• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.349-352
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• 2020

클라우드는 가상화 기술을 사용한 리소스의 유연성과 뛰아난 접근성을 장점으로 빅데이터, 딥러닝 등 여러 분야에서 클라우드를 사용하고 있다. 최근 클라우드와 결합된 IoT 시스템을 통해 시스템 관리, 데이터 처리 및 저장, 데이터를 이용한 빅데이터 활용 등 여러 방법으로 사용 할 수 있어 많은 관심을 받고 있다. 그러나 IoT 클라우드의 많은 활용에 따라 대규모 시스템화, 여러 사용자의 개인정보 저장 등의 이유로 많은 공격자의 표적이 되고있다. 여러 공격자의 공격을 방아하기 위해 IoT 클라우드 시스템은 블록체인, 보안 IoT 디바이스, 변형된 클라우드 모델등 여러 연구가 진행되고 있다. 본 논문에서는 최근 연구되고 있는 블록체인, 클라우드, IoT 시스템의 동향에 대해 조사하고, 기존에 연구되었던 기술을 바탕으로 효과적인 블록체인 기반의 IoT 클라우드 시스템을 제안한다. 제안하는 IoT 클라우드 시스템은 블록체인 기술을 사용하여 보안정책을 관리할 수 있어 신뢰성이 높으며, 클라우드 시스템이 작동하지 않을 경우 페일오버 기능을 수행할 수 있어 가용성이 뛰어나다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1688-1696
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• 2020

The paradigm of Internet-of-things(IoT) systems is changing from a cloud-based system to an edge-based system to solve delays caused by network congestion, server overload and security issues due to data transmission. However, edge-based IoT systems have fatal weaknesses such as lack of performance and flexibility due to various limitations. To improve performance, application-specific hardware can be implemented in the edge device, but performance cannot be improved except for specific applications due to a fixed function. This paper introduces a edge-centric metamorphic IoT(mIoT) platform that can use a variety of hardware through on-demand partial reconfiguration despite the limited hardware resources of the edge device, so we can increase the performance and flexibility of the edge device. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to previous studies in which the reconfiguration algorithm was executed on the server.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.77-84
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• 2024

Over the past few years, IoT edges have begun to emerge based on new low-latency communication protocols such as 5G. However, IoT edges, despite their enormous advantages, pose new complementary threats, requiring new security solutions to address them. In this paper, we propose a cloud environment-based IoT edge architecture model that complements IoT systems. The proposed model acts on machine learning to prevent security threats in advance with network traffic data extracted from IoT edge devices. In addition, the proposed model ensures load and security in the access network (edge) by allocating some of the security data at the local node. The proposed model further reduces the load on the access network (edge) and secures the vulnerable part by allocating some functions of data processing and management to the local node among IoT edge environments. The proposed model virtualizes various IoT functions as a name service, and deploys hardware functions and sufficient computational resources to local nodes as needed.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.137-142
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• 2021

The Internet of Things (IoT) paradigm is at the forefront of present and future research activities. The huge amount of sensing data from IoT devices needing to be processed is increasing dramatically in volume, variety, and velocity. In response, cloud computing was involved in handling the challenges of collecting, storing, and processing jobs. The fog computing technology is a model that is used to support cloud computing by implementing pre-processing jobs close to the end-user for realizing low latency, less power consumption in the cloud side, and high scalability. However, it may be that some resources in fog computing networks are not suitable for some kind of jobs, or the number of requests increases outside capacity. So, it is more efficient to decrease sending jobs to the cloud. Hence some other fog resources are idle, and it is better to be federated rather than forwarding them to the cloud server. Obviously, this issue affects the performance of the fog environment when dealing with big data applications or applications that are sensitive to time processing. This research aims to build a fog topology job scheduling (FTJS) to schedule the incoming jobs which are generated from the IoT devices and discover all available fog nodes with their capabilities. Also, the fog topology job placement algorithm is introduced to deploy jobs into appropriate resources in the network effectively. Finally, by comparing our result with the state-of-art first come first serve (FCFS) scheduling technique, the overall execution time is reduced significantly by approximately 20%, the energy consumption in the cloud side is reduced by 18%.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.65-71
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• 2017

