• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2590-2608
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• 2023

The high-altitude and mobility characteristics of unmanned aerial vehicles (UAVs) have made them a key element of new radio systems, particularly because they can exceed the limits of terrestrial networks. However, at high altitudes, UAVs can be significantly affected by intercell interference at a high line-of-sight probability. To mitigate this drawback, we propose an algorithm that selects the optimal beam to reduce interference and maximize transmission efficiency. The proposed algorithm comprises two steps: constructing a user-location-based fingerprint database according to the user types presented herein and cooperative beam selection. Simulations were conducted using cellular cooperative downlink systems for analyzing the performance of the proposed method, and the signal-to-interference-plus-noise cumulative distribution function and spectral efficiency cumulative distribution function were used as performance analysis indicators. Simulation results showed that the proposed algorithm could reduce the effect of interference and increase the performance of the desired signal. Moreover, the algorithm could efficiently reduce overheads and system cost by reducing the amount of resources required for information exchange.

• Journal of the Korea Safety Management & Science
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• v.21 no.4
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• pp.59-65
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• 2019

The Location Based Service is growing rapidly nowadays due to the universalization of the use for smartphone, therefore the location determination technology has been placed in an important position. This study suggests a method that can provide the estimate of users' location by using PDR method and smartphone geomagnetic sensor data. This method assists the measure of enhancing the accuracy of indoor localization. Moreover, it is to study ways to provide the exact indoor layout for evacuating the workers in emergency such as fires and natural disasters.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.71-82
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• 2006

This paper propose a realtime tracking algorithm of mobile object in indoor environment. this proposed system selects location data closer to mobile objects in real time that are results of Triangulation method and DCM(Database Correlation Method)method. Also, this system applies adjusted location data selected by using Kalman filter, and in result it improved location accuracy of transfer object. Be studied in existing the Kalman filter have unstable location data until its settlement because of it extracts current values by using the past the information. However, proposed location tracking system don't apply existent Kalman filter to this system and it permits precisional tracking location by uses more effective methods.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.316-318
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• 2006

무선 통신 기술의 발달로 사용자의 이동성이 제공되기 시작하면서 위치기반서비스(LBS: Location Based Service)가 부각되었다. 서비스의 예로 공공안전 서비스, 위치추적 서비스, 항법 서비스, 정보제공 서비스 등 부가가치가 높은 서비스들이 많이 있는데, 이러한 서비스를 개발하려면 필수적으로 사용자의 위치를 파악해야 한다. 옥내 측위 방법으로 여러가지가 실험되고 있는데, Fingerprint 방법이 일반적으로 가장 정확도가 높다. 기존의 Fingerprint 방식에는 K-NN 방법과 Bayesian 방법이 소개되었는데, 결정 트리를 이용한 방법은 효율성이 기존의 K-NN이나 Bayesian 방법보다 뛰어나게 좋음에도 불구하고 적용한 사례가 없다. 그래서 본 논문은 결정 트리를 이용하는 방법을 제안한다. K-NN 및 Bayesian 방법과 제안하는 방법을 비교 분석한 결과와 제안하는 방법의 실험 결과도 보인다.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.869-875
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• 2009

Safety is a big issue in the construction sites. For safe and secure management, tracking locations of construction resources such as labors, materials, machineries, vehicles and so on is important. The materials, machineries and vehicles could be controlled by computer, whereas the movement of labors does not have fixed pattern. So, the location and movement of labors need to be monitored continuously for safety. In general, Global Positioning System(GPS) is an opt solution to obtain the location information in outside environments. But it cannot be used for indoor locations as it requires a clear Line-Of-Sight(LOS) to satellites Therefore, indoor location monitoring system could be a convenient alternative for environments such as tunnel and indoor building construction sites. This paper presents a case study to investigate feasibility of Wi-Fi based indoor location monitoring system in construction site. The system is developed by using fingerprint map of gathering Received Signal Strength Indication(RSSI) from each Access Point(AP). The signal information is gathered by Radio Frequency Identification (RFID) tags, which are attached on a helmet of labors to track their locations, and is sent to server computer. Experiments were conducted in a shield tunnel construction site at Guangzhou, China. This study consists of three phases as follows: First, we have a tracking test in entrance area of tunnel construction site. This experiment was performed to find the effective geometry of APs installation. The geometry of APs installation was changed for finding effective locations, and the experiment was performed using one and more tags. Second, APs were separated into two groups, and they were connected with LAN cable in tunnel construction site. The purpose of this experiment was to check the validity of group separating strategy. One group was installed around the entrance and the other one was installed inside the tunnel. Finally, we installed the system inner area of tunnel, boring machine area, and checked the performance with varying conditions (the presence of obstacles such as train, worker, and so on). Accuracy of this study was calculated from the data, which was collected at some known points. Experimental results showed that WiFi-based indoor location system has a level of accuracy of a few meters in tunnel construction site. From the results, it is inferred that the location tracking system can track the approximate location of labors in the construction site. It is able to alert the labors when they are closer to dangerous zones like poisonous region or cave-in..

