• Smart Media Journal
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• v.12 no.7
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• pp.27-42
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• 2023

The development of cloud services and IoT technology has radically changed the cloud environment, and has evolved into a new concept called fog computing and F2C (fog-to-cloud). However, as heterogeneous cloud/fog layers are integrated, problems of access control and security management for end users and edge devices may occur. In this paper, an F2C-based IoT smart health monitoring system architecture was designed to operate a medical information service that can quickly respond to medical emergencies. In addition, a role-based service access control technology was proposed to enhance the security of user's personal health information and sensor information during service interoperability. Through simulation, it was shown that role-based access control is achieved by sharing role registration and user role token issuance information through blockchain. End users can receive services from the device with the fastest response time, and by performing service access control according to roles, direct access to data can be minimized and security for personal information can be enhanced.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.269-275
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• 2022

Public healthcare has recently become an issue of great importance due to the exponential growth in the human population, the increase in medical expenses, and the COVID-19 pandemic. Speed is one of the crucial factors in saving life, particularly in case of heart attack. Therefore, a healthcare device is needed to continuously monitor and follow up heart health conditions remotely without the need for the patient to attend a medical center. Therefore, this paper proposes a portable electrocardiogram (ECG) monitoring system to improve healthcare for heart attack patients in both home and ambulance settings. The proposed system receives the ECG signals of the patient and sends the ECG values to a MySQL database on the IoT-cloud via Wi-Fi. The signals are displayed as an ECG data chart on a webpage that can be accessed by the patient's doctor based on the HTTP protocol that is employed in the IoT-cloud. The proposed system detects the ECG data of the patient to calculate the total number of heartbeats, number of normal heartbeats, and the number of abnormal heartbeats, which can help the doctor to evaluate the health status of the patient and decide on a suitable medical intervention. This system therefore has the potential to save time and life, but also cost. This paper highlights the five main advantages of the proposed ECG monitoring system and makes some recommendations to develop the system further.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.828-840
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• 2014

There is a new evolution in technological advancement taking place called the Internet of Things (IoT). The IoT enables physical world objects in our surroundings to be connected to the Internet. For this idea to come to life, two architectures are required: the Sensing Entity in the environment which collects data and connects to the cloud and the Cloud Service that hosts the data. In particular, the combination of wireless sensor network for sensing and cloud computing for managing sensor data is becoming a popular intervention for the IoT era. The pharmaceutical cold chain requires controlled environmental conditions for the sensitive products in order for them to maintain their potency and fit for consumption. The monitoring of distribution process is the only assurance that a process has been successfully validated. The distribution process is so critical that anomaly at any point will result in the process being no longer valid. Taking the cold chain monitoring to IoT and using its benefits and power will result in better management and product handling in the cold chain. In this paper, Arduino based wireless sensor network for storage and logistics (land and sea) is presented and integrated with Xively cloud service to offer a real-time and innovative solution for pharmaceutical cold chain monitoring.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.87-94
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• 2019

IoT-based services are being released in accordance with the aging population and the demand for well-being pursuit needs. In addition to medical device companies, companies with ideas ranging from global ICT companies to startup companies are accelerating their market entry. The areas where these services are most commonly applied are health/medical, life/safety, city/energy, automotive and transportation. Furthermore, by expanding IoT technology convergence into the area of life care services, it contributes greatly to the development of service models in the public sector. It also provides an important opportunity for IoT-related companies to open up new markets. By addressing the problems of life care services that are still insufficient. We are providing opportunities to pursue the common interests of both users and workers and improve the quality of life. In order to establish IoT-based digital life care services, it is necessary to develop convergence technologies using cloud computing systems, big data analytics, medical information, and smart healthcare infrastructure.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.41-56
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• 2019

Cloud manufacturing is a new concept of manufacturing process that works like a single factory with connected multiple factories. The cloud manufacturing system is a kind of large-scale CPS that produces products through the collaboration of distributed manufacturing facilities based on technologies such as cloud computing, IoT, and virtualization. It utilizes diverse and distributed facilities based on centralized information systems, which allows flexible composition user-centric and service-oriented large-scale systems. However, the cloud manufacturing system is composed of a large number of highly heterogeneous subsystems. It has difficulties in interconnection, data exchange, information processing, and system verification for system construction. In this paper, we derive the user requirements of various aspects of the cloud manufacturing system, such as functional, human, trustworthiness, timing, data and composition, based on the CPS Framework, which is the analysis methodology for CPS. Next, by analyzing the user requirements we define the system requirements including scalability, composability, interactivity, dependability, timing, interoperability and intelligence. We map the defined CPS system requirements to the requirements of oneM2M, which is the platform standard for IoT, so that the support of the system requirements at the level of the IoT platform is verified through Mobius, which is the implementation of oneM2M standard. Analyzing the verification result, finally, we propose a large-scale cloud manufacturing platform based on oneM2M that can meet the cloud manufacturing requirements to support the overall features of the Cloud Manufacturing CPS with dependability.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.5
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• pp.301-308
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• 2021

