KR20170091879A - A Tool for Checking Attendance of Students in Classroom Automatically - Google Patents

Abstract

We design and use a software tool capable of automatically checking the attendance of an enrolled student by using a smartphone. The student needs to install the application on his/her smartphone. The challenging issue of the software tool is that we need to automatically find out if a student is inside or outside the classroom. For this purpose, the application of the smartphone of the student will measure the current signal strength of Wi-Fi and Bluetooth devices near the classroom. The measurement results are reported to a server running software to check the attendance of the student. The server can analyze the signal strengths to determine whether the student is inside or outside the classroom. A comprehensive measurement of the strength of WiFi and Bluetooth signals shows that WiFi scanning of the smartphone consumes more energy than BLE scanning. Also, the received signal of a Wi-Fi beacon can change dynamically due to interference and fading. In other words, we are not able to find out the location of the student in the classroom by the strength of the Wi-Fi signal. If we can place a Bluetooth device, we can adjust a transmission power level. This helps to improve the accuracy of the location measurement of the student. By leveraging the Bluetooth device, we are able to perform highly advanced location measurement.

Description

A Tool for Checking Attendance of Students in a Classroom Automatically.

The present invention relates to a tool for automatically confirming attendance of a student in a classroom.

The user's location is one of the most essential context data for providing intelligent services called location based services (LBS) such as navigation, healthcare, marketing, and the like. Due to the recent proliferation of smartphones, the demand for and usage of location-based services is increasing. However, in the case of the indoor space where the GPS signal can not be used, an alternative technique for estimating the position of the user is required. Currently, smartphones are equipped with various sensors such as Wi-Fi and Bluetooth, and inertial sensors such as accelerometers and magnetometers. Efforts have therefore been made to design effective indoor positioning systems and services by utilizing these sensors.

Location-based services are also required in the education market. Most students have smartphones and spend most of their time in indoor space (for example, on-campus buildings or classrooms), so there is an opportunity to provide personalized intelligent education services based on their location information do. There are multi-location location-based service solutions such as navigation systems and location-aware healthcare solutions, but location-based services that are specific to on-campus locations are hard to find. With this in mind, the present inventor intends to invent a software tool for confirming attendance of students.

The present invention is provided to ensure that the user is inside or outside the classroom.

In order to achieve the above object, the present invention provides a method for detecting a terminal in a mobile communication system. The method includes receiving a first signal from a first device; Receiving a second signal from a second device; Determining differences in received signal strength (RSS) values from the first device and the second device; And identifying the terminal based on the determination.

By using the proposed method, it is possible to grasp whether a user having a smartphone is inside or outside the classroom.

FIG. 1 is a diagram showing energy level levels of a smartphone according to time when either the Bluetooth (BLE) or the WiFi interface is turned on for scanning purposes.
2 is a diagram showing received signal strength (RSS) values of access points that change over time when three access points (APs) are turned on at 0 minute, 5 minute, and 10 minute, respectively.
3 is a plan view showing the target space, including the location of the measurement points and the position of the Bluetooth (BLE) beacon.
FIG. 4 is a diagram showing received signal strength (RSS) values of transmit power specific Bluetooth (BLE) signals averaged over the inside and outside of the classroom (denoted as _in and _out, respectively).
5 is a diagram showing the number of beacon frames observed at each position when the transmission power is set to 0 dBm.
6 is a plan view showing locations of two Bluetooth (BLE) beacons and target doors.
FIG. 7 is a diagram showing changes in received signal strength (RSS) values of internal and external beacons when a student is entering or exiting.
FIG. 8 is a diagram showing an attendance confirmation tool of the present invention based on sensors such as a Bluetooth (BLE) and an accelerometer capable of calculating whether a student is moving and re-entering the room.
Figure 9 shows a repetitive pattern of acceleration magnitudes while a student is walking and shows a change in standard deviation in an idle mode and a walking mode.
Figure 10 is a diagram showing three paths leading to the classroom and one experimental fake path.
11 is an exemplary diagram illustrating changes in the number of beacons and the difference in RSS signal strength measured at intervals of 10 seconds when a student enters or leaves the room.
12 is a diagram showing the received signal strength (RSS) and the number of received beacons when the participating student walks up and down the false path (shown on the left and right sides, respectively).

