# Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation
## 1 Introduction
Researchers from [10] solved this problem by using a robot to collect RSS at various locations in the environment. They also used interpolation methods to estimate RSS values at unmeasured locations. In addition, they applied machine-learning algorithms during the online phase to estimate the object's current position and obtained a 2.21 m error. Furthermore, no map of the environment was created, and the researchers did not provide valuable information about the environment. Only 10 reference points were used in the offline stage of the study. Indoor positioning reduces the time required to scan the environment, as well as the possibility of dealing with changes in the environment over time. To keep Bluetooth RSS maps up to date, the researchers of [11] developed a robot that can update the fingerprint database frequently. The method was tested on 210 m\({}^{2}\), and the mean error was 1.84 m. Within 27 min, the robot scanned the entire environment and generated a route between the specified reference points. According to [12], the researchers used the Gaussian Process Regression-conditioned Least Square Generative Adversarial Network (GPR-GAN) method to solve constrained spaces in the environment. Despite the authors' inability to handle the issue of reaching a small part of the environment with the robot, they reduced the mean error distance to 1.98 m over the environment. Previous studies have shown that the role of robots is important in improving the efficiency of data collection, and this has an important role in improving the efficiency of the model. Therefore, this study focuses on how the strength of the received WiFi signal is affected by the height of the handheld WiFi antenna, which is connected to the robot. Different sets of antenna heights are considered. In the rest of the paper, the related work is discussed first. The existing literature and research relevant to using Wi-Fi beacons in localization is explored. Then, an overview of the overall system architecture is presented, outlining its key components, and functionalities. A detailed description of the robot sub-system follows, where an overview of the robot's external structure, physical design, and characteristics is discussed. Additionally, a schematic description of the robot's electronic system is provided to offer a visual representation of its internal layout and connections. The next section details the robot's operation and its sub-modules. Then, the paper discusses the indoor-environment infrastructure, covering the setup necessary to support the robot's operations in an indoor setting. The backend database and web-server interface is then explored, outlining the underlying technology and functionality enabling interaction with the robot. Then, the paper presents the results obtained, along with a comprehensive discussion analyzing the implications and significance of the results. A conclusion section summarizes the main points discussed in the paper and provides a final reflection. Finally, a list of references is included to acknowledge the sources cited throughout the paper, ensuring academic integrity and facilitating further research.