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| Content Provider | Springer Nature Link |
|---|---|
| Author | Wang, Jiangtao Wang, Yasha Zhang, Daqing Wang, Leye Chen, Chao Lee, Jae Woong He, Yuanduo |
| Copyright Year | 2017 |
| Abstract | People often have to queue for a busy service in many places around a city, and knowing the queue time can be helpful for making better activity plans to avoid long queues. Traditional solutions to the queue time monitoring are based on pre-deployed infrastructures, such as cameras and infrared sensors, which are costly and fail to deliver the queue time information to scattered citizens. This paper presents CrowdQTE, a mobile crowdsensing system, which utilizes the sensor-enhanced mobile devices and crowd human intelligence to monitor and provide real-time queue time information for various queuing scenarios. When people are waiting in a line, we utilize the accelerometer sensor data and ambient contexts to automatically detect the queueing behavior and calculate the queue time. When people are not waiting in a line, it estimates the queue time based on the information reported manually by participants. We evaluate the performance of the system with a two-week and 12-person deployment using commercially-available smartphones. The results demonstrate that CrowdQTE is effective in estimating queuing status. |
| Starting Page | 49 |
| Ending Page | 60 |
| Page Count | 12 |
| File Format | |
| ISSN | 20952228 |
| Journal | Frontiers of Computer Science in China |
| Volume Number | 11 |
| Issue Number | 1 |
| e-ISSN | 20952236 |
| Language | English |
| Publisher | Higher Education Press |
| Publisher Date | 2017-04-07 |
| Publisher Institution | Chinese Universities |
| Publisher Place | Beijing |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | mobile crowdsensing queue time estimation opportunistic and participatory sensing Computer Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Theoretical Computer Science Computer Science |
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