensing refers to a computing technology that incorporates information about the current environment of a mobile user to provide more relevant services to the users. This dissertation addresses two key issues that emerge from real location-awareness applications, namely object localization and micro-environment sensing from the perspective of human-centric context and device-centric perception, respectively. We explore built-in sensors on off-the-shelf handhold devices to perceive such ambient context and design two location-awareness sensing platforms: CamLoc and Sherlock. CamLoc leverages phone sensors such as accelerometer and gyroscope, together with camera to localize remote object, while Sherlock employs the calibration of various functional sensors on smartphone to perceive immediate surroundings of smartphones at centimeter level accuracy. Through long-term evaluations and real-world case studies, we verify the viability and effectiveness of the proposed systems.