• Smart Media Journal
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• v.8 no.4
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• pp.17-24
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• 2019

As IoT technology is widely used in industrial environments, its environmental security issues are becoming more important. In such a context, studies utilizing hardware security functions are being actively carried out. However, previous studies did not consider the performance degradation that occurs when using hardware security functions in IoT environment. Gateway devices that are mainly used in IoT environments are often resource-limited. Utilizing hardware security in such an environment can cause serious performance degradation as the number of IoT devices connected to the gateway increases. Therefore, in this paper, we propose a data classification scheme to efficiently utilize hardware security functions in resource limited environment. We implement a platform with the proposed technique using ARM Trustzone. Performance degradation due to the hardware security functions is measured through experiments on the implemented platform and compared with the performance as of when the proposed technique is applied.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1301-1306
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• 2018

Artificial intelligence is one of the key technologies of the Fourth Industrial Revolution. This paper introduces the diverse kinds of approaches to subjects that tackle diverse kinds of research fields such as model-based MS approach, deep neural network model, image edge detection approach, cross-layer optimization model, LSSVM approach, screen design approach, CPU-GPU hybrid approach and so on. The research on Superintelligence and superconnection for IoT and big data is also described such as 'superintelligence-based systems and infrastructures', 'superconnection-based IoT and big data systems', 'analysis of IoT-based data and big data', 'infrastructure design for IoT and big data', 'artificial intelligence applications', and 'superconnection-based IoT devices'.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.17-26
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• 2023

A smart city is a massive internet of things (IoT) environment, where all terminal devices are connected to a network to create and share information. In accordance with massive IoT environments, millions of IoT devices are connected, and countless data are generated in real time. However, since heterogeneous IoT devices are used, collecting the logs for each IoT device is difficult. Due to these issues, when an IoT device is invaded or is engaged in malicious behavior, such as infection with malware, it is difficult to respond quickly, and additional damage may occur due to information leakage or stopping the IoT device. To solve this problem, in this paper, we propose identifying the attack technique used for initial access to IoT devices through MITRE ATT&CK, collect the logs that can be generated from the identified attack technique, and use them to identify the cause of malware infection.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.1-12
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• 2019

The role of Internet of Things (IoT) has been evolving from connectivity to intelligent and autonomous functions. The increase in the number of connected things and the volume of data has revealed the limit of cloud-based intelligent IoT. Meanwhile, the development of microprocessors for the IoT has enabled their intelligent decision making and reactions without the intervention of the cloud; this phase is referred to as the "autonomous IoT era." However, intelligence is not the only function of the IoT. When a cyber physical system (CPS) is running on the cloud, the real-time synchronization between the real and virtual worlds cannot be guaranteed. If a CPS is running on the IoT, both the worlds can be synchronized closely enough for a zero- time gap, i.e., achieving the goals of autonomous IoT. ETRI implements intelligence into the role of IoT and collaborates their decision making and reactions without the intervention of humans. Then, we focus on the development of a new IoT computing paradigm that enables human-like discussions.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.122-128
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• 2022

Minimization of human casualties in disaster situations is of paramount importance. In particular, if a marine disaster occurs, it can be directly connected to human casualties, so prompt action is needed. In the event of a marine disaster, the route and location of movement should be identified and life tubes should be used to float on the water. This paper designs and proposes an emergency IoT notification system that can quickly rescue drowning people. The maritime emergency IoT notification system consists of four main types. First, an emergency IoT device that detects the expansion of the life tube and delivers location and situation information to the emergency IoT notification server. Second, an emergency IoT web server that manages emergency information and provides notification. Third, a database server that stores and manages emergency IoT notification information. And finally, an emergency notification app that can receive and respond to emergency notification information. The emergency IoT device consists of a TPMS(Tube Pressure Monitoring System) device that checks the pressure value of the TPMS in real time and sends it to the IoT device, and an IoT device that sends the rescuer's voice information and emergency information to the emergency IoT server. Emergency information is delivered using the MQTT(Message Queuing Telemetry Transport) protocol, and voice information is delivered to the IoT server as HTTP FormData.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.21-29
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• 2015

The Internet of Things (IoT) is an Infrastructure which allows to connect with each device in physical world through the Internet. Thus IoT enables to provide meahup services or intelligent services to human user using collected data from those devices. Due to these advantages, IoT is used in divers service domains such as traffic, distribution, healthcare, and smart city. However, current IoT provides restricted services because it only supports monitor and control devices according to collected data from the devices. To resolve this problem, we propose a design and implementation of personalized IoT service base on service orchestration. The proposed service allows to discover specific services and then to combine the services according to a user location. To this end, we develop a service ontology to interpret user information according to meanings and smartphone web app to use the IoT service by human user. We also develop a service platform to work with external IoT platform. Finally, to show feasibility, we evaluate the proposed system via study.

• Journal of Advanced Navigation Technology
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• v.19 no.5
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• pp.417-422
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• 2015

A service size of the IoT environment is determined by the number of sensors. The number of sensors increase means increases the amount of data generated by the IoT environment. There are studies to reliably operate a network for research and operational dynamic buffer for data when network congestion control congestion in the network environment. There are also studies of the stream data that has been processed in the connectionless network environment. In this study, we propose a sensor gateway for processing big data of the IoT environment. For this, review the RESTful for designing a sensor middleware, and apply the double-buffer algorithm to process the stream data efficiently. Finally, it generates a big data traffic using the MJpeg stream that is based on the HTTP protocol over TCP to evaluate the proposed system, with open source media player VLC using the image received and compare the throughput performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.531-532
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• 2017

OneM2M's domain is divided into Device, Network, and Application domain, and data generated from various devices is collected through IoT/M2M gateway, it is delivered to appropriate IoT/M2M Infrastructure through Core/Access Network. However, access to the Core/Access Network is also required if the device transmit data to an Infrastructure located within the same area. In this paper, it propose data transmission based on region gateway that judge and transmit local information of data by adding a concept of region network domain to oneM2M domain, prevent access to unnecessary Core/Access Network.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.287-296
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• 2024

Artificial intelligence (AI), the Internet of Things (IoT), and Big Data are playing important roles in improving or upgrading energy efficiency. Furthermore, their roles in energy efficiency are expected to become more and more essential. This study conducted a bibliometric comparative analysis on the features in the articles on the AI, the IoT, and the Big Data in energy efficiency by using the Web of Science database and compared the features in their trends in article publications, citations, countries, research areas, journals, and funding agencies from 2012 to 2022. This study attempted to make significant contributions by shedding new light on the following features. Among the AI, the IoT, and the Big Data in energy efficiency, the most articles were published and the most article citations were received in the AI in energy efficiency. China was found out to be the most leading country. Engineering and computer science were revealed to be the first research area. IEEE Access and IEEE Internet of Things were ranked with first journal. National Natural Science Foundation of China was the first research funding agency concerning the articles published in the AI, the IoT, and the Big Data in energy efficiency from 2012 to 2022.