# Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation
## 4 Results and Discussion  
The data collection and analysis framework proposed in this paper is exceptional in terms of:  
1. The needed time, effort, and cost: the total needed time for data collection using the proposed robot-based system was approximately 3 h, which introduced a clear acceleration of the data-collection process compared to what could be collected manually. Specifically, about one minute was needed to collect 32 RSSI samples from all routers in the vicinity at three different heights, which indicates the robot's efficiency. There were about 1.5 K beacons collected, processed, transmitted to the cloud, and stored in the database. In addition, using the proposed robot was the ideal solution for such a repetitive task. The effort was minimized since the robot moved autonomously along the predefined area of study. The transceivers used were off-the-shelf low-cost NodeMCUs, which facilitate the research and development of state-of-the-art projects in developing communities, as was the case in this project. The cost of placing and measuring the track for the robot was marginal since it was guided by the layout of the existing floor tiles and was done in parallel to the robot's operation. 2. Highlighting the effect of the transceiver height on the RSSI value: For efficient real indoor-positioning systems, the height of the APs is usually fixed, but the transceiver  
Figure 10: PHP flowchart for offline post-processing. height is always changing based on the handheld device, its user, and its posture (sitting, standing, young child, short/tall person, etc.). This results in different received power values. To verify the effect of different heights on the RSSI values, three different heights in a real scenario were studied in this paper: the height of the handheld device when the person is sitting at the ground, the height when a child is holding the device while standing, and the height when an adult is holding the device while standing. The corresponding heights were 0.3, 0.9, and 1.5 m, respectively. The transmitter AP's height was modelled to 2.7 m in this paper and the physical location of the APs were fixed to that height. Figures 11-13 show the value of the received RSSI from different APs at the three heights. It can be seen in Figures 11-13 and Tables 2-4 that the value of the RSSI varied significantly at a selected checkpoint when the transceiver height was changing. This is a very important observation that should be taken into consideration in any indoor-positioning system. The sub-figures of Figures 11-13 demonstrate a section of the captured data where the signal strength was at its minimum (the associated with strongest Wi-Fi signal). The sub-figures clearly show that the highest collection point was associated with the strongest signal. In Tables 2-4, signal strengths at selected stop points are highlighted. The stop points highlighted in the tables were spread over the whole indoor environment of the study. The tables clearly support the observation that the signal strengths increased as the height of the transceiver device increased. Therefore, we can conclude that the height of 1.5 m had the strongest RSSI value, as the distance between the AP and the receiver was the shortest. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Checkpoint**} & \multicolumn{3}{c}{**Sample Mean of RSSI, dBm at Different Heights**} \\ \cline{2-4}  & **Height 0.3 m** & **Height 0.9 m** & **Height 1.5 m** \\ \hline \(1\) & \(-89\) & \(-80\) & \(-76\) \\ \hline \(23\) & \(-83.7\) & \(-82.6\) & \(-69.6\) \\ \hline \(72\) & \(-52.7\) & \(-50\) & \(-47.45\) \\ \hline \(91\) & \(-48.7\) & \(-36.35\) & \(-29\) \\ \hline \(112\) & \(-66.4\) & \(-64.25\) & \(-60\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the three different heights at selected checkpoints from AP (5). Figure 12: RSSI values from AP (1) with three different heights.