• Proceedings of the Korean Information Science Society Conference
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• 2007.10d
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• pp.381-384
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• 2007

RF 신호 강도인 RSSI(Received Signal Strength indication)를 이용해 지역화를 하는 방법론이나 알고리즘은 많이 연구되어 왔다. 그러나 대다수의 연구에서 추가적인 장비를 요구하거나, 복잡한 시스템 환경을 필요로 한다. 본 논문에서는 일반적인 무선랜 환경을 사용할 수 있는 노트북에서, Windows 기반의 802.11의 RSSI를 이용해 지역화를 수행하였다. IEEE 802.11의 네트워킹 구성을 한 센서노드들 중에 AP(Access Point) 역할을 하는 한 개의 고정노드(Anchor)를 중심으로 다수의 움직일 수 있는 모바일 노드들의 위치를 추적해가는 방법이며, Anchor가 많아지거나 모바일 노드가 늘어나면 지역화의 정확도는 증가하게 된다. 이 방법은 일반적인 802.11의 무선랜 환경이 가능한 랩톱에서도 쉽게 적응이 가능하며, 부가적인 장비의 의존성이 전혀 없고 소프트웨어 적인 방법론으로 해결하였다. 또한 지역화가 가능함과 동시에 각각의 센서 노드들은 802.11을 이용한 데이터 송수신이 가능하다. 본 논문에서는 MS의 Windows 플랫폼 기반으로 테스트하였고, 효과적으로 Beacon 패킷을 송출하기 위한 해법을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1162-1179
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• 2017

In this paper, we propose indoor location tracking method using RSSI(Received Signal Strength Indicator) value received from BLE(Bluetooth Low Energy) beacon. Due to the influence of various external environmental factors, it is very difficult to improve the accuracy in indoor location tracking. In order to solve this problem, we propose a novel method of reducing the noise generated in the external environment by using a double Gaussian filter. In addition, the value of the RSSI signal generated in the BLE beacon is different for each device. In this study, we propose a method to allocate additional weights in order to compensate the intensity of signal generated in each device. This makes it possible to improve the accuracy of indoor location tracking using beacons. The experiment results show that the proposed method effectively decrease the RSSI deviation and increase location accuracy. In order to verify the usefulness of this study, we compared the Kalman filter algorithm which is widely used in signal processing. We further performed additional experiments for application area for indoor location service and find that the proposed scheme is useful for BLE-based indoor location service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5422-5441
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• 2016

Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.999-1004
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• 2010

Most of campus vehicle management systems, so far, simply manages coming in or go out of vehicles, issuing a parking tickets. Recently some of them use RFID tags to count total numbers of cars in the campus, excluding exact parking position management. In this paper we propose a new campus vehicle management system using wireless sensor network location management scheme. This system adopts RSSI based location management method with some performance improvement technique. According to the experimental result, this proposed scheme can be used to implement an effective campus vehicle management system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.352-354
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• 2016

Recent with the progress of smart devices, there is an increasing demand for indoor location-based services. For this reason, research on indoor positioning system using a iBeacon techniques added to BLE(Bluetooth Low Energy) specifications of Bluetooth4.0 has been actively. However, RSSI signal used for the measurement of the distance between the iBeacon and the receiving terminal has the problems of inaccurate distance measurement to environmental factors such as obstacles. In this paper, we propose an implemented indoor positioning technique to use filtering technology enhance the reliability of the RSSI signal and the broadcasting signal of the terminal access point function.

• The KIPS Transactions:PartC
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• v.13C no.3 s.106
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• pp.323-330
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• 2006

This paper proposes a handoff time decision mechanism to utilize resource efficiently by using Differential Received Signal Strength Indicator (DRSSI). DRSSI can be used to predict the movement direction of Mobile Station (MS). In other words, DRSSI changes its sign (+ or -) when a MS changes movement direction. This mechanism maximizes resource availability of Base Station (BS) by predicting resource reservation of MS and by giving priority of resource to MS. It is possible when a BS predicts the behavior of MSs by monitoring the DRSSI of MSs in overlapped region among cells. Additionally, we show that our proposed mechanism has better handoff blocking probability than existing mechanism with numerical modeling and analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4B
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• pp.386-390
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• 2011

Modem warfares have changed from PCW (platform Centric Warfare) to NCW (Network Centric Warfare). Therefore, it is more important to operate and manage the network. This paper proposed a node management scheme in military wireless networks. In military wireless networks, nodes can join and leave the networks easily. It causes a degradation of network capacity. This paper figured out a problem caused by node which is leaving the network. This paper proposed a RSSI based method of estimating and detecting the leaving nodes in the networks. Finally, an experimental result was demonstrated to show the efficiency of the proposed method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.95-97
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• 2019

본 논문에서는 ZigBee 기반의 WSN과 노드 연결을 위한 새로운 기법을 제안한다. 이 기법은 통신 노드 간에 격리된 노드의 수를 최소화하기 위해 수신신호강도(RSSI) 샘플에 적용된 딕슨 테스트(Dixon's test)를 사용하여 ZigBee 기반의 WSN을 위한 새로운 노드 연결 구조로써 특이점(outlier)을 제거하여 적은 수의 RSSI 값으로도 정확한 노드 연결이 가능하다. 본 논문에서는 시뮬레이션을 통하여 제안하는 기법이 기존의 RSSI 기반의 기법보다 더 높은 정확도를 유지하면서 처리 시간은 줄어든 것을 증명하였다.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.280-286
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• 2022

In order to efficiently collect data, it is essential to locate the facilities and analyze the movement data. The current technology for location collection can collect data using a GPS sensor, but GPS has a strong straightness and low diffraction and reflectance, making it difficult for indoor positioning. In the case of indoor positioning, the location is determined by using wireless network technologies such as Wifi, but there is a problem with low accuracy as the error range reaches 20 to 30 m. In this paper, using BLE 4.2 built in Raspberry Pi, we implement Bluetooth Smart Ready. In detail, a beacon was produced for Advertise, and an experiment was conducted to support the serial port for data transmission/reception. In addition, advertise mode and connection mode were implemented at the same time, and a 3-count gradual algorithm and a quadrangular positioning algorithm were implemented for Bluetooth RSSI error correction. As a result of the experiment, the average error was improved compared to the first correction, and the error rate was also improved compared to before the correction, confirming that the error rate for position measurement was significantly improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.180-182
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• 2021

This paper proposes a method for estimating the location of a user using a beacon to provide a service in an indoor environment. To estimate the location using the beacon, a Gaussian filter was applied to the RSSI value of the beacon, and the distance conversion function was obtained through the filtered RSSI value to estimate the tag location by trilateration. Then, in the indoor space where the beacons are installed, the location estimation accuracy of 8 places where 3 beacons are at a certain distance was confirmed. As a result, it was possible to confirm the position estimation accuracy of ±0.097 standard deviation and 0.242 distance error.