Bluetooth Low Energy (BLE) is probably the best technological tool that we can harness today for studies on close human interactions. It has a wide deployment base (i.e., on smartphones), has peer discovery as an inherent protocol feature, does not require infrastructure (e.g., satellites or base stations) to operate, and sparingly uses energy that is good for extended monitoring. In this paper, we show that we can use the BLE peer discovery capability on smartphones to detect and monitor massive and dynamic encounters, which would provide valuable insights into many epidemiological or sociological phenomena. However, being designed for more leisurely interactions, BLE needs some stretching in order to be used in large-scale operations. Specifically, the protocol design is not optimal to rapidly discover hundreds of devices in the communication range, whereas dense crowds and mass gatherings are not unrealistic in city life. Moreover, if the crowd is dynamic, discovery becomes even more time-pressed because encounters should be recorded before churn. In this paper, we push the BLE technology with the requirements to discover hundreds of devices before co-presence expires, and to work continually over a typical smartphone charge cycle. Specifically, we investigate how we should modify the BLE protocol and how we should set its protocol parameters for this purpose. We show that with the proposed changes and configurations, we can accelerate the speed of discovery for massive and dynamic crowds by more than an order of magnitude compared to the case that we naively follow the guidance of the current BLE standard.

A smart car can be defined in various dimensions. In this paper, we consider a gaze-based driver query processing system where the vehicle recognizes where the driver is looking at when she asks about roadside landmarks. By linking the tracked gaze of the driver with annotated maps and geo-location information, the system can let the vehicle provide immediate answers to the driver. We discuss the design and implementation of the system, along with the test results during real driving.