QTime: A Queuing-Time Notification System Based on Participatory Sensing Data

Yasha Wang, Jiangtao Wang, Xiaoyu Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

19 Citations (Scopus)

Abstract

People living in big cities often suffer from long queuing time waiting for checking out in supermarkets when the crowd density is high. This paper develops QTime, an application to inform queuing time in nearby supermarkets to help people make time-efficient plan about when and which store to go. QTime uses participatory sensing data collected by commodity sensors built into every-day smartphones without dependence on any pre-installed sensing hardware or software infrastructure. QTime calculates queuing time of an in-store user by detecting his/her queuing movement mode in the phone-side, and estimates the queuing time in given supermarkets by aggregating data from different users in the server-side, and notifies the users who have shopping plans through phones or webpages. Because even in a crowded supermarket, the queuing time of only a few customers can represent the majority, QTime can estimate queuing time accurately even only a few users upload data to the server. An experiment has been conducted and described to prove the validity of QTime.
Original languageEnglish
Title of host publication2013 IEEE 37th Annual Computer Software and Applications Conference
PublisherIEEE
ISBN (Print)9780769549866
DOIs
Publication statusPublished - 31 Oct 2013
Externally publishedYes
Event2013 IEEE 37th Annual Computer Software and Applications Conference - Kyoto, Japan
Duration: 22 Jul 201326 Jul 2013
Conference number: 37

Conference

Conference2013 IEEE 37th Annual Computer Software and Applications Conference
CountryJapan
CityKyoto
Period22/07/1326/07/13

Keywords

  • Pervasive Computing
  • Participatory Sensing
  • Mobile System
  • Queuing Time Estimation

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  • Cite this

    Wang, Y., Wang, J., & Zhang, X. (2013). QTime: A Queuing-Time Notification System Based on Participatory Sensing Data. In 2013 IEEE 37th Annual Computer Software and Applications Conference IEEE. https://doi.org/10.1109/COMPSAC.2013.127