• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.25-31
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• 2017

The indoor location based service proposed in the study was assigned to target a moving user. Positioning in the outdoor environment is accurate while using GPS. However, in an indoor environment, positioning is inaccurate and difficult. In order to overcome this, studies of various techniques for positioning based on wireless communication such as Wi-Fi, Zigbee and Bluetooth are being performed. The RSSI value and the delivery signal of the bluetooth beacon are measured according to the distance, and to a database. It was applied calculating the value for the average RSSI and the RSSI filtering feedback. Filtering is used to reduce the error of the RSSI values that are measured at long distance. When average and feedback filtering coefficient are set with 0.5, irregular and highly RSSI values are decreased. As the distance increases, the range of error is confirmed to have a reduction when using a distance calculation correction algorithm. Finally, when using the RSSI measurement results filtering, it corrects an unstable signal. Also, the distance correction algorithm is used to reduce a range of errors.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.242-243
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• 2018

무선 통신기술이 발전함에 따라 위치기반 서비스에 대한 관심 또한 증가하고 있다. 그 중 저전력 블루투스 기술을 사용한 비콘(Beacon)은 실내 위치인식이 불가능한 GPS와 달리 실내에서도 측위가 가능하여 사용성이 주목 받고 있다. 그러나 비콘으로부터 수신되는 RSSI(Received Signal Strength Indication) 값은 여러 환경요소로부터 영향을 받기 때문에 RSSI값을 기반으로 한 거리측정이 실제거리와의 오차가 크게 나타난다. 이에 따른 문제를 해결하기 위한 기존의 연구들이 존재하지만 평균적으로 10m이하의 거리에서 2m의 오차를 나타내고 있다. 본 연구에서는 RSSI의 오차를 줄이기 위해 확장 칼만 필터와 신호 안정화 필터를 사용하여 Raw Data를 전처리 한 후 산출된 Cleaned Data를 기반으로 각 거리단위에 최적화된 ANN(Artificial Neural Network)모델을 생성하여 거리를 측정하는 기법을 제안한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.308-311
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• 2008

One of the key technical elements of wireless sensor network (WSN) is location management of sensor nodes. Typical node location management methods use GPS, ultrasonic sensors or RSSI. In this paper we propose a new location management method which adopts regression analysis of RSSI measurement to improve the accuracy of sensor node position estimation. We also evaluated the performance of proposed method by comparing the experimental results with existing scheme. According to the results, our proposed method showed better accuracy than existing location management scheme using RSSI and Firis' equation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.85-90
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• 2017

Indoor location-based services have been developed based on the Internet of Things technologies which measure and analyze users who are moving in their daily lives. These various indoor positioning technologies require separate hardware and have several disadvantages, such as a communication protocol which becomes complicated. Based on the fact that a reduction in signal strength occurs according to the distance due to the physical characteristics of the transmitted signal, RSSI technology that uses the received signal strength of the wireless signal used in this paper measures the strength of the transmitted signal and the intensity of the attenuated received signal and then calculates the distance between a transmitter and a receiver, which requires no separate costs and makes to implement simple measurements. It was applied calculating the value for the average RSSI and the RSSI filtering feedback. Filtering is used to reduce the error of the RSSI values that are measured at long distance.It was confirmed that the RSSI values through the average filtering and the RSSI values measured by setting the coefficient value of the feedback filtering to 0.5 were ranged from -61 dBm to - 52.5 dBm, which shows irregular and high values decrease slightly as much as about -2 dBm to -6 dBm as compared to general measurements.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1129-1136
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• 2022

This study leads to the shortcoming that the RSSI distance measurement method is easily affected by the external environment and the position error is large, leading to the problem of optimizing the distance values measured by the RSSI distance measurement nodes in this three-dimensional configuration environment. We proposed the CA-PSO-BP algorithm, which is an improved version of the CA-PSO algorithm. The proposed algorithm allows setting unknown nodes in WSN 3D space. In addition, since CA-PSO was applied to the BP neural network, it was possible to shorten the learning time of the BP network and improve the convergence speed of the algorithm through learning. Through the algorithm proposed in this study, it was proved that the precision of the network location can be increased significantly (15%), and significant results were obtained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.243-245
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• 2021