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.5A
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• pp.526-536
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• 2006

According as Ubiquitous comes, interest for indoor location tracking system was more increased socially. However, existing indoor location tracking system doesn't correspond actively in frequent change of indoor environment, and there is a problem that correct location measurement of transfer object is difficult by NLOS property of indoor environment. Purpose of this paper proposes environment accommodation location tracking system that is improved location precision of transfer object and grasps location of indoor transfer object effectively that is essential element effectively to provide service to satisfy various user's request according as Ubiquitous comes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.129-134
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• 2018

Research on the technique for estimating the indoor position has been actively carried out. In particular, the WiFi fingerprint method, which does not require any additional infrastructure, is being partially used because of its high economic efficiency. The KNN method which estimates similar points to the corresponding points by comparing intensity information of the WLAN reception signal measured at various points in advance with intensity information measured at a specific point in the future is simple but has a good performance. However, in the conventional KNN scheme, since the number K of average candidate positions is constant, there is a problem that the position estimation error is not optimized according to a specific point. In this paper, we proposed an algorithm that adaptively changes the K value for each point and applied it to experimental data and evaluated its performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.6
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• pp.531-539
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• 2013

Fingerprinting technique uses the radio signal strength measured reference locations is typically used, although there are many Wi-Fi based location tracking techniques. However, it needs numerous reference locations for precision and accuracy. This paper the analyzes problems of previous techniques and proposes a fingerprinting system using reliability based on a signal strength map. The system collects the signal strength data from a number of reference locations designated by the developer. And then it generates path-loss models to one of the access points for each reference location. These models calculate the predicted signal strength and reliability for a lattice. To evaluate proposed method and system performance, We perform experiments in a $20m{\times}22m$ real indoor environment installed access points. According to the result, the proposed system reduced distance error than RADAR. Comparing the existing system, it reduced about 1.74m.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.490-491
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• 2021

Recently, technologies using Wi-Fi fingerprints and deep learning are being studied to provide accurate location-based services in an indoor environment. At this time, the composition of learning data is very important, and it is essential to collect sufficient data necessary for learning. However, the number of specific points for the collection of radio signal data within the area requiring positioning is infinite, and it is impossible to collect all of these data. Therefore, there is a need for a way to make up for insufficient learning data. This study proposes a method of constructing a sufficient number of location data necessary for learning based on insufficiently collected location data.

• ETRI Journal
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• v.40 no.4
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• pp.531-536
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• 2018

The weighted K-nearest neighbor (WKNN) algorithm is used to reduce positioning accuracy, as it uses a fixed number of neighbors to estimate the position. In this paper, we propose a dynamic threshold location algorithm (DH-KNN) to improve positioning accuracy. The proposed algorithm is designed based on a dynamic threshold to determine the number of neighbors and filter out singular reference points (RPs). We compare its performance with the WKNN and Enhanced K-Nearest Neighbor (EKNN) algorithms in test spaces of networks with dimensions of $20m{\times}20m$, $30m{\times}30m$, $40m{\times}40m$ and $50m{\times}50m$. Simulation results show that the maximum position accuracy of DH-KNN improves by 31.1%, and its maximum position error decreases by 23.5%. The results demonstrate that our proposed method achieves better performance than other well-known algorithms.