Geofencing using Bluetooth technology

The present invention provides a software tool for identifying student attendance in the classroom. This is referred to as " geofencing " which means that the user can see whether it is inside or outside the area of interest. To implement this tool, one of the wireless communication interfaces of the smartphone is used to establish a virtual barrier representing the boundaries of the area of interest. The received signal strength (RSS) of the wireless signal is weaker as the distance between the student and the reference node (e.g., Wi-Fi access point, Bluetooth beacon) increases. If the distance exceeds a predetermined threshold, the signal may not be detected at all. Therefore, "geofencing" can be performed around the classroom by measuring the received signal strength of the radio signal, and the geofencing range can be controlled by adjusting the signal transmission power of the reference node.

In general, recent smartphones have a number of wireless interfaces such as cellular (3G or LTE), WiFi, Bluetooth low energy (BLE), and Near Field Communication (NFC). Since these technologies have different characteristics, it may be necessary to use multiple interfaces for geofencing. In the case of a cellular network interface, the precise location of the base station (as a reference node) is unknown and the associated information such as transmit power is not easily accessible. Also, in the case of NFC, the communication range is too short to be used for geofencing. So, for geofencing purposes, we exclude these two techniques.

For Wi-Fi, Wi-Fi signals have several drawbacks compared to Bluetooth signals. First, Wi-Fi consumes energy faster than Bluetooth because of scanning. Figure 1 shows how quickly the energy balance of a smartphone decreases as it continuously collects beacon frames of access points (APs) to obtain received signal strength values. Wi-Fi scanning consumes 1.5 times more energy than Bluetooth scanning and consumes nearly twice as much energy as a "pure" scenario where smartphones do not scan. This result is due to the difference in number of communication channels between WiFi and Bluetooth that the smartphone should scan. While the Bluetooth specification has only three advertising channels, the smartphone needs to scan at least 13 Wi-Fi channels. This limitation also affects the time required for Wi-Fi scanning. In most smartphones, Wi-Fi scanning takes about 3-4 seconds, while Bluetooth scanning can take less than a second.

Moreover, most Wi-Fi access point providers employ dynamic transmission power control algorithms to avoid interference between access points. 2 shows a change in the received signal strength values of the access points when the adjacent three access points are successively turned on successively at five-minute intervals. The result shows that the transmit power of the access point can be changed dynamically without manual adjustment. This is why you can not rely on WiFi to design a geofencing mechanism. In addition, the Apple iPhone does not officially support the application programming interface (API) for Wi-Fi scanning operations. That is, the received signal strength values of the Wi-Fi access points can not be obtained with the iPhone. Eventually, we use Bluetooth signals to design the geofencing system.

Bluetooth signal measurement

This section describes comprehensive measurement of Bluetooth signal. This provides the basis for designing classroom attendance verification systems. In order to collect data, a proper sized classroom (9m to 12m) in the school building is selected as the target space, and nine measurement positions are selected around the classroom as shown in Fig. Then, a normal class 2 Bluetooth beacon having an appropriate communication range is arranged, and the received signal strength values are collected for 1 minute at each position. To minimize fading and the effects of the human body by minimizing the shaded area of the Bluetooth signal, the placement of the Bluetooth beacon is centered on the ceiling of the room (indicated by the red dot).

Basically, as the distance from the Bluetooth beacon increases, the Bluetooth signal can not be detected. Also, the intensity of the signal is degraded by the attenuation of the wall. Based on this observation, we can estimate whether the student (or the student's smartphone) is located inside or outside the classroom by observing changes in the received signal strength. It is therefore an important step to find the optimal transmit power level that can reach the entire area of the classroom. To find the optimal transmit power, the received signal strength values are collected from inside the classroom (points 0-4 in FIG. 3) and outside (points 5 through 8 in FIG. 3) while varying the transmit power. In addition, it adopts non-line-of-sight (NLOS) and line-of-sight (LOS) to observe the human body's influence on the received signal strength of Bluetooth. In non-sight areas, the body of a person is between a beacon and a smartphone while collecting. The available transmit power range for Class 2 Bluetooth beacons is between -23 dBm and 4 dBm, and the beacon advertising rate is 1 Hz.

