## 3 Beacon-Based Hazard Alarm System Design
### Detailed Design of the Components
#### 3.3.1 Beacon-Based Distance Monitoring
If the hazard alarm was not received until reaching the entrance of the room, the result was recorded as (0,0). ## 5 Results

The collected data (i.e., locations where the experimenter received the first alarm notification, expressed as x and y coordinates) were then visualized in heat map-style 2D graphs. In this process, a data analysis and graphing software program, Origin, was utilized, and specifically, a 2D kernel density algorithm in this software was used to create the heat map graphs, as shown in Figure 6. This algorithm uses existing data points to estimate the possibility of unknown data points falling in a certain local position. In this type of graph, a higher density value means that the statistical probability of the function (in the context of this research, the alarm notification) occurring at that location is higher. As the experiment site was modeled as a 2D plane with XY grids, the 2D kernel density results could be integrated with the site map, showing the distribution of the locations where the first alarm notification was received in each trial. Figure 6: Heat map of hazard alarm reception locations (when the BLE signal generating beacon sensor was deployed at Location 1 (**a**), Location 2 (**b**), Location 3 (**c**) and Location 4 (**d**)). Figure 6 shows the heat map of the locations where the user received a hazard alarm notification on the mobile phone for the first time in each of the four sensor deployment cases. The denser the notification locations, the darker the color on the heat map. When the beacon signal-sending sensor was placed at the entrance of the room, the results show that the notifications were received many footsteps before, as shown in Figure 6a. The longest distance between the beacon sensor and the notification point was approximately 10 m. Therefore, the area of the notification points was the widest in this setting. Additionally, the rate of successful alarm notification (meaning that the user received the notification before the user arrived at the entrance of the room) was 89%. When the beacon sensor was placed behind the front side wall close to the entrance, the notifications were received mostly when the user was near the entrance, as shown in Figure 6b. The rate of successful alarm notification was 68% in this case. Figure 6c,d shows that the rate of successful alarm notification decreased dramatically when the beacon sensor was placed on the back side wall, meaning that many times, the user did not receive a hazard alarm notification until arriving at the entrance of the workspace. The rate of successful alarm notification was only 48% and 19% when the beacon sensor was located at positions 3 and 4, respectively. In these cases, the hazard alarm notifications were received only when the user arrived at the entrance of the room or even after the user entered the room, except for a few alarm notifications received early enough when the user approached by following walking route 5, in which there was no obstacle in the straight line between the user and the sensor. Table 1 summarizes the rate of successful alarm notification and the longest distance at which the hazard alarm notification arrived for each of the four sensor deployment cases. ## 6 Discussion

The results of this research help clarify both the feasibility and potential issues of using a beacon sensor-based hazard alarm notification system at construction sites. The experiment results show that, depending on the sensor deployment setting, the user can receive a hazard alarm notification on time, meaning a worker would receive the hazard information just before arriving at the workspace with the hazard in it. As the hazard alarm system requires the user's response, construction managers can confirm that the hazard information has been transferred to the affected workers.