The design, development and evaluation of a smart attendance tracking system using bluetooth low energy beacons

The Bluetooth Low Energy (BLE) signal is a wireless personal area network device that periodically broadcasts the signals of a BLE advertising. Smartphone devices take these signals and are used to locate users. This process will allow the provision of context-aware information to users' mobile devices and link the online virtual world to the online physical world. The main purpose of this study is to develop a system that allows students to register their participation in classes using BLE devices and implement this system on a university campus. This research is aimed to reach a set of key targets, including (1) The development of a system capable of polling students by using BLE beacons as an indoor positioning system; (2) Reduce the time for attendance registration; (3) To use more than one BLE device in parallel for students' attendance tracking. This study is also an attempt to understand the potential of using BLE beacons in educational institutions and the challenges in using BLE beacons. In this study, different topologies were designed to evaluate the accuracy of the measured powers from iBeacon devices, and a different number of iBeacon devices were used to do this. Based on measured power levels for iBeacon devices in the class using decision trees and random forest classifiers in each topology, the correctness of estimating the positions of the students were assessed. Power levels were achieved using software developed specifically for Apple iPhone devices; where three measurements were recorded for each location in the class to minimize the error. The results of this study show that students who are positioned close to the classroom walls have the highest probability of failure to register. Moreover, students outside the class can participate in the attendance without being in the classroom. For this reason, these regions are considered to be critical regions, and the signal powers are collected with a half meter resolution to increase the density of the collected measurements in these regions. To provide a useful method, it is essential to distinguish students who are physically located in the class from those who are not; so that the student should not be able to register for participation unless they are actually in the class. For this purpose, closed-field positioning technology based on Bluetooth Low Energy (BLE) devices is used in this study. Different distributions of BLE devices have been evaluated in this study to recommend a topology where students from outside the classroom are never allowed to participate in attendance. In this study, it was found that at least four BLE devices were required for each class to achieve 100% accuracy using random forest classifiers to classify students in and out of the classroom.