Figure 4 shows the average received signal strength in each case. Each mark on the X-axis means (i) one of the adjusted transmit powers and (ii) whether the measurement location is inside or outside the classroom. When the transmission power is -23 dBm (minimum transmission power), the received beacon strength is too weak, so no Bluetooth beacon frames are observed in nonvisual locations. This means that if the classroom is crowded or if the student is in the pocket with the smartphone, attendance may not be confirmed even though the student is in the classroom. We define this situation as a false negative case, and -23 dBm is too weak to avoid false negatives. For 4 dBm (maximum transmit power), the received signal strengths are clearly observed at all positions. However, even when the smartphone is located in nonvisual locations, Bluetooth beacon frames will reach outside the classroom, which will negatively impact geofencing performance. In such a situation, a false positive case occurs, which means that the student is confirmed to be present despite being outside the classroom.

As a result, it is necessary to find the proper transmit power of the Bluetooth device to avoid both the false negative state and the false positive state. It has been found that setting the transmit power to 0 dBm can reduce both false negatives and false positives. At 0 dBm, although beacon frames can reach out-of-class students at line-of-sight locations, they will not be detected as soon as the distance to the beacon increases.

As described above, since the received signal strength of the Bluetooth beacon is unstable, the number of observed beacon frames at each position is measured as shown in FIG. Figure 5 shows the number of beacon frames observed for a time of one minute at each location. Considering the worst case (position number 2) in which the number of beacon frames observed in one minute is 35, if the ratio of receiving beacon frames exceeds 0.5, it may be considered that the student is likely to be inside the classroom.

The next step is to solve the false positive problems that can occur in the line of sight outside the classroom. Confirming attendance based solely on the percentage of bluetooth beacons that are observed only when a student is located next to the wall outside the classroom or when placing the smartphone in a line-of-sight environment is at risk. To solve this problem, an entrance detector based on an infrared sensor is usually used. The entrance detector can count the number of people in the classroom by detecting people's entry and exit. A similar process can be performed using a Bluetooth beacon rather than an infrared sensor.

In order to detect the entrance and exit of a student through the door of the classroom, another Bluetooth beacon may be installed at an appropriate place outside the classroom and the difference in received signal strength values from the inner and outer beacons may be tracked. As shown in the above equation, the difference in the received signal intensities is calculated by subtracting the received signal strength of the external beacon from the received signal strength of the internal beacon. If the difference is increased (> 0), the student is deemed to have entered, and if the difference is decreased (less than 0), the student is deemed to have left.

The proper location of a Bluetooth beacon outside the classroom is the point that contains all possible paths to the door. Also, the distance between two beacons and the door is so close that when passing through the door, the difference in received signal strength should be close to zero. Therefore, in the experiment of the present invention, the out-of-classroom beacon is located as shown in FIG.

By setting the transmission power of the beacon outside the classroom to 0 dBm, it is possible to take charge of an area of a size similar to that of the beacon in the classroom. The beacon frames are received by the smartphone at different points in time, since the beacon advertisement time of each beacon is not consistent and their signals undergo fading independently. To solve this problem, an average difference between the received signal strengths of the frames of the internal beacons and the received signal strengths of the frames of the external beacons are calculated during the last five seconds. FIG. 7 shows the change in the received signal intensity difference when entering or exiting through the door, and the black vertical line means the time when the student passes through the door. The difference in received signal strength shows the increase / decrease patterns clearly when the student enters or exits through the door, and becomes almost zero as the student passes through the door.

System design

This section describes the overall system design and detailed algorithms of attendance verification systems.

Figure 8 shows the building blocks of the proposed attendance confirmation system. Based on the insights from the previous measurements, two components for identifying attendance are designed: an internal detector and an entrance / exit detector. The internal detector determines whether the student is inside or outside the classroom based on the ratio of the observed beacon frames, and the entrance / exit detector detects that the student enters or exits through the door based on the received signal strength difference.

