# Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Robot Type** & **Environment** & **Map** & **Location** & **Positioning** & **Time** & **Observations** & **Localization** & **Scope** \\  & & & **Area** & **Acquisition** & **Estimation** & **Method** & **Time** & **Observations** & **Localization** & **Scope** \\ \hline
[3] & 2019 & Custom & 145 m\({}^{2}\) & SLAM & ASMF & ZigBee & NA & S31 & 0.81 m & Generic (ZigBee) \\
[8] & 2020 & Industrial & & & KNN Bayes with & 94 & & & & & Generic (Samephones) \\
[9] & 2020 & (Pioneer 3-DX) & 2 university floors & SLAM & Gaussian process & WiFi & min & NA & 2.4 m & ( smartphones) \\
[9] & 2020 & Industrial (Scuba) & 89 m\({}^{2}\) & SLAM & EVSFM & WiFi & 2.5 h & 825 & 1 m & Generic \\
[10] & 2019 & Custom & unknown & Pre-defined & KNN & WiFi & NA & 10 & 2.21 m & Generic \\
[11] & 2018 & Industrial & 210 m\({}^{2}\) & SLAM & PDR & Bluetooth & 27 & NA & 1.84 m & Generic \\
[12] & 2020 & Industrial (Scuba) & 700 m\({}^{2}\) & SLAM & GPR-GAN & WiFi & NA & NA & 1.98 m & Generic \\
[13] & 2021 & Industrial & One room & SLAM & PDR & VLP & NA & NA & 0.8 m & Robot specific \\
[14] & 2021 & Industrial (Roborock & One room & & & & & & & \\
[15] & 580 vacuum) & (43.4 m\({}^{2}\)) & SLAM & Ridge regression & VLP & NA & 7344 & 84 to 144 mm & Robot specific \\
[16] & 2020 & Custom & One floor & SLAM & SLAM & BLE & NA & NA & NA & Wearable device \\
[17] & 2021 & Industrial (Pioneer 3-DX) & 45 m\({}^{2}\) & Pre-defined & Odometry & RFID & NA & NA & 1.22 m & Robot specific \\
[18] & 2022 & Custom & 100 m\({}^{2}\) & Pre-defined & Support-vector & WiFi & NA & NA & 0.8528 m & ( smartphones) \\  & & & & regression & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent studies in the field of indoor positioning. For an efficient RSS-based IPS, there are various factors that should be considered, such as human-body effects, building material, device diversity, antenna polarization, and sensor-placement height, and a good understanding of RSSI behavior with these factors will lead to an accurate, efficient, and effective IPS [19]. Various studies have discussed these factors, such as [20], in which the author introduces a detailed explanation of the building material and environmental effects on WiFi RSSI measurements, such that their conclusion shows that metal makes a significant contribution to signal-strength fluctuations and that isolators such as wood and plastic contribute to a reduction in signal strength. The author of [21] discussed the effect of device diversity in the localization process, and their results showed significant differences in the behavior of different devices in a practical environment. The authors of [22] demonstrated the use of a handheld device operated manually by a human for a dataset construction that maps the same area of study as this paper. The authors then used the collected dataset for constructing, training, and then testing an AI model used for location estimation. The size of the collected dataset in [22] is not clear, but it is apparent that the number of collected samples was not sufficient for constructing an accurate ANN, hence the use of the BSI method to generate a denser database and attain an effective ANN solution. The authors in [23] considered the polarization of the antenna and its effect on the accuracy of the location-estimation identification. Although the authors of [23] discussed mathematically how the RSSI value is related to the distance between the transmitter and the receiver, their focus was on the polarization of the transceivers and not on the effect of the distance between the transmitter and the receiver on the location estimation, which is the focus of this paper. Most of the existing studies overlooked the effect of the height of the receiver antenna. Therefore, this paper investigates the effect of the height of the hand-held mobile WiFi antenna attached to the robot on the received power strength of the WiFi signal.