• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.93-102
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• 2023

Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors. An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation. Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the number of input data on performance was confirmed.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.395-400
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• 2019

The CBS and the AEAS functionalities are defined in cellular systems and T-DMB systems, respectively. In the case that communication facilities are disabled in indoor environments, it is impossible for the residents to receive the emergency messages. In this paper, a novel collaborative technology of intelligent mobile robots is proposed, which relies on cooperative communications among the intelligent mobile robots. In order to improve the performance, the intelligent mobile robots exploit their location information. Simulation results confirm that the proposed method is very suitable for reliable emergency alert broadcast.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.2
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• pp.187-194
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• 2015

Accurate relative location estimation is a key requirement in indoor localization systems based on wireless sensor networks (WSNs). However, although these systems have applied not only various optimization algorithms but also fusion with sensors to achieve high accuracy in position determination, they are difficult to provide accurate relative azimuth and locations to users because of cumulative errors in inertial sensors with time and the influence of external magnetic fields. This paper based on ultra-wideband positioning system, which is relatively suitable for indoor localization compared to other wireless communications, presents an indoor localization system for estimating relative azimuth and location of location-unaware nodes, referred to as target nodes without applying any algorithms with complex variable and constraints to achieve high accuracy. In the proposed method, the target nodes comprising three mobile nodes estimate the relative distance and azimuth from two reference nodes that can be installed by users. In addition, in the process of estimating the relative localization information acquired from the reference nodes, positioning errors are minimized through a novel modified rounding estimation technique in which Kalman filter is applied without any time consumption algorithms. Experimental results show the feasibility and validity of the proposed system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.494-500
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• 2020

This paper proposes a new distance estimation technique for indoor localization in ultra wideband (UWB) systems. The proposed technique is based on recurrent neural network (RNN), one of the deep learning methods. The RNN is known to be useful to deal with time series data, and since UWB signals can be seen as a time series data, RNN is employed in this paper. Specifically, the transmitted UWB signal passes through IEEE802.15.4a indoor channel model, and from the received signal, the RNN regressor is trained to estimate the distance from the transmitter to the receiver. To verify the performance of the trained RNN regressor, new received UWB signals are used and the conventional threshold based technique is also compared. For the performance measure, root mean square error (RMSE) is assessed. According to the computer simulation results, the proposed distance estimator is always much better than the conventional technique in all signal-to-noise ratios and distances between the transmitter and the receiver.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1123-1128
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• 2016

Recently BLE beacon has been widely used as a method for measuring the indoor location in the IoT Technique. But it requires a filtering technique for the measurement of the correct position. It is used the most fixed beacon. It is not accurate that calculates the position information through the identification of the beacon signal. Therefore, filtering is important. So it takes a lot of time, position measurement and filtering. Thus, we is to measure the exact position at the indoor using a mobile beacon. The measured beacon signal is composed of an ontology for reuse in the same pattern. RSSI is measured the receiver is the distance of the beacon. And this value configure the location ontology to be normalized by the relationship analysis between the values. The ontology is a method for calculating the position information of the moving beacon. It can detect fast and accurate indoor position information and provide the service.

• Journal of Communications and Networks
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• v.7 no.2
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• pp.157-170
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• 2005

The combination of multiple antennas and multi-carrier code division multiple-access (MC-CDMA) is a strong candidate for the downlink of the next generation mobile communications. The study of such systems in scenarios that model real-life trans-missions is an additional step towards an optimized achievement. We consider a realistic MIMO channel with two or four transmit antennas and up to two receive antennas, and channel state information (CSI) mismatches. Depending on the mobile terminal (MT) class, its number of antennas or complexity allowed, different data-rates are proposed with turbo-coding and asymptotic spectral efficiencies from 1 to 4.5 bit/s/Hz, using three algorithms developed within the European IST-MATRICE project. These algorithms can be classified according to the degree of CSI at base-station (BS): i) Transmit space-frequency prefiltering based on constrained zero-forcing algorithm with complete CSI at BS; ii) transmit beamforming based on spatial correlation matrix estimation from partial CSI at BS; iii) orthogonal space-time block coding based on Alamouti scheme without CSI at BS. All presented schemes require a reasonable complexity at MT, and are compatible with a single-antenna receiver. A choice between these algorithms is proposed in order to significantly improve the performance of MC-CDMA and to cover the different environments considered for the next generation cellular systems. For beyond-3G, we propose prefiltering for indoor and pedestrian microcell environments, beamforming for suburban macrocells including high-speed train, and space-time coding for urban conditions with moderate to high speeds.

• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.6-9
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• 2008

In future domestic context aware applications the location of mobile devices is often required. Ultrasonic technology enables high resolution indoor position measurements. A disadvantage of state-of-art ultrasonic systems is that several base stations are required to estimate 3D position. This study aims to evaluate the efficiency and effectiveness of using UPS as a 3D free-hand writing or drawing tool. The processes include the design and testing of UPS as an efficient 3D free-hand writing or drawing tool in the air. The paper will further explain the system architecture of the UPS and how to use GPS as 3D free-hand writing or drawing tool. The efficiency and effectiveness of the system was confirmed by a computer software simulation. The software will further display the result of drawing or writing from the user by graphics. As a result, it is possible to implement UPS as a 3D free-hand writing or drawing tool in the air.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.508-522
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• 2011

Femtocell is an economical solution to provide high speed indoor communication instead of the conventional macro-cellular networks. Especially, OFDMA femtocell is considered in the next generation cellular network such as 3GPP LTE and mobile WiMAX system. Although the femtocell has great advantages to accommodate indoor users, interference management problem is a critical issue to operate femtocell network. Existing OFDMA resource management algorithms only consider optimizing system-centric metric, and cannot manage the co-channel interference. Moreover, it is hard to cooperate with other femtocells to control the interference, since the self-configurable characteristics of femtocell. This paper proposes a novel interference-aware resource allocation algorithm for OFDMA femtocell networks. The proposed algorithm allocates resources according to a new objective function which reflects the effect of interference, and the heuristic algorithm is also introduced to reduce the complexity of the original problem. The Monte-Carlo simulation is performed to evaluate the performance of the proposed algorithm compared to the existing solutions.

• Journal of Satellite, Information and Communications
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• v.11 no.2
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• pp.69-73
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• 2016

In order to derive reliable propagation models for future millimeter-wave frequency indoor pico-cellular communications systems, accurate reflectivity data of building materials is necessary. The broad variety of building materials and construction codes makes accurate attenuation prediction very difficult without the support of specific construction data or measurements. This paper derives a transmission and reflection coefficient based on 13 GHz to 28 GHz measurement data. Transmission and reflection is measured by applying change in the reception angle of each building material, such as plasterboard. The transmission and reflection coefficient derived shows a correlation between frequency dependence and angle. As a result, as the reception angle is reduced, the reflected angle from the transmitter that could be received increases, showing that there is a correlation. In addition, the fundamental investigations carried out lay the foundation for radio channel-related research, which is essential for the development of future millimeter-wave communications systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.3
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• pp.794-814
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• 2012

We propose a novel method for improving Wi-Fi positioning accuracy while reducing the energy consumption of mobile devices. Our method presents three contributions. First, we jointly and intelligently select the optimal subset of access points for positioning via maximum mutual information criterion. Second, we further propose local discriminant embedding algorithm for nonlinear discriminative feature extraction, a process that cannot be effectively handled by existing linear techniques. Third, to reduce complexity and make input signal space more compact, we incorporate clustering analysis to localize the positioning model. Experiments in realistic environments demonstrate that the proposed method can lower energy consumption while achieving higher accuracy compared with previous methods. The improvement can be attributed to the capability of our method to extract the most discriminative features for positioning as well as require smaller computation cost and shorter sensing time.