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Recently, UWB-based technology providing centimeter-level accuracy has been developed and widely utilized in indoor real-time location tracking systems. However, location accuracy varies due to factors such as frequency interference, collisions, reflected signals, and whether line-of-sight (LOS) conditions are met, and it can be challenging to ensure high accuracy in specific environments. Fortunately, when anchor positions are fixed, the locations of large obstacles such as columns or furniture remain relatively stable, leading to similar patterns of positioning bias at specific points. This study proposes an algorithm that corrects inaccurate positioning to more closely reflect the actual location based on bias and deviation maps generated using natural neighbor interpolation. Initially, positioning bias and deviations at specific points are sampled, and bias and deviation maps are created using natural neighbor interpolation. During location tracking, the algorithm detects candidate clusters and determines the centroid to estimate the actual location by applying the bias and deviation maps to the measured positions derived through trilateration. To validate the proposed algorithm, experiments were conducted in a non-LOS (NLOS) indoor environment. The results demonstrate that the proposed algorithm can reduce the positioning bias of a UWB-based RTLS by approximately 71.34% compared to an uncalibrated system. [ABSTRACT FROM AUTHOR]

Cloud virtual reality (VR) is attracting attention in terms of its lightweight head-mounted display (HMD), providing telepresence and mobility. However, it is still in the research stages due to motion-to-photon (MTP) latency, the need for high-speed network infrastructure, and large-scale traffic processing problems. These problems are expected to be partially solved through edge computing, but the limited computing resource capacity of the infrastructure presents new challenges. In particular, in order to efficiently provide multi-user content such as remote meetings on edge devices, resource provisioning is needed that considers the application's traffic patterns and computing resource requirements at the same time. In this study, we present a microservice architecture (MSA)-based application to provide multi-user cloud VR in edge computing and propose a scheme for planning an efficient service deployment considering the characteristics of each service. The proposed scheme not only guarantees the MTP latency threshold for all users but also aims to reduce networking and computing resource waste. The proposed scheme was evaluated by simulating various scenarios, and the results were compared to several studies. It was confirmed that the proposed scheme represents better performance metrics than the comparison schemes in most cases from the perspectives of networking, computing, and MTP latency. [ABSTRACT FROM AUTHOR]

Indoor position fingerprint-based location estimation methods have been widely used by applications on smartphones. In these localization estimation methods, it is very popular to use the RSSI (Received Signal Strength Indication) of signals to represent the position fingerprint. This paper proposes the design of a particle filter for reducing the estimation error of the machine learning-based indoor BLE location fingerprinting method. Unlike the general particle filter, taking into account the distance, the proposed system designs improved likelihood functions, considering the coordinates based on fingerprint points using mean and variance of RSSI values, combining the particle filter with the k-NN (k-Nearest Neighbor) algorithm to realize the reduction in indoor positioning error. The initial position is estimated by the position fingerprinting method based on the machine learning method. By comparing the fingerprint method based on k-NN with general particle filter processing, and the fingerprint estimation method based on only k-NN or SVM (Support Vector Machine), experiment results showed that the proposed method has a smaller minimum error and a better average error than the conventional method. [ABSTRACT FROM AUTHOR]