• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3194-3210
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• 2022

Wall obstruction is the main factor leading to the non-line of sight (NLoS) error of indoor localization based on received signal strength indicator (RSSI). Modeling and correcting the path loss of the signals through the wall will improve the accuracy of RSSI localization. Based on electromagnetic wave propagation theory, the reflection and transmission process of wireless signals propagation through the wall is analyzed. The path loss of signals through wall is deduced based on power loss and RSSI definition, and the theoretical model of path loss of signals through wall is proposed. In view of electromagnetic characteristic parameters of the theoretical model usually cannot be accurately obtained, the statistical model of NLoS error caused by the signals through the wall is presented based on the log-distance path loss model to solve the parameters. Combining the statistical model and theoretical model, a hybrid model of path loss of signals through wall is proposed. Based on the empirical values of electromagnetic characteristic parameters of the concrete wall, the effect of each electromagnetic characteristic parameters on path loss is analyzed, and the theoretical model of regional path loss of signals through the wall is established. The statistical model and hybrid model of regional path loss of signals through wall are established by RSSI observation experiments, respectively. The hybrid model can solve the problem of path loss when the material of wall is unknown. The results show that the hybrid model can better express the actual trend of the regional path loss and maintain the pass loss continuity of adjacent areas. The validity of the hybrid model is verified by inverse computation of the RSSI of the extended region, and the calculated RSSI is basically consistent with the measured RSSI. The hybrid model can be used to forecast regional path loss of signals through the wall.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1841_1842
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• 2009

본 논문에서는 WSN(Wireless Sensor Network)에서 RSSI(Receive Strength Signal Indicator)를 이용해 미지노드의 위치측정을 위한 퍼지 모델링 기법을 제안한다. RSSI는 거리에 따른 전파의 감쇠를 나타내는 것으로 측정 환경에 따라 신호 반사 및 잡음의 영향에 민감하다. 본 논문에서는 퍼지를 이용하여 측정된 RSSI를 거리로 환산하고 가장 짧은 거리와 그에 따른 거리오차를 모델링한다. 출력으로 입력 거리에 따른 가중치를 얻은 뒤 가중치를 적용한 거리의 무게 중심을 구하고 실제 미지노드의 위치와 비교함으로써 제안한 기법이 응용 가능함을 증명한다.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.2
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• pp.67-77
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• 2008

Representation of various types of information in an interactive virtual reality environment on mobile devices had been an attractive and valuable research in this new era. Our main focus is presenting spatial indoor location sensing information in 3D perception in mind to replace the traditional 2D floor map using handheld PDA. Designation of 3D virtual reality by Virtual Reality Modeling Language (VRML) demonstrates its powerful ability in providing lots of useful positioning information for PDA user in real-time situation. Furthermore, by interpolating portal culling algorithm would reduce the 3D graphics rendering time on low power processing PDA significantly. By fully utilizing the CC2420 chipbased sensor nodes, wireless sensor network was established to locate user position based on Received Signal Strength Indication (RSSI) signals. Implementation of RSSI-based indoor tracking method is low-cost solution. However, due to signal diffraction, shadowing and multipath fading, high accuracy of sensing information is unable to obtain even though with sophisticated indoor estimation methods. Therefore, low complexity and flexible accuracy refinement algorithm was proposed to obtain high precision indoor sensing information. User indoor position is updated synchronously in virtual reality to real physical world. Moreover, assignment of magnetic compass could provide dynamic orientation information of user current viewpoint in real-time.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.70-75
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• 2022

Accurately measuring location is necessary to provide a variety of services. The data for indoor positioning measures the RSSI values from the WiFi device through an application of a smartphone. The measured data becomes the raw data of machine learning. The feature data is the measured RSSI value, and the label is the name of the space for the measured position. For this purpose, the machine learning technique is to study a technique that predicts the exact location only with the WiFi signal by applying an efficient technique to classification. Ensemble is a technique for obtaining more accurate predictions through various models than one model, including backing and boosting. Among them, Boosting is a technique for adjusting the weight of a model through a modeling result based on sampled data, and there are various algorithms. This study uses Xgboost among the above techniques and evaluates performance with other ensemble techniques.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.348-353
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• 2008

Despite the increasing popularity of wireless sensor network, indoor positioning using low power IEEE 802.15.4 compliant radio had attracted an interest of many researchers in the last decade. Old fashionable indoor location sensing information has been presented in dull and unpleasant 2D image standard. This paper focused on visualizing high precision 3 dimensional RSSI-based (received signal strength indication) spatial sensing information in an interactive virtual reality on PDA. The developed system operates by capturing and extracting signal strength information at multiple pre-defined reference nodes to provide information in the area of interest, thus updating user's location in 3D indoor virtual map. VRML (Virtual Reality Modeling Language) which specifically developed for 3D objects modeling is utilized to design 3D indoor environment.

• ETRI Journal
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• v.34 no.4
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• pp.625-628
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• 2012

In this letter, a novel method for localizing a user in a smart home environment is presented. We propose a two-level structure, in which the first level determines an occupant's location in the block level using RSSI in a ZigBee network, while the second level accurately estimates the occupant's location using a particle filter to handle the variations in the signal strength measurement. We devise an experimental setup with people performing significant tasks in the smart home. The results obtained from the testbed indicate that the proposed model leads to an improvement in the mean distance error.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.477-482
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• 2015

In this paper, an android platform with Unity engine was proposed for the effective 3D presentation of IoT sensor's position to IoT(Internet of Things) users. This android platform based home-gateway was designed with the cooperation of 3D unity engine for 3D texture according to MovieTexture simultaneously. As a result, the 3D presentation technology of IoT sensors was described with Unity based 3D modeling. In this proposed smart gateway, the 3D position was presented with the received RSSI(Received Signal Strength Indicator) and angle of IoT sensors. This 3D Unity launcher can be used for the 3D position, monitoring, and the life managing of IoT sensors with the beacon and 3 dimensional cube-style after the 3D conversion of 2D.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.4
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• pp.104-111
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• 2012

It is advantageous to avoid the handover to cell whose dwell time is short or can be ignored in terms of service continuity and average throughput. This paper proposes the handover scheme that is suitable for vehicle in order to improve the wireless Internet service quality. In the proposed scheme, the handover process continues to be learned before being modeled to Discrete-Time Markov Chain (DTMC). This modeling reduces the handover frequency by preventing the handover to cell that could provide service sufficiently to passenger even when vehicle passed through the cell but there was no need to perform handover. In order to verify the proposed scheme, we observed the average number of handovers, the average RSSI and the average throughput on various moving paths that vehicle moved in the given urban environment.