Existing beacons are suitable for providing untact services, but they have the disadvantage of difficulty in accurate indoor positioning because the deviation in signal strength increases depending on the environment. In general, trilateration technique can reduce deviation, but if the distance between beacons is quite irregular, it becomes difficult to apply the algorithm. Therefore, in this paper, we studied how to reduce the signal power measurement error between beacons. First, we transformed the distance measurement formula using RSSI, assuming that the TX values were the same. In addition, we compared measurement errors with existing beacons by searching beacons with beacons scanner applications implemented with Android. As a result, it was confirmed that if a certain distance was further away, the measurement was measured more accurately than the non-changing beacon. Through this, accurate indoor positioning will be possible even in various disability situations. It is also expected that there will be more cases of establishing services that combine beacon with non-face-to-face services.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.147-153
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• 2018

Beacon enables one to measure distance indoors based on low-power Bluetooth low energy (BLE) technology, while GPS (Global Positioning System) only can be used outdoors. In measuring indoor distance using Beacon, RSSI (Received Signal Strength Indication) is considered as the one of the key factors, however, it is influenced by various environmental factors so that it causes the huge gap between the estimated distance and the real. In order to handle this issue, we propose the adaptive ANN (Artificial Neural Network) based approach to measuring the exact distance using Beacon. First, we has carried out the preprocessing of the RSSI signals by applying the extended Kalman filter and the signal stabilization filter into decreasing the noise. Then, we suggest the multi-layered ANNs, each of which layer is learned by specific training data sets. The results showed an average error of 0.67m, a precision of 0.78.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.745-748
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• 2009

본 논문은 ZigBee 기술을 이용하여 실내 위치 인식 알고리즘을 설계하고 구현하였다. ZigBee의 가장 큰 장점은 RFID, 적외선, 초음파 기술 등과 비교하여 저전력으로 오랜 시간 동안 사용할 수 있으면서도 가격이 저렴하다는 것이다. 본 논문은 ZigBee를 이용한 위치 인식 기술에 RSSI와 삼각 측량법, 그리고 다수의 데이터에서 정확한 RSSI값을 선택하는 알고리즘을 설계하였고, 위치 인식의 정확도를 높이는데 초점을 두었다. RSSI값을 미리 실측하여 Curve Fitting을 이용하여 각각의 고정 AP마다 RSSI와 거리의 관계식을 산출하여 위치 계산에 사용하였다. 또한 실제 위치 인식 시스템을 기존의 삼각 측량법만을 사용하는 방법과 본 논문에서 제안하는 방법을 각각 구현하였다. 또한 모의실험을 통해 실제 모바일 노드의 위치와 측정된 위치의 오차율을 비교하여 성능을 측정하였다. 모의실험을 통해 성능을 비교하여 모바일 노드의 위치 인식 오차율을 줄이고, 정확도를 향상하였다.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1840-1848
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• 2017

Linear wireless sensor networks typically construct a network topology with a high reliability through sequential 1:1 mapping among sensor nodes, so that they are used in various surveillance applications of major national infrastructures. Most existing techniques for identifying sensor nodes in those networks are using GPS, AOA, and RSSI mechanisms. However, GPS or AOA based node identification techniques affect the size or production cost of the nodes so that it is not easy to construct practical sensor networks. RSSI based techniques may have a high deviation regrading location identification according to propagation environments and equipment quality so that complexity of error correction algorithm may increase. We propose a timestamps based sequential localization algorithm that uses transmit and receive timestamps in a message between sensor nodes without using GPS, AOA, and RSSI techniques. The algorithms for distance measurement between each node are expected to measure distance within up to 1 meter in case of an crystal oscillator of 300MHz or more.