Since existing Internet of Things(IoT) is vulnerable, it may cause property damage due to the information leakage. Especially, the smart door lock system built on the IoT can cause more damage. To solve these problems, this paper classify the data generated by the sensor according to the condition and send an alarm message to the user's smartphone through Google Cloud Message (GCM). We made it possible to check the images in real time through the smartphone application and control the door lock using the TCP / IP protocol. Also, we applied OTP-Based Matrix SEED algorithm to door lock system to improve security.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.107-117
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• 2017

Recently, IoT applications are being deployed in Smart TVs, and these IoT applications are using smart TVs as application platforms rather than broadcast platforms. With the advent of Hybrid broadcast technologies, now it becomes available to develop IoT applications which are coupled to the broadcast information. However, the existing IoT services are not suitable for Hybrid broadcast application since they are built on cloud and require various protocol implementations. In this paper, a Fog Computing-based architecture for hybrid broadcast application is proposed. Instead of accessing IoT services from hybrid broadcast app directly, the proposed architecture places Fog Applet Server between them and distribute loads of hybrid broadcast app to the Fog Applet. The proposed architecture is implemented as a service to control IoT device with hybrid application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.281-298
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• 2020

Fog computing has become a popular concept in the application of internet of things (IoT). With the superiority in better service providing, the edge cloud has become an attractive solution to IoT networks. The data outsourcing scheme of IoT devices demands privacy protection as well as computation verification since the lightweight devices not only outsource their data but also their computation. Existing solutions mainly deal with the operations over encrypted data, but cannot support the computation verification in the same time. In this paper, we propose a data outsourcing scheme based on an encrypted database system with linear computation as well as efficient query ability, and enhance the interlayer program in the original system with homomorphic message authenticators so that the system could perform computational verifying. The tools we use to construct our scheme have been proven secure and valid. With our scheme, the system could check if the cloud provides the correct service as the system asks. The experiment also shows that our scheme could be as effective as the original version, and the extra load in time is neglectable.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.491-494
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• 2018

Radio base stations, which are widely distributed across large areas, have many difficulties in managing them. Unmanned radio base stations in remote mountains are having a hard time accessing them in case of emergencies. Major telephone service providers only remotely control incoming and outgoing information and local small business partners responsible for maintaining actual facilities do not possess such technologies, so they are each checked during field visits. In this study, in order to monitor temperature, humidity, fire condition, and power operation at a wide range of radio base stations, real-time monitoring is carried out at the office of Klaus server through real-time monitoring.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.355-357
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• 2019

Now that we are entering an aging society, the user interface for new devices and IoT technology is very inconvenient for senior generation. To improve this, we propose an AI technology-based intelligent senior assistant voice recognition system. This system implements Cloud platform based API to accumulate data for machine learning processing, provides content for diagnosis and prevention of dementia, and provide chat-bot content for senior generation. We hope that senior generations will increase the accessibility and convenience of IoT devices and new technology devices with our system.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.75-83
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• 2018

Predictive IoT Sensor Algorithm is a technique of data science that helps computers learn from existing data to predict future behaviors, outcomes, and trends. This algorithm is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. Sensors and computers collect and analyze data. Using the time series prediction algorithm helps to predict future temperature. The application of this IoT in industrial environments like power plants and factories will allow organizations to process much larger data sets much faster and precisely. This rich source of sensor data can be networked, gathered and analyzed by super smart software which will help to detect problems, work more productively. Using predictive IoT technology - sensors and real-time monitoring - can help organizations exactly where and when equipment needs to be adjusted, replaced or how to act in a given situation.