Shin, H.-G.; Lee, D.; Hwang, C.-G.; Yoon, C.-P. Enhanced Clustering and Indoor Movement Path Generation from Wi-Fi Fingerprint Data Using Bounding Boxes and Grid Cells. Appl. Sci.2023, 13, 10647.
Shin, H.-G.; Lee, D.; Hwang, C.-G.; Yoon, C.-P. Enhanced Clustering and Indoor Movement Path Generation from Wi-Fi Fingerprint Data Using Bounding Boxes and Grid Cells. Appl. Sci. 2023, 13, 10647.
Shin, H.-G.; Lee, D.; Hwang, C.-G.; Yoon, C.-P. Enhanced Clustering and Indoor Movement Path Generation from Wi-Fi Fingerprint Data Using Bounding Boxes and Grid Cells. Appl. Sci.2023, 13, 10647.
Shin, H.-G.; Lee, D.; Hwang, C.-G.; Yoon, C.-P. Enhanced Clustering and Indoor Movement Path Generation from Wi-Fi Fingerprint Data Using Bounding Boxes and Grid Cells. Appl. Sci. 2023, 13, 10647.
Abstract
Recently, various applications of indoor location-based services using Wi-Fi fingerprint data have been studied. However, since fingerprint data collected at a specific location does not have continuous information between signals and correlation information between signals, path data cannot exist in principle. Therefore, most indoor positioning technologies are based on predicting the position of a stationary positioner at a specific location based on signal data collected at a specific location in the indoor space. Due to the discontinuous nature of these signals, they are unable to account for user movement. Therefore, there is a need for techniques to improve the accuracy of indoor positioning in moving situations. This paper proposes a method for enriching all data points with relevant information to obtain improved indoor positioning results and an improved technique for generating movement path data. The proposed technique generates relevant information with continuity by expressing data points as Bounding Boxes (BB) and subsequently clustering them using grid cells. Through the proposed method, we represented and expanded the indoor location data, which consists of data points, as BB. However, data points represented by BB do not have any adjacency information with each other, and as a result, it is not possible to create a movement path between neighboring BB. To solve the problem, we used clustering, a machine learning technique that categorizes data points into groups, to group BBs. This was done by constructing a YOLO (You Only Look Once) grid cell that included all BBs and clustered them into grid cells that were divided into specific regions through the suggestion phase. The clustering results align with the indoor structure of the building, indicating that the data points have been appropriately clustered. BBs inside a grid cell are contained within a moveable area and have related information. We also generated association information between neighboring grid cells through the proposal technique, and then generated path information that can be traveled between grid cells. In this paper, we use the movement path information generated from a dataset as training data for machine learning, and through this, we propose an enhanced indoor positioning technology.
Keywords
Wi-Fi fingerprint; Calculate Movement Path; IoU(Intersection over Union); LSTM(Long Short-Term Memory); YOLO v2
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