It is important to develop a digital SOC (Social Overhead Capital) maintenance system for preemptive maintenance in response to the rapid aging of social infrastructures. Abnormal signals induced from structures can be detected quickly and optimal decisions can be made promptly using IoT sensors deployed on the structures. In this study, a digital SOC monitoring system incorporating a multimetric IoT sensor was developed for long-term monitoring, for use in cloud-computing server for automated and powerful data analysis, and for establishing databases to perform : (1) multimetric sensing, (2) long-term operation, and (3) LTE-based direct communication. The developed sensor had three axes of acceleration, and five axes of strain sensing channels for multimetric sensing, and had an event-driven power management system that activated the sensors only when vibration exceeded a predetermined limit, or the timer was triggered. The power management system could reduce power consumption, and an additional solar panel charging could enable long-term operation. Data from the sensors were transmitted to the server in real-time via low-power LTE-CAT M1 communication, which does not require an additional gateway device. Furthermore, the cloud server was developed to receive multi-variable data from the sensor, and perform a displacement fusion algorithm to obtain reference-free structural displacement for ambient structural assessment. The proposed digital SOC system was experimentally validated on a steel railroad and concrete girder bridge.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.3
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• pp.139-150
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• 2015

The major components of IoT(Internet of Things) environment are IoT devices rather than the conventional desktop computers. One of the intrinsic characteristics of IoT devices is diversity in view of data type and data access method. In addition, IoT devices usually deal with real-time data. In order to use such IoT data for internal business or cloud services, an IoT platform capable of easy domain management and consistent data access interface is required. This paper proposes a Linux FUSE-based virtual file system connecting IoT devices on POSIX file system view. It is possible to manage IoT domain with the native Linux utilities such as mkdir, mknod, ls and find in the file system. Also, the file system makes it possible to access or control IoT devices through POSIX interface such as open(), read(), write() or close() without any separate APIs or utilities. A test result shows that the management performance of the file system is lower than that of linux file system negligibly.

• Journal of the Korea Society of Computer and Information
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• v.22 no.9
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• pp.91-97
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• 2017

IoT (Internet of Things) systems are based on heterogeneous hardware systems of different types of devices interconnected each other, ranging from miniaturized and low-power wireless sensor node to cloud servers. These IoT systems composed of heterogeneous hardware utilize data sets collected from a particular set of sensors or control designated actuators when needed using open APIs created through abstraction of devices' resources associated to service applications. However, previously existing IoT services have been usually developed based on vertical platforms, whose sharing and exchange of data is limited within each industry domain, for example, healthcare. Such problem is called 'data silo', and considered one of crucial issues to be solved for the success of establishing IoT ecosystems. Also, IoT services may need to dynamically organize their services according to the change of status of connected devices due to their mobility and dynamic network connectivity. We propose a way of dynamically composing IoT services under the concept of WoT (Web of Things) where heterogeneous devices across different industries are fully integrated into the Web. Our approach allows developers to create IoT services or mash them up in an efficient way using Web objects registered into multiple standardized horizontal IoT platforms where their resources are discoverable and accessible. A Web-based service composition tool is developed to evaluate the practical feasibility of our approach under real-world service development.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.128-130
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• 2021

Smart cities collect data from thousands of IoT-based sensor devices for intelligent application-based services. Centralized cloud servers support application tasks with higher computation resources but introduce network latency. Fog layer-based data centers bring data processing at the edge, but fewer available computation resources and poor task allocation strategy prevent real-time data analysis. In this paper, tasks generated from devices are distributed as high resource and low resource intensity tasks. The novelty of this research lies in deploying a virtual node assigned to each cluster of IoT sensor machines serving a joint application. The node allocates tasks based on the task intensity to either cloud-computing or fog computing resources. The proposed Task Allocation Strategy provides seamless allocation of jobs based on process requirements.