Cross-device interaction is becoming an increasingly hot topic as we often have multiple devices at our immediate disposal in this era of mobile computing. Various cross-device applications such as file sharing, multi-screen display, and cross-device authentication have been proposed and investigated. However, one of the most fundamental enablers remains unsolved: How to achieve ubiquitous multi-device localization? Though pioneer efforts have resorted to gesture-assisted or sensing-assisted localization, they either require extensive user participation or impose some strong assumptions on device sensing abilities. This introduces extra costs and constraints, and thus degrades their practicality. To overcome these limitations, we propose TUM, an acoustic-assisted localization scheme Towards Ubiquitous Multi-device localization. The basic idea of TUM is to utilize the dual-microphones and speakers to obtain distance cues among devices. At the same time it resolves the location ambiguity with the help of MEMS sensors. We devise techniques for distance constraint extraction, static localization, continuous localization, and multi-device localization, and build a prototype that runs on commodity devices. Extensive experiments show that TUM provides a real-time 3D relative localization service under 10cm mean error for both static and continuous localization.

Indoor localization is of great importance to a wide range of applications in shopping malls, office buildings and public places. The maturity of computer vision (CV) techniques and the ubiquity of smartphone cameras hold promise for offering sub-meter accuracy localization services. However, pure CV-based solutions usually involve hundreds of photos and pre-calibration to construct image database, a labor-intensive overhead for practical deployment. We present ClickLoc, an accurate, easy-to-deploy, sensor-enriched, image-based indoor localization system. With core techniques rooted in semantic information extraction and optimization-based sensor data fusion, ClickLoc is able to bootstrap with few images. Leveraging sensor-enriched photos, ClickLoc also enables user localization with a single photo of the surrounding place of interest (POI) with high accuracy and short delay. Incorporating multi-modal localization with Manifold Alignment and Trapezoid Representation, ClickLoc not only localizes efficiently, but also provides image-assisted navigation. Extensive experiments in various environments show that the 80-percentile error is within 0.26m for POIs on the floor plan, which sheds light on sub-meter level indoor localization.

In this work, we propose RAD, a RApid Deployment localization framework without human sampling. The basic idea of RAD is to automatically generate a fingerprint database through space partition, of which each cell is fingerprinted by its maximum influence APs. Based on this robust location indicator, fine-grained localization can be achieved by a discretized particle filter utilizing sensor data fusion. We devise techniques for CIVD-based field division, graph-based particle filter, EM-based individual character learning, and build a prototype that runs on commodity devices. Extensive experiments show that RAD provides a comparable performance to the state-of-the-art RSS-based methods while relieving it of prior human participation.

Indoor localization is of great importance to a wide range of applications in the era of mobile computing. Current mainstream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer locations. However, those methods suffer from fingerprint ambiguity that roots in multipath fading and temporal dynamics of wireless signals. Though pioneer efforts have resorted to motion-assisted or peer-assisted localization, they neither work in real time nor work without the help of peer users, which introduces extra costs and constraints, and thus degrades their practicality. To get over these limitations, we propose Argus, an image-assisted localization system for mobile devices. The basic idea of Argus is to extract geometric constraints from crowdsourced photos, and to reduce fingerprint ambiguity by mapping the constraints jointly against the fingerprint space. We devise techniques for photo selection, geometric constraint extraction, joint location estimation, and build a prototype that runs on commodity phones. Extensive experiments show that Argus triples the localization accuracy of classic RSS-based method, in time no longer than normal WiFi scanning, with negligible energy consumption.