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- Improving Indoor Positioning Via Mobile Sensing
- Qiu, Chen
- Electronic Theses & Dissertations
Accurate indoor position and movement information of devices enables numerous opportunities for location based services. Services such as guiding users through buildings, highlighting nearby services within shopping malls, or tracking the number of steps taken are some of the opportunities available when accurate positioning information is computed by devices. GPS provides accurate localization results in an outdoor environment, such as navigation information for vehicles. Unfortunately, GPS...
Show moreAccurate indoor position and movement information of devices enables numerous opportunities for location based services. Services such as guiding users through buildings, highlighting nearby services within shopping malls, or tracking the number of steps taken are some of the opportunities available when accurate positioning information is computed by devices. GPS provides accurate localization results in an outdoor environment, such as navigation information for vehicles. Unfortunately, GPS cannot be applied indoors pervasively due to the various interferences.Indoor localization has been a challenging and significant topic in recent decades. Although extensive research has been dedicated to this field, accurate indoor location information remains a challenge without the incorporation of expensive devices or sophisticated infrastructures within buildings. We explored one practical approach for indoor map construction and four representative resolutions for indoor positioning.Considering most indoor localization approaches are based upon indoor maps, we develop techniques to construct indoor maps. Since indoor maps might be dynamic and updated regularly, we present iFrame, a dynamic approach that uses mobile sensing techniques for constructing 2-dimensional indoor maps. We abstract the unknown map as a matrix and use mobile devices that incorporate three mobile sensing technologies - accelerometers to support dead reckoning, Bluetooth RSSI detection, and WiFi RSSI detection. The layouts of rooms and hallways can be constructed automatically within 5-10 minutes.Based on the indoor map, we propose four indoor localization approaches by leveraging mobile sensing techniques. First, although GPS can not help indoor localization directly, we propose iLoom, which adopts a user's motion behaviors built by GPS information to enhance indoor localization. iLoom leverages an Acceleration Range Box to improve a user's acceleration value used for computing dead reckoning. By transfer learning the information from users' motion behaviors to the Acceleration Range Box, iLoom improves the Acceleration Range Box to achieve more accurate indoor positioning results. Second, we introduce CRISP - a prototype that leverages opportunities of the interaction of multiple smartphones to enhance indoor positioning. When mobile devices cooperate and share position information iteratively, the localization accuracies of mobile devices increases gradually. In addition, based upon the obtained location information, CRISP provide a pedometer that can avoid the accumulative errors caused by accelerometers. Third, we present SilentWhistle, a mobile prototype that incorporates acoustic information and motion traces on smartphones to locate users. When users encounter each other or related beacons, by measuring the relation between sound strength and distance, the initial location information obtained by dead reckoning can be enhanced by triangulations transferred from sound strength. Centralized and distributed models of SilentWhistle can avoid the incorrect location messages spreading based on the fault tolerance property. Finally, by employing mobile robots in indoor scenarios, we propose AirLoc to improve the indoor positioning accuracies on smartphones. When a robot is near a smartphone, the robot sends accurate location information to users' smartphones via Bluetooth. In AirLoc, we design dynamic programming algorithms to generate the optimal serving routes for a single robot. Then, we extend the single robot model to multi-robot model. In our simulation, the multi-robots are organized by an unbalanced tree and serve areas by the Distance/Density First Algorithm.