Name
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 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 two approaches in our system, one is directly feature-driven and the second uses a simple Bayesian network. Extensive experiments conducted both in the laboratory as well as in two public facilities demonstrate that our system is robust to real-world environments. We show that in spite of noisy signal readings, our methods can measure service and waiting times to within a $10$ second resolution. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2594368.2594382 | MobiSys |
Keywords | Field | DocType |
miscellaneous,smartphones,human queue monitoring,received signal strength,wifi | Signal monitoring,Bottleneck,Workflow scheduling,Computer science,Queue,Real-time computing,Bayesian network,Queue management system,Embedded system | Conference |
Citations | PageRank | References |
36 | 1.95 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Wang | 1 | 811 | 40.19 |
| Jie Yang | 2 | 1605 | 83.06 |
| Yingying Chen | 3 | 2495 | 193.14 |
| Hongbo Liu | 4 | 1426 | 105.95 |
| Marco Gruteser | 5 | 4631 | 309.81 |
| Richard P. Martin | 6 | 1777 | 165.29 |