首页    期刊浏览 2024年11月24日 星期日
登录注册

文章基本信息

  • 标题:Classroom Attendance Systems Based on Bluetooth Low Energy Indoor Positioning Technology for Smart Campus
  • 本地全文:下载
  • 作者:Apiruk Puckdeevongs ; N. K. Tripathi ; Apichon Witayangkurn
  • 期刊名称:Information
  • 电子版ISSN:2078-2489
  • 出版年度:2020
  • 卷号:11
  • 期号:6
  • 页码:329-346
  • DOI:10.3390/info11060329
  • 出版社:MDPI Publishing
  • 摘要:Student attendance during classroom hours is important, because it impacts the academic performance of students. Consequently, several universities impose a minimum attendance percentage criterion for students to be allowed to attend examinations; therefore, recording student attendance is a vital task. Conventional methods for recording student attendance in the classroom, such as roll-call and sign-in, are an inefficient use of instruction time and only increase teachers’ workloads. In this study, we propose a Bluetooth Low Energy-based student positioning framework for automatically recording student attendance in classrooms. The proposed architecture consists of two components, an indoor positioning framework within the classroom and student attendance registration. Experimental studies using our method show that the Received Signal Strength Indicator fingerprinting technique that is used in indoor scenarios can achieve satisfactory positioning accuracy, even in a classroom environment with typically high signal interference. We intentionally focused on designing a basic system with simple indoor devices based on ubiquitous Bluetooth technology and integrating an attendance system with computational techniques in order to minimize operational costs and complications. The proposed system is tested and demonstrated to be usable in a real classroom environment at Rangsit University, Thailand.
  • 关键词:smart attendance; Bluetooth Low Energy; indoor positioning; Received Signal Strength Indicator (RSSI); fingerprinting; neural network smart attendance ; Bluetooth Low Energy ; indoor positioning ; Received Signal Strength Indicator (RSSI) ; fingerprinting ; neural network
国家哲学社会科学文献中心版权所有