• Annual Conference of KIPS
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• 2021.11a
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• pp.1055-1058
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• 2021

2019년 산업재해 현황 분석 결과 복장, 보호구의 잘못 사용으로 사고가 발생한 비율이 20%로 높은 비율을 차지했고, 전체 사고자 중 두부 손상을 입은 비율이 41%로 가장 높은 비율을 보였다[1]. 고용 노동부가 발표한 '건설현장 추락위험 일제점검 결과(2021.7)'에서는 안전모 미착용 근로자가 32.6%를 차지하였다[2]. 우리는 ICT기술을 활용해 안전모의 기능개선 가능성을 확인하였고, 안전사고를 예방하고, 빠르게 감지할 수 있는 스마트 안전모를 개발하고자 하였다. 그리고 본 연구를 통해 IoT 센서들과 딥러닝 영상분석을 이용한 스마트 안전모 서비스는 작업 전 부정착용 방지, 작업 중 위험감지, 사고 발생 시 빠른 감지를 통한 신속한 대처를 목표로 하여, 안전한 작업환경을 만들 수 있는 가능성을 제시하고자 한다.

• Journal of KIISE
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• v.43 no.6
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• pp.710-717
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• 2016

Recently, several research studies on things abstraction framework have been proposed in order to implement the multi-service Internet of Things (IoT) system, where various IoT services share the thing devices. Distributed things abstraction has an IoT service duplication problem, which aggravates power consumption of mobile devices and network traffic. On the other hand, cloud server-centric things abstraction cannot cover real-time interactions due to long network delay. Fog server-centric things abstraction has limits in insufficient IoT interfaces. In this paper, we propose Smart Fog which is a fog server-centric things abstraction framework to resolve the problems of the existing things abstraction frameworks. Smart Fog consists of software modules to operate the Smart Gateway and three interfaces. Smart Fog is implemented based on IoTivity framework and OIC standard. We construct a smart home prototype on an embedded board Odroid-XU3 using Smart Fog. We evaluate the network performance and energy efficiency of Smart Fog. The experimental results indicate that the Smart Fog shows short network latency, which can perform real-time interaction. The results also show that the proposed framework has reduction in the network traffic of 74% and power consumption of 21% in mobile device, compared to distributed things abstraction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.437-446
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• 2017

70 percent of Korea's territory is covered with mountains, whose difficult conditions can cause damage to facilities. Recently, the demand for facilities related to outdoor activities including monorails has been on the rise, and such facilities are much more likely to become damaged. For this reason, a monitoring system applying IoT to mountainous areas was developed and its applicability is evaluated in this study. The current status of the existing mountainous facilities and monitoring systems were reviewed, and the current wired monitoring technology was analyzed. A scenario for IoT-based monitoring was developed, and then sensor nodes were developed, which include an RF-communication module and interface, power-supply and solar-cell. A testbed was set up at K University. The same data was collected by the wireless system as had been collected by the wired one. The study findings are as follows. Firstly, by using the wireless system, it is estimated that the construction duration can be reduced by about 25 percent, while the construction costs can be reduced by about 3~52 percent. Secondly, the safety of the construction workers can be improved by making the working conditions less dangerous, such as by eliminating the need to transport cables.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.221-228
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• 2017

With the recent development of IoT technology, medical services using IoT technology are increasing in many medical institutions providing health care services. However, as the number of IoT sensors attached to the user body increases, the healthcare information transmitted to the server becomes complicated, thereby increasing the time required for analyzing the user's healthcare information in the server. In this paper, we propose a deep learning based health care information management method to collect and process healthcare information in a server for a large amount of healthcare information delivered through a user - attached IoT device. The proposed scheme constructs a subnet according to the attribute value by assigning an attribute value to the healthcare information transmitted to the server, and extracts the association information between the subnets as a seed and groups them into a hierarchical structure. The server extracts optimized information that can improve the observation speed and accuracy of user's treatment and prescription by using deep running of grouped healthcare information. As a result of the performance evaluation, the proposed method shows that the processing speed of the medical service operated in the healthcare service model is improved by 14.1% on average and the server overhead is 6.7% lower than the conventional technique. The accuracy of healthcare information extraction was 10.1% higher than the conventional method.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.69-75
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• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.1-12
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• 2016

Internet of Things (IoTs) technologies have been computerized information generated from a variety of objects and humans, and have been applied to various fields by connecting via the Internet. In order to compensate for the marginal characteristics of IoT smart devices, such as low-power, limited processing and capacities, combining IoT and cloud computing technologies is now established itself as one of the paradigms. In this paper, we look at the definition, features and services of IoT and cloud computing technology, and we investigate and analyze the conversing needs of IoT and could computing, existing conversion paradigms, convergence cases, and platforms. In results, there are challenges to be solved, even though the cloud technologies complement a number of restrictions of IoT and offer various advantages such as scalability, interoperability, reliability, efficiency, availability, security, ease of access, ease of use, and reduced cost of deployment. We analyze the new research issues of convergence paradigm, and finally suggest a research challenges for convergence.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.119-128
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• 2013

As ICT technology becomes advanced, the importance of future internet is emphasized and in part of that, M2M (Machine-to Machine communications) is magnified in terms of importance and usage in public and private sector. M2M is emerging as a next generation strategic industry but there is no existing analyzed data or market classification, so it disrupts establishing policies on the M2M industry. As the technology is progressing, the evolution from M2M to IoT (Internet of Things) has started and many countries actively try to find technological trend through market analysis in order to develop new growth engine. Therefore, in order to strengthen competitiveness, we should secure differentiated capabilities in industry and service. This article examines Korea's domestic market and international market trends in IoT and analyses the economic impact of the IoT industry using quantitative methodology and evaluates relations between the IoT industry and other relevant industries. As a result, the effect of IoT industry on production inducement is KRW474.6 billion; the effect on value-added inducement is KRW314.7 billion; and it is measured that 3,628 jobs will be created by the IoT industry.

• 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.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.1-8
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• 2017

Recently, the technological development in the Internet of Things(IoT) field has grown rapidly worldwide and China is also expected to show a noticeable growth trend in this field. Therefore, the aim of this research is to analyze the recent trend of Chinese patents in the IoT field. China has been very active between 2010 and 2014, surpassing USA, and the this trend will accelerate in the future. The results of the analysis are as follows. First, the number of IoT-related patents in China increased rapidly from 2010 to 2014. Second, the major actors that led this quantitative increase are firms including ZTE and higher education institutes such as Nanjing University of Posts and Telecommunications. Lastly, major technological fields and the convergence networks amongst those fields were analyzed. As a result, it was found that the patenting activity in H04L was the most active and this field has led the convergence among other technological fields.

• Journal of Platform Technology
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• v.8 no.2
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• pp.22-31
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• 2020

The construction and operation of industrial automation has been actively taking place from manufacturing plan to production for improving operational efficiency of production line and flexibility of equipment. ISO/TC184 is standardizing on operating methods that can share information of programmable device controllers such as PLC and IoT that are geographically distributed in the production line. In this study, the design of the cell controller consists of PLC group and IoT group that perform signals such as temperature sensors, gas sensors, and pressure sensors for thermal processes and corresponding motors or valves. The operation and analysis of the cell controller were performed using SDN(Software Defined Network) and the three types of process services performed in thermal processes are real-time transmission service, loss-sensitive large-capacity transmission service, and normal transmission service. The simulation result showed that the average loss rate improved by about 17% when the traffic increased before and after the application of the SDN route technique, and the delay in the real-time service was as low as 1 ms.