• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.100-103
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• 2019

Due to the fourth industrial revolution called 'fusion and connection', interest in 'high connectivity society' and 'highland society' is increasing, and related objects are not limited to automation and connected cars. The Internet of Things is the main concern of the 4th Industrial Revolution and it is expected to play an important role in establishing the basis of the next generation mobile communication service. Several domestic and foreign companies have been studying various types of LPWANs for the construction of the Internet based on things, and there is Semtech's LoRaWAN technology as representative. LoRaWAN is a long-distance, low-power network designed to manage a large number of devices and sensors, with communications from hundreds to thousands to thousands of devices and sensors. In this paper, we analyze the optimum node capacity of gateway for maximum performance while reducing resource waste in using LoRaWAN.

• Food Science and Industry
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• v.54 no.3
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• pp.124-131
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• 2021

In the 4th industrial revolution, Artificial Intelligence (AI), big data, Internet of Things (IoT) are already around us, making our society hyper-connected and blurring the lines between the digital and biological spheres. We witness drastic changes not only in the food industry, but also in economy, society and our life as a whole. Technologies bring industrial reorganization and greater changes at the system level and the food industry is not exceptional. Human demand for foods continues to grow and the very nature of the food industry remains unchanged, but its production, distribution and marketing face unprecedent innovations. Passing through the global pandemic, the food industry has been evolved into 'contact-free', as the safety become our top priority. Amid the gradual shift to technology-oriented society, the smarter food safety management skills and tools are being adopted in many countries exerting greater efforts to enhance traceability and to upgrade AI-powered safety management system.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.127-143
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• 2023

Currently, the domestic traditional market has not escaped the swamp of stagnation that began in the early 2000s despite various projects promoted by many related players such as the central government and local governments. In order to overcome the crisis faced by the traditional market, various R&Ds have recently been conducted on how to build a smart traditional market that combines information and communication technologies such as big data analysis, artificial intelligence, and the Internet of Things. This study analyzes various previous studies, users of traditional markets, and application cases of ICT technology in foreign traditional markets since 2012 and proposes a model to build a smart traditional market using ICT technology based on the analysis. The model proposed in this study includes building a traditional market metaverse that can interact with visitors, certifying visits to traditional markets through digital signage with NFC technology, improving accuracy of fire detection functions using IoT and AI technology, developing smartphone apps for market launch information and event notification, and an e-commerce system. If a smart traditional market platform is implemented and operated based on the smart traditional market platform model presented in this study, it will not only draw interest in the traditional market to MZ generation and foreigners, but also contribute to revitalizing the traditional market in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.327-332
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• 2017

As Internet of Things (IoT) technology advances, there is a growing demand for location-based services (LBSs) to identify users' mobility and identity. The initial LBS system was mainly used to measure position information by measuring the phase of a signal transmitted from a global positioning system (GPS) satellite or by measuring distance to a satellite by tracking the code of a carrier signal. However, the use of GPS satellites is ineffective, because it is difficult to receive satellite signals indoors. Therefore, research on wireless communications systems like ultra-wide band (UWB), radio frequency identification (RFID), and ZigBee are being actively pursued for location recognition technology that can be utilized in an indoor environment. In this paper, we propose an LBS system that includes the 2.45GHz band for chirp spread spectrum (CSS), and the 3.1-10.6GHz band and the 250-750MHz bands for UWB using the IEEE 802.15.4a standard for low power-based location recognition. As a result, we confirmed that the 2.45GHz Industrial, Scientific and Medical (ISM) band RF transceiver and the ranging function can be realized in the hardware and has 0dBm output power.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.920-929
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• 2014

Recently, embedded system which demand is explosively increasing in the fields of communication, traffic, medical and industry facilities, expands to cyber physical system (CPS) which monitors and controls the networked embedded systems. In addition, internet of things(IoT) technology using wearable devices such as Google Glass, Samsung Galaxy Gear and Sony Smart Watch are gaining attention. In this situation, Samsung Smart Home and LG Home Chat are released one after another. However, since these services can be available only between smart phones and home appliances, there is a disadvantage that information cannot be passed to other terminals without commercial global messaging server. In this paper, to solve above issues, we propose the structure of an indoor location network based on unit space, which prevents the information of the devices or each individual person from leaking to outside and can selectively communicate to all existent terminals in the network using IoT chatting. Also, it is possible to control general devices and prevent external leakage of private information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2093-2099
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• 2015

To prevent damage to the cargo, monitoring and remote management for reefer containers is necessary. The currently used remote monitoring service is the Power Cable Transmission(PCT) system, which is recommended by the International Maritime Organization(IMO). However, this system is not widely used because it requires a separate PCT infrastructure and is susceptible to data loss problems. To solve this problem, this study introduces the "IoT-based reefer container management system", The proposed system which is attached to reefer container collects and transmits data on the temperature, status and location of reefer container to middleware using RS-232 communication and WCDMA/GSM communication. Middleware is store the data received in the database and provide information to user in real time through the web and mobile program. At this time, users able to change setting temperature in real time from a distant place through the web program. This study tested by transit about shipment of strawberries to monitor and analyze and check the system's overall effectiveness.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1494-1507
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• 2018

Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.5
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• pp.1149-1155
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• 2017

In these days, cloud environments such as cloud platforms, cloud services like Amazon AWS, IBM Bluemix are used in the Internet of Things for providing efficient services. These cloud platform environments have various security threats according to increasing of use, so the recent research results on cloud security and privacy protection technologies and related regimes and legislations are written in this paper and we suggest prospect of research on cloud platform environment security and privacy preserving.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1196-1213
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• 2020

There is a growing interest in the development of smart environments through predicting the behaviors of inhabitants of smart spaces in the recent past. Various smart services are deployed in modern smart cities to facilitate residents and city administration. Prediction algorithms are broadly used in the smart fields in order to well equip the smart services for the future demands. Hence, an accurate prediction technology plays a vital role in the smart services. In this paper, we take out an extensive survey of smart spaces such as smart homes, smart farms and smart cars and smart applications such as smart health and smart energy. Our extensive survey is based on more than 400 articles and the final list of research studies included in this survey consist of 134 research papers selected using Google Scholar database for period of 2008 to 2018. In this survey, we highlight the role of prediction algorithms in each sub-domain of smart Internet of Things (IoT) environments. We also discuss the main algorithms which play pivotal role in a particular IoT subfield and effectiveness of these algorithms. The conducted survey provides an efficient way to analyze and have a quick understanding of state of the art work in the targeted domain. To the best of our knowledge, this is the very first survey paper on main categories of prediction algorithms covering statistical, heuristic and hybrid approaches for smart environments.