• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.742-745
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• 2009

This paper proposed indoor location recognition method based on RSSI (received signal strength indication) using the LVQ (Learning Vector Quantization) network. The LVQ inputs are the RSSI values measured by the fixed reference nodes and the output are the spatial sections. In order to verify the effectiveness of the proposed method, we performed experiments, and then compared to the conventional triangularity measurement method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.2
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• pp.181-188
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• 2013

In this paper, we present a system design for location determination inside the ship. Since the GPS signal can not be received in the interior of the large vessel, we use the vessel wireless AP (Access Point) RSSI (received signal strength indication) to accurately measure the position. We convert the RSSI for the 3 AP's into distance through the Friis formula and get the location through triangulation. The signal strength varies irregularly due to noise making it difficult to obtain the exact location. Thus Kalman filter is used to real-time position correction, that is store in a server database.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.67-73
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• 2008

This paper describes a system for location tracking wireless sensor nodes in an indoor environment. The sensor reading used for the location estimation is the received signal strength indication (RSSI) as given by an RF interface. By tagging users with a mobile node and deploying a number of reference nodes at fixed position in the room, the received signal strength indicator can be used to determine the position of tagged users. The system combines Euclidean distance technique with signal strength obtained by measurement driven log-normal path loss model of 2.4 GHz wireless channel. The experimental results demonstrated the ability of this system to estimate the location with a error less than 1.3m.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.187-188
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• 2016

실내에서 작업자의 위치 정보를 취득하면 사용자의 이동 경로 파악이나 물체의 지역화 및 상황인지 서비스가 가능하다. 최근, BLE(Bluetooth Low Energy) 비콘을 사용하여 작업자의 실내 위치를 파악할 수 있는 여러 가지 연구가 진행되고 있으나 BLE 비콘의 신호세기 강도와 RSSI(Received signal strength indication)의 정확도에 대한 에러율 증가로 인해 실내 작업자 위치 파악 연구에 어려움이 있다. 본 연구에서는 BLE 비콘에서 취득한 RSSI 정보에 대해 PCA, ICA, SVM의 패턴인식 알고리즘을 적용하여 인식정확도를 구하고, 여러 패턴인식 알고리즘 중에서 어떤 알고리즘이 인식정확도 측면에서 적용이 가능한지 제시하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.9-14
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• 2008

Ubiquitous implementation of indoor location-based technology is recognized that one of the important elements of the technology. Specifically, the hospital management of patients, the silver-town management, the implementation of the smart home for the indoors rather than outdoors in a range of broadband users for location-aware technology is needed. This paper in wireless devices with an indoor location awareness shows about the system's technical design and implementation. Location-based technology for wireless LAN users aware of the strength of radio signals (Received Signal Strength Indication, RSSI) using trilateration. Topographic mapping system will be implemented wireless devices and servers, Access Point (AP), which is the system's development and testing throughout the physical environment to determine the potential for real-life applications.

• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.2
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• pp.71-78
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• 2012

Based on the Zigbee-based wireless sensor network, I suggest the way to reduce errors between the short distance, improving the accuracy of the presumed distance by revising the deviation of RSSI(Received Signal Strength Indication) values is to estimate the distance using only the RF signal power without the additional hardware. In general, the graph measured by RSSI values shows the proximity values which are ideally reduced in proportion to the distance under the free outdoor space in which LOS(Line-Of-Sight) is guaranteed. However, if the result of the received RSSI values are each substituted to the formula, it can produce a larger margin of error and less accurate measurement since it is based upon the premise that this free space is not affected by reflected waves or obstacles caused by the ground and electronic jamming engendered by the environment. Therefore, the purpose of this study is to reduce the margin of errors between the distances and to measure the proximity values with the ideal type of graph by suggesting the way to revise the received RSSI values in the light of these reflected waves or obstacles and the electronic jamming. In conclusion, this study proves that errors are reduced by comparing the proposed deviation correction method to the revised RSSI value.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.725-730
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• 2012

In this paper, we propose the localization algorithm for the cooperative behavior of the swarm robots based on WSN (Wireless Sensor Network). The proposed method is as follows: First, we measure positions of the L-bot (Leader robot) and F-bots (Follower robots) by using the APIT (Approximate Point In Triangle) and the RSSI (Received Signal Strength Indication). Second, we measure relative positions of the F-bots against the pre-measured position of the L-bot by using trilateration. Then, to revise a position error caused by noise of the wireless signal, we use the particle filter. Finally, we show the effectiveness and feasibility of the proposed method though some simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.444-452
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• 2017

Recently, a demand of Internet-of-things (IoT) rises dramatically and an interest in Low Power Wide Area (LPWA) grows larger accordingly. In this paper, performance in LoRa which is included in LPWA standard is analyzed. Particularly, after measuring Received Signal Strength Indication (RSSI) of received signal on Line-of-sight (LoS) and Non-line-of-sight (NLoS) environment and it is compared with RSSI which theoretical path loss model is applied to. Among many path loss models, the simulation for theoretical RSSI use Log-distance, Two-ray model and Okumura-Hata model that is based on the test database. Consequently, the result of Okumura-Hata model is the most similar with the measured RSSI. When a network based on LoRa is built, this result can used to decide optimal node arrangement.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.163-168
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• 2008

Navigation system based on indoor location estimation is one of the core technologies in mobile robot systems. Wireless sensor network has great potential in the indoor location estimation due to its characteristics such as low power consumption, low cost, and simplicity. In this paper we present an algorithm to estimate the indoor location of mobile robot based on wireless sensor network and fuzzy modeling. ZigBee-based sensor network usually uses RSSI(Received Signal Strength Indication) values to measure the distance between two sensor nodes, which are affected by signal distortion, reflection, channel fading, and path loss. Therefore we need a proper correction method to obtain accurate distance information with RSSI. We develop the fuzzy distance models based on RSSI values and an efficient algorithm to estimate the robot location which applies to the navigation algorithm incorporating the time-varying data of environmental conditions which are received from the wireless sensor network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1295-1302
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• 2010

This paper proposed indoor location recognition method based on RSSI(received signal strength indication) using the LVQ network. In order to verify the effectiveness of the proposed method, we performed experiments, and then compared to the conventional triangularity measurement method. In the experiments, we set up the system to the laboratory, divided the 40 section, and installed 6 nodes as a reference node. We obtained the log-normal path loss model of wireless channels, RSSI converted into the distance. The distance values used as the input of LVQ. To learn the LVQ network, we set the target values as section indices. In the experiments, we determined the optimal number of subclass, and confirmed that the success rate of training phase was 96%, test phase was 91%.