• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.558-560
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• 2009

Ad hoc could be very important in the location-based technology, much research could work underway in these days. In this paper, the property of based on RSSI (Received signal strength indicator) could extract information. A distance of self-made nodes testing were implemented for an each other between given nodes. A special purpose UoC (ubiquitous of System On Chip) system was developed in this paper. This system could provide a function of RSSI property for location information experiments. This attribute information could make possible LBS (Location-Based System) experiment for ranging technique in this development system. The experiment results could show a possibility performance delivery ratio and the number of hops. This given performance of the location information could be used to acquire a ranging technique in large number of nodes network system.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.71-76
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• 2023

In this paper, we propose a smoothed received signal strength indicator (RSSI)-based distance estimation using deep neural network (DNN) for accurate distance estimation in an environment where a single receiver is used. The proposed scheme performs a data preprocessing consisting of data splitting, missing value imputation, and smoothing steps to improve distance estimation accuracy, thereby deriving the smoothed RSSI values. The derived smoothed RSSI values are used as input data of the Multi-Input Single-Output (MISO) DNN model, and are finally returned as an estimated distance in the output layer through input layer and hidden layer. To verify the superiority of the proposed scheme, we compared the performance of the proposed scheme with that of the linear regression-based distance estimation scheme. As a result, the proposed scheme showed 29.09% higher distance estimation accuracy than the linear regression-based distance estimation scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.107-112
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• 2013

In this paper, we discuss a 3-dimensional localization sensor node using EM waves (Electromagnetic waves) with RSSI (Received Signal Strength Indicator). Generally EM waves cannot be used in underwater environment, because the signal is highly attenuated by the water medium according to the distance. Although the signal quickly reduces in underwater, the reducing tendency is very clear and uniform. Hence EM waves have possibility as underwater distance sensors. The authors have verified the possibility by theory and several experiments, and developed calibration methods in case of linear and planer environment. For 3-dimensional localization in underwater, it must be known antenna's radiation pattern property in electric plane(called E-plane). In this paper, we proceed experiments to verify attenuation tendency with z axis movement, PLF (Polarization Loss Factor) and ILF (Inclination Loss Factor) with its theoretical approach.

• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.55-62
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• 2016

In this paper, we present indoor localization technologies using the bluetooth signal categorizing them into proximity based, triangulation based and fingerprinting based technologies. Then we provide localization accuracy improvement algorithms such as moving average, K-means, particle filter, and K-Nearest neighbor algorithms. We define important performance issues for indoor localization technologies and analyze recent technologies according to the performance issues. Finally we provide experimental results for correlation between RSSI and distance. We believe that this paper provide wise view and necessary information for recent localization technologies using the bluetooth signal.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.167-168
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• 2023

본 논문에서는 저전력 433MHz 주파수 RF 신호가 여러 종류의 벽을 투과할 시 신호에 일어나는 감쇠를 비교한다. 국내에서 기존의 와이파이, 블루투스 같은 고주파수 대역의 RF 신호에 관한 연구 및 실험은 많이 행해지었지만, 한국의 전파 관리법에 의해 성능이 제한된 비면허 주파수인 433MHz 대역의 RF 신호에 관한 연구는 매우 적게 이루어져 있다. 이러한 저주파수 대역 신호의 가장 큰 장점은 장거리 통신에 능하고 벽 투과특성이 뛰어나다는 것이다. 본 논문에서는 실험을 통해 433MHz 대역 RF 신호가 여러 종류의 장애물을 통과 시 신호 세기가 어떻게 변하는지 각각 비교하고 이를 통해 비가시 영역에서 저전력 주파수 통신의 사용 가능성을 확인한다.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.162-164
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• 2009

IEEE 802.15.4에서 채널 간 충돌을 회피하기 위해 제안된 CSMA/CA 알고리듬은 수신되는 신호의 상관관계를 나타내는 지표인 LQI (Link Quality Indicator) 값과 수신되는 신호의 크기를 나타내는 지표인 RSSI (Received Signal Strength Indicator) 값을 이용한다. 지그비 모듈에서 측정되는 LQI 값은 수신되는 신호의 크기에 따라 변화하는데, 그 상관관계가 매우 부정확한 문제가 있어 왔다. 본 논문에서는 CC2430과 CC2591을 이용해 제작된 지그비 모듈을 기반으로 수신 신호의 크기와 LQI 값을 실험적으로 측정하여 RSSI 값에 따른 LQI 값의 상관관계를 분석하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.595-599
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• 2006