In the case of an internal detector, if the number (or ratio) of observed beacon frames exceeds the threshold at a given time interval, the student is considered to be inside the classroom. Based on the previous measurement, this threshold is defined as 0.5 per second, and the length of the measurement interval is set to 10 seconds. Therefore, the threshold is 5. That is, if the number of beacon frames observed over the last 10 seconds exceeds 5, the internal detector assumes that the student is in the classroom. The appropriate length of the time interval can be determined from extensive experiments.

For access detection, two states are defined based on the difference in observed received signal strength. If the received signal strength difference is greater than 0, it is an 'internal' state, and if the difference is less than 0, it is an 'external' state. Basically, the entry / exit detector estimates entry and exit based on whether a state transition occurs between two states (internal or external). However, due to unstable received signal strength fluctuations, state transitions around the door can occur frequently. Thus, the present invention introduces a state holding threshold, which means the minimum holding time for determining either of two states to avoid false state determinations. That is, the state transition occurs only when the holding time for the new state exceeds the state holding threshold. From comprehensive experiments, the state holding threshold is set to 4 seconds. Note that due to the limited range of communication of the two beacons, the difference in received signal strength can only be observed around the door, the observation time is usually between 8 and 10 seconds before and after passing through the door at the appropriate walking speed. Thus, a state hold threshold of 4 seconds is appropriate since it may include half of the state sojourn time. In order to smooth the pattern, a lower pass filter is applied to the received signal intensity difference.

Finally, attendance is allowed only if both the internal detector and the entrance / exit detector indicate that the student is inside the classroom, and attendance is not recognized if at least one of the detectors indicates that the student is outside the classroom.

The walking mode estimator is an auxiliary component of the system. If your smartphone continuously scans for Bluetooth signals, your smartphone's battery will soon be exhausted. Thus, a smartphone app to confirm attendance will be activated periodically (eg, every 5 minutes) during a student's class time. Class hours may be recorded by the student or obtained from the school information infrastructure. In addition, the present invention utilizes a pedestrian dead reckoning (PDR) mechanism that estimates a student's gait mode based on (i) the detection of a gait and (ii) an estimate of the direction of travel, It works only when the verification app is activated. The app stops Bluetooth scanning and switches to a sleep mode for energy saving purposes. This dormant process assumes that the student does not generally move during the class.

To detect the walking mode, the present invention adopts a stepped detection module as proposed in the ZEE. The left graph of FIG. 9 shows a repetitive pattern of smartphone accelerometer sizes during walking. The right graph is a standard deviation pattern of the accelerometer size in idle mode and walking mode. ZEE uses both repetitive pattern detection and standard deviation monitoring to detect the walking mode. The unit of the Y axis is normalized by the gravitational acceleration.

Execution

Run the Android app to perform the evaluation of the proposed method. The app operates all the components: a walking mode estimator, a Bluetooth scanning unit, an internal detector, an entrance / exit detector, and an attendance confirmation unit. In the future, a back-end server will be run in a centralized manner to confirm the attendance of all students. Except for the Bluetooth scanning and gait mode estimator, all components associated with the attendance verification algorithm will be moved to the back-end server.

evaluation

To evaluate the system, the app was tested on three paths to the classroom and one false path along the corridor around the classroom. For the three real paths, the experimental student holding the smartphone in his hand moves along the paths as shown in FIG. 10 and stays in the classroom for 15-20 seconds. In the case of a fake route, the student can not enter the classroom. The reverse paths are also experimented.

Overall, in all three real paths, students are expected to attend school successfully when they enter the classroom, and attendance is not allowed in all cases of leaving. For example, the graph on the left of FIG. 11 shows the change in received signal intensity difference when entering the classroom along Route 1 and the number of beacons observed for 10 seconds. The right graph shows the measured values when leaving the classroom along Route 1 (in the reverse direction). The black vertical line represents the moment when the participant passes through the door and the black horizontal line represents the algorithm threshold for the internal detector and the entrance / exit detector.

