The recurrent neural network (RNN) model, which is a deep-learning network that can memorize past information, is used in this paper to memorize continuous movements in indoor positioning to reduce positioning error. To use an RNN model in Wi-Fi-fingerprint based indoor positioning, data set must be sequential. However, Wi-Fi fingerprinting only saves the received signal strength indicator for a location, so it cannot be used as RNN data. For this reason, we propose a movement path data generation technique that generates data for an RNN model for sequential positioning from Wi-Fi fingerprint data. Movement path data can be generated by creating an adjacency list for Wi-Fi fingerprint location points. However, creating an adjacency matrix for all location points requires a large amount of computation. This problem is solved by dividing indoor environment by K-means clustering and creating a cluster transition matrix based on the center of each cluster.

Recently, various application fields utilizing Wi-Fi fingerprint data have been under research. However, fingerprint data collected from a specific location does not include relevant information, such as continuity. Therefore, most indoor positioning technologies predict the user’s location based on location signals collected at specific points within the indoor space, without taking into account the user’s movements. Hence, there is a need for technology that improves the accuracy of indoor positioning while moving. This paper proposes a technique to generate movement path data by applying the concepts of “BB” and “Grid Cell” from computer vision to Wi-Fi fingerprint data. This approach represents data points as bounding boxes (BBs), then establishes grid cells and clusters of these BBs to generate adjacency information. Subsequently, movement path data are created based on this information. We utilized the movement path information generated from the dataset as training data for machine learning and introduced an enhanced indoor positioning technology. First, the experiments in this paper assessed the performance of the proposed technology based on the number of paths in the LSTM model. Second, the performance of clustering methods was compared through experiments. Finally, we evaluated the performance of various machine learning models. The experimental results confirmed a maximum accuracy of 94.48% when determining the location based on route information. Clustering performance improved accuracy by up to 3%. In comparative experiments with machine learning models, accuracy improved by up to 2.8%.

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.

The Ontology-based Patient Management System using Sensor Data Sensor data Integration;Ontology;iBeacon;Mobile Beacon;Indoor Positioning; Recently, there have been many research that recognize the situation using sensors. However, sensor data collection and analysis are still lacking in integration. This is because the data generated by the sensor is difficult to match in terms of metadata and units. Therefore, a methodology for efficiently using data generated from various sensors is needed. In this paper, we propose a system that recognizes the location through information generated from a moving iBeacon. This system constructs the ontology with the data that can recognize the exact position when the patient wearing iBeacon moves in the room. This maps standard items and sensor items, and stores the results of filtering the detected values as knowledge. the system can extract efficient location information by recognizing the value generated by moving the patient carrying iBeacon through the ontology. This can be applied not only to beacons but also to other sensors, and it can be applied variously according to the ontology configuration.