Low-cost and accurate indoor location information can add spatiotemporal context to information systems, enabling new location-aware applications. Time-of-Flight (ToF)-based acoustic localization using speakers and microphones allows for localization accuracy within a few tens of centimeters, outperforming RF-based techniques. However, ToF-based localization requires synchronization between the speaker and microphone, i.e., the time offset between them must be known. Previous time offset estimation methods required custom hardware for speakers, limiting their practical use. Estimating the time offset using a single, unmodified speaker is essential for leveraging widely deployed speakers and enhancing coverage. This paper presents the first method for time offset estimation using a single speaker and a microphone, enabled by two key factors: (i) a time offset computation method that utilizes higher-order floor-ceiling reflections as multiple geometrically-constrained virtual speakers, and (ii) a signal processing pipeline that isolates these critical reflections from numerous others by leveraging the speaker's frequency-dependent radiation pattern. Experiments show that the proposed technique can achieve time offset estimation with a 90th percentile error of 259 μs at a 5 m distance. Furthermore, we implemented a ToF localization system based on SyncEcho, demonstrating a 11.0 cm localization accuracy with a 90th percentile error.

Users and operators of shared spaces must ensure safety in such areas to prevent the spread of COVID-19. Although each organization has operated a variety of safety-related systems, including contact tracing, congestion monitoring, and check-in services, it is unclear what elements, such as privacy protection level, benefits, and permission procedures, have promoted the usage of these systems. In this study, we created MOCHA, a platform for sharing and tracking room-level locations. This platform automatically detects visited places by scanning Bluetooth beacons in each room using smartphones and shares location data according to predefined user settings. The collected data is used for room-level contact tracing, congestion monitoring, and reservation services. According to >6,500 users' usage data for a year in a university, outlining the advantages of utilizing the app encouraged people to install the app, and reinforced connections in small private groups are encouraged to use the app continuously.

When customers wait their turn to place an order at a street vendor, they often form a spontaneous queue. Due to the presence of passers-by and people standing outside the queue, it is more difficult than one might think to distinguish between those in the queue and those not in the queue. In this paper, we consider a method that uses acoustic ranging to autonomously detect who is in line and in which order, under the condition that all customers have smartphones. The proposed method is unique in that it can distinguish whether a newly arrived user has joined the end of the queue or not by taking cues from the geometric properties of the queue. Our preparatory queueing simulations confirm that 92.5% of the queuers are estimated correctly.