Abstract
We investigate using smartphone WiFi signals to track human queues, which are common in many business areas such as retail stores, airports, and theme parks. Real-time monitoring of such queues would enable a wealth of new applications, such as bottleneck analysis, shift assignments, and dynamic workflow scheduling. We take a minimum infrastructure approach and thus utilize a single monitor placed close to the service area along with transmitting phones. Our strategy extracts unique features embedded in the signal traces to infer the critical time points when a person reaches the head of the queue and finishes service, and from these inferences we derive a person's waiting and service times. We develop a feature driven approach in our system. Extensive experiments conducted both in the laboratory demonstrate that our system is robust to queues with different waiting time. We show that in spite of noisy signal readings, our methods can measure important time periods in queue (e.g., service and waiting times) to within a $10$ second resolution.