센서 네트워크는 주로 물리적인 공간의 모니터링이나 위치 추적과 같은 주변의 정보를 얻고자 하는 환경에 사용되며, 이러한 정보는 비대칭적이고 비신뢰적인 무선 링크로 인해 불필요한 재전송을 요구하고 많은 손실이 발생한다. 이 때문에 신뢰적이고 에너지 효율적인 링크를 선택하기 위해 RSSI(Received Signal Strength Indicator), LQI(Link Quality Indicator)를 이용하여 무선 링크에 대한 품질을 추정하는 기법이 필요하다. 본 논문에서는 그 일차적인 단계로써 단일 홉 무선 센서 네트워크에서 MICAZ에 구현된 RSSI, LQI 값을 이용하여 노드간 무선 링크 품질을 모니터링하는 응용을 구현하였다. 구현 시나리오는 무선 링크의 비대칭성을 고려하기 위해, 순방향 링크 품질의 경우 센서 플랫폼이 BS로부터 수신한 요구 메시지의 RSSI, LQI 값을 응답 메시지에 캡슐화하여 BS로 전송하도록 하였고, 역방향 링크 품질의 경우 BS가 센서 플랫폼으로부터 수신한 응답 메시지의 RSSI, LQI 값을 기반으로 하였다. 또한 BS로 취합된 이러한 두 링크 품질을 PC상에 시각적으로 표시하기 위해 자바 기반의 링크 품질을 모니터링하는 응용을 구현하였다. 간단한 실험을 통해 RSSI, LQI로 얻은 무선 링크의 품질과 특성을 확인하였다.

• Journal of the Korea Society of Computer and Information
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• v.22 no.8
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• pp.9-14
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• 2017

In this paper, we propose the smart belt system for preventing loss of items using Beacon. The proposed system monitors the distances of the registered items via the belt that is always worn. The belt determines the loss of the items by measuring the relative distance via RSSI (Received Signal Strength Indicator) value of the signals received from the BLE (Bluetoothl Low Energy) sensor, which is attached on the items such as bags and wallets. If the registered item is determined to be lost, the belt rings to remind the user of the loss. The missing status could be known to users through the smartphone application connected to the belt. The smartphone application communicates with the belt using Beacon, and provides users with a quick and easy way to check the status of their items.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1204-1208
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• 2014

As required performance of UAV (Unmanned Aerial Vehicle) becomes more complex and complicated, required positioning accuracy is becoming more and more higher. GPS is a reliable world wide positioning providing system. Therefore, UAV generally acquires position information from GPS. But when GPS is not available such as too weak signal or too less GPS satellites environments, UAV needs alternative positioning system such as network positioning system. RSSI (Received Signal Strength Indicator) based positioning, which is one method of network positioning technologies, determines its position using RSSI measurements containing distance information from AP (Access Point)s. In that method, a selected AP's configuration has strong and tight relationship with its positioning errors. In this paper, for, we additionally account AP's configuration information by adopting DOP (Dilution of Precision) into AP selection procedures and provide more accurate RSSI based positioning results.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.49-54
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• 2021

Indoor positioning system becomes of increasing interests due to the demands for accurate indoor location information where Global Navigation Satellite System signal does not approach. Wi-Fi access points (APs) built in many construction in advance helps developing a Wi-Fi Received Signal Strength Indicator (RSSI) based indoor localization. This localization method first collects pairs of position and RSSI measurement set, which is called fingerprint database, and then estimates a user's position when given a query measurement set by comparing the fingerprint database. The challenge arises from nonlinearity and noise on Wi-Fi RSSI measurements and complexity of handling a large amount of the fingerprint data. In this paper, machine learning techniques have been applied to implement Wi-Fi based localization. However, most of existing indoor localizations focus on single position estimation. The main contribution of this paper is to develop multi-target localization by using deep neural, which is beneficial when a massive crowd requests positioning service. This paper evaluates the proposed multilocalization based on deep learning from a multi-story building, and analyses its learning effect as increasing number of target positions.