From the experimental results, it can be seen that (i) the internal detector detects the entrance of the student before the actual entrance time, and (ii) the internal detector detects the exit at the exit of the student. This can be explained by two facts: (i) the conservative (i.e. high) transmission power of the internal beacon, and (ii) the low level of the internal detector, taking into account the worst case Threshold. Adopting a sufficiently low threshold for the ratio of received beacons, as shown in the previous measurements, and adopting a sufficiently high communication range of the internal beacon to handle the nonvisualities of the students (or to prevent false negative conditions). According to previous experiments, the time difference in which the internal detector detects the movement of the student is 6 to 7 seconds from the moment the door passes. The detection delay of the entrance / exit detector is shorter and this detection is more accurate than the internal detector. However, it is still necessary to use an internal detector for many cases, such as when a student arrives very early in the classroom.

When a student enters the classroom, the internal detector detects the entrance of the student before the actual transit time. However, attendance is recognized only if both the internal detector and the entrance / exit detector are within the classroom. Therefore, the attendance confirmation time generally coincides with the detection time of the entrance / exit detector. Even when the student leaves the system, the system operates similarly to the case of the entrance. If any one of the two detectors is considered to be outside the classroom, attendance is not recognized and the attendance confirmation time will match the detection time of the entrance / exit detector. Despite the state holding threshold of the entrance / exit detector, in all cases the attendance confirmation time is within 5 seconds of the door passing moment.

Figure 12 shows the measurement data of the smartphone when traveling along a false path. In the case of the upward direction, since the number of observed beacons is equal to or higher than the threshold (threshold is 5), the internal detector determines that the student is inside from 14 to 20 seconds. As shown in FIG. 10, if the student moves in the upward direction, the student's smartphone may be located at the line of sight to the internal beacon when the student approaches the classroom. The internal detector therefore reports false positives at some points. Attendance is not accepted due to the entrance / exit detector. This proves that the system of the present invention can effectively distinguish false positive states by utilizing an entrance / exit detector.

related research

In recent years, there has been intensive research on the range - based positioning method of finding the user 's position by calculating the distance to the reference node. Of the range-based positioning schemes, one approach is a received signal strength based approach that utilizes the fact that the received signal strength decreases as the distance increases. Research has been conducted on a received signal strength based approach by various wireless technologies including Wi-Fi, Wireless Sensor Network (WSN), Bluetooth, and Acoustic signal.

Another approach is to use time information to compute the distance. These types of schemes rely on time-related information such as Time-of-Departure (ToD) and Time-of-Arrival (ToA) to estimate the distance. For example, a time-of-flight (ToF) approach secures the propagation time of a radio signal between two entities in order to estimate the distance between a client and an access point (AP).

The geofencing scheme described herein is somewhat different from range-based positioning. The range based positioning scheme attempts to accurately estimate the user's location, while the geofencing attempts to distinguish the user location within the region of interest at a low level. Geofencing does not need to know the exact location of a student, but it needs to know if the student is in the area of interest.

Conclusion and follow-up study

In this specification, a smartphone software tool for automatically confirming student attendance has been proposed. The present inventor has designed a Bluetooth-based geofencing system that grasps whether a student's smartphone is inside or outside the classroom. A comprehensive measurement of Bluetooth shows that the observation of beacon frames from internal beacons is not sufficient. The key idea of the present invention is to introduce an entrance / exit detector based on the difference between the smartphone received signal strength values of the two Bluetooth beacons. To evaluate the system, the inventor executed the Android app and performed well within the campus building environment. As a follow-up study, the inventor will first try to perform experiments using multiple entrance / exit detectors and try to improve the energy efficiency of the proposed approach. In the future, we plan to generalize solutions for classrooms and smartphones of various sizes.

references

a. Munson, Jonathan P., and Vineet K. Gupta. "Location-based notification as a general-purpose service." Proceedings of the 2nd international workshop on mobile commerce. ACM, 2002.

b. Cheng, Long, et al. "A survey of localization in wireless sensor network." International Journal of Distributed Sensor Networks 2012 (2012).

Claims (1)

A method for detecting a terminal in a mobile communication system,
Receiving a first signal from a first device;
Receiving a second signal from a second device;
Determining differences in received signal strength (RSS) values from the first device and the second device; And
Identifying the terminal based on the determination;
≪ / RTI >

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