Journal
FRONTIERS OF COMPUTER SCIENCE
Volume 11, Issue 1, Pages 49-60Publisher
HIGHER EDUCATION PRESS
DOI: 10.1007/s11704-016-5553-z
Keywords
mobile crowdsensing; queue time estimation; opportunistic and participatory sensing
Categories
Funding
- National Natural Science Foundation of China [61572048]
- China Electric Power Research Institute [JS71-16-005]
- Microsoft
- Fundamental Research Funds for the Central Universities [106112015CDJXY180001]
- Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University, China)
- Chongqing Basic and Frontier Research Program [cstc2015jcyjA00016]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available