• Spatial Information Research
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• v.21 no.4
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• pp.35-45
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• 2013

Most multimedia contains location information whether they are implicit or explicitly, and which are very useful for several purposes. In particular, we may use location information in defining query conditions to retrieve relevant multimedia. For this reason, a number of works have been done to organize and retrieve geo-referenced multimedia data. However, they mostly focus on outdoor space where position is identified by (x, y, z) coordinates. In this paper, we focus on multimedia in an alternative space, indoor space, which differs from outdoor space in several aspects. First indoor space is considered as symbolic space, where location is identified by a symbolic code such as room number rather than coordinates. Second, topological information is a crucial element in providing indoor spatial information services. Third, indoor space is in more micro-scale than outdoor space, which influences on determining the visibility of cameras. Based on these different characteristics of indoor space, we survey the requirements of management systems of indoor geo-referenced multimedia. Then we propose a geo-coding scheme for multimedia in indoor space as an extension of IndoorGML, an OGC(Open Geospatial Consortium) candidate standard for indoor spatial information. We also present a prototype system called, IngC (INdoor Geo-Coding) developed to store and manage indoor geo-referenced multimedia.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2968-2974
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• 2014

When lighting has installed indoor, we control lighting using human-detecting sensors for people who pass at night and places that are lack of quantity of light. However, malfunction can be caused by positions of inappropriate sensors, and in the case of passages of big buildings, it is a problem that even after a person pass, light apparatuses are turned on for a long time. In this paper, we propose lighting control system based on location positioning for energy saving that control lighting in accordance with passers's position through indoor location positioning. This system use the fingerprinting technology that is one of the location positioning technologies and RSSI data that is collected by a smart device. Using those, it can turn on only lightings that are included in the positioned location and reduce unnecessary power consumption. As a result of experiment, on condition that four people were existing and illumination was 308 lux, we assured reduction effect of 49 Wh.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.149-155
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• 2019

In this paper, we propose an emergency call system which makes a call to an institution or a guardian when an urgent case occurs to a user based on the indoor location service. In addition, it establishes indoor location service infrastructure assuming welfare institutions and hospitals. The proposed system receives the sensor value from the device that is in the form of a clock on the wrist and determines the emergency situation and delivers emergency information to the terminal. The location terminal transmits location and emergency information to the server, and the server accesses the database and stores the data. This enables the caregiver to communicate with the server through the application, monitor the user's status, receive notifications, and respond to emergency situations by using the emergency call function. If the proposed system is applied to the fields requiring urgent action such as medical field and welfare field, it will provide more stable and prompt emergency service to users and carers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.300-308
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• 2012

Many indoor location tracking technologies have been proposed. Generally indoor location tracking using TOA signal is used, there is a weak point that it's difficult to track the location due to obstacles like a refraction, reflection and dispersion of radio wave. In this paper, we apply ETOA(Estimated-TOA) algorithm in NLOS(Non-Line-Of-Sight) environment to solve above problem. In NLOS environment, TOA value between Beacon and Mobile node is predicted by ETOA algorithm and the tracking of indoor location is also possible to identify using two NLOS beacons of three beacons by this algorithm. We show that the proposed algorithm is accurate location tracking is accomplished using the applying the proposed algorithm to indoor moving robot and the inertia sensor of robot and Kalman filter algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.156-170
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• 2019

Rapid increasing of smartphones has changed people's lifestyles, and location-based services are providing a platform to provide various conveniences in accordance with these changes. In particular, it may provide convenience to many users if location-based services are provided in an indoor area such as subway station. However, it is still a difficult task to ensure accurate positioning result for guiding routes in subway stations. This study proposes a KAI-R system that allows all processes to be performed in one system for indoor navigation in subway stations. The proposed system includes a new pedestrian step detection method for continuous positioning along with an improved fusion positioning algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.717-720
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• 2020

An indoor localization system that uses Wi-Fi RSSI signals for localization gives accurate user position results. The conventional Wi-Fi RSSI signal based localization system uses raw RSSI signals from access points (APs) to estimate the user position. However, the RSSI values of a particular location are usually not stable due to the signal propagation in the indoor environments. To reduce the RSSI signal fluctuations, shadow fading, multipath effects and the blockage of Wi-Fi RSSI signals, we propose a Wi-Fi localization system that utilizes the advantages of Wi-Fi RSSI heat maps. The proposed localization system uses a regression model with deep convolutional neural networks (DCNNs) and gives accurate user position results for indoor localization. The experiment results demonstrate the superior performance of the proposed localization system for indoor localization.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.241-243
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• 2015

Recently, There is increasing interest in the IoT(Internet of Thing) as intelligent information service that enables communication between people and things based on internet. In particular the demand for indoor location-based services with the development of smart devices is rapidly increasing. If iBeacon of BLE(Bluetooth Low Energy) is made available to provide a basic signal for the indoor location information measurement then reliability of Indoor location information for unreliable signal data for a variety of reasons, such as signal interference is significantly lowered. In this paper, Proposes a technique for obtaining an effective and reliable location information via genetic operations in order to obtain reliable location information from the iBeacon signal information.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.2
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• pp.179-186
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• 2015

In the indoor location estimation system, which has recently been actively studied, the received signal strength indicator contains a high level of noise when measuring the signal strength in the range between two nodes consisting of a receiver and a transceiver. To minimize the noise level, this paper proposes an improved adaptive smoothing filter that provides different exponential weights to the current value and previous averaged one of the data that were obtained from the nodes, because the characteristic signal attenuation of the received signal strength indicator generally has a log distribution. The proposed method can effectively decrease the noise level by using a feedback filter that can provide different weights according to the noise level of the obtained data and thus increase the accuracy in the distance and location without an additional filter such as the link quality indicator, which can verify the communication quality state to decrease the range errors in the indoor location recognition using ZigBee based on IEEE 802.15.4. For verifying the performance of the proposed improved adaptive smoothing filter, actual experiments are conducted in three indoor locations of different spatial sections. From the experimental results, it is verified that the proposed technique is superior to other techniques in range measurement.

• Journal of the Korean Society for Railway
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• v.19 no.5
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• pp.585-591
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• 2016

Recently, indoor location detection has become an important area in the IoT (Internet of Things) environment for various indoor location-based services. In this paper, our proposed method shows that a virtual region can be divided electromagnetically according to specific facilities or services in various IoT application areas called zones. The MLP (Multi-Layer Perceptron) method is applied to recognize the service zone at the current position. The MLP utilized an RSSI (Received Signal Strength Indicator) signal of the BLE (Bluetooth Low Energy) Beacon as input data and made decisions to affiliate the zone of the current region as output. In order to verify the proposed method, we constructed an experimental environment similar in size to an actual rail station using four of the beacon and two zones.

• 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.