• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.191-196
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• 2024

Recently, research on intelligent IoT technology has been actively conducted by various companies and research institutes to analyze various data collected from IoT devices and provide it through actual application services. However, security issues such as personal information leakage may arise in the process of transmitting and receiving data to use data collected from IoT devices for research and development. In addition, as data collected from multiple IoT devices increases, data management difficulties exist, and data movement is costly and time consuming. Therefore, in this paper, we intend to develop an Ethereum network-based federated learning system with guaranteed reliability to improve security issues and inefficiencies in a federated learning environment composed of various devices.

• Smart Media Journal
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• v.10 no.1
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• pp.88-98
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• 2021

As blockchain technology has emerged as a security issue for IoT, technology which integrates block chain into IoT is being studied. In this paper is a research concerning token-based IoT service access control technology for data sharing, which propose a possessor focused data sharing technic by using the permissioned blockchain. To share IoT health data, a Hyperledger Fabric Network consisting of three organizations was designed to provide a way to share data by applying different access control policies centered on device owners for different services. In the proposed system, the device owner issues access control tokens with different security levels applied to the participants in the organization, and the token issue information is shared through the distributed ledger of the HFN. In IoT, it is possible to lightweight the access control processing of IoT devices by granting tokens to service requesters who request access to data. Furthmore, by sharing token issuance information among network participants using HFN, the integrity of the token is guaranteed and all network participants can trust the token. The device owners can trust that their data is being used within their authorized rights, and control the collection and use of data.

• The Journal of Bigdata
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• v.6 no.1
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• pp.161-178
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• 2021

The importance of 'Big Data' is increasing to the point that it is likened to '21st century crude oil'. For smart city IoT data, attention should be paid to quality control as the quality of data is associated with the quality of public services. However, data quality indicators presented through ISO/IEC organizations and domestic/foreign organizations are limited to the 'User' perspective. To complement these limitations, the study derives supplier-centric indicators and their priorities. After deriving 3 categories and 13 indicators of supplier-oriented smart city IoT data quality evaluation indicators, we derived the priority of indicator categories and data quality indicators through AHP analysis and investigated the feasibility of each indicator. The study can contribute to improving sensor data quality by presenting the basic requirements that data should have to individuals or companies performing the task. Furthermore, data quality control can be performed based on indicator priorities to provide improvements in quality control task efficiency.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.57-62
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• 2021

As the parallel computing capability for artificial intelligence improves, the field of artificial intelligence technology is expanding in various industries. In particular, artificial intelligence is being introduced to process data generated from IoT sensors that have enoumous data. However, the limitation exists when applying the AI techniques on IoT network because IoT has time series data, where the importance of data changes over time. In this paper, we propose time-weighted and user-state based artificial intelligence processing techniques to effectively process IoT sensor data. This technique aims to effectively classify IoT sensor data through a data pre-processing process that personalizes time series data and places a weight on the time series data before artificial intelligence learning and use status of personal data. Based on the research, it is possible to propose a method of applying artificial intelligence learning in various fields.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.30-36
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• 2024

Recently, the Internet of Things (IoT), which has been applied to various industrial fields, is constantly evolving in the process of automation and digitization. However, in the network where IoT devices are built, research on IoT critical information-related data sharing, personal information protection, and data integrity among intermediate nodes is still being actively studied. In this study, we propose a blockchain-based IoT critical information management technique that is easy to implement without burdening the intermediate node in the network environment where IoT is built. The proposed technique allocates a random value of a random size to the IoT critical information arriving at the intermediate node and manages it to become a decentralized P2P blockchain. In addition, the proposed technique makes it easier to manage IoT critical data by creating licenses such as time limit and device limitation according to the weight condition of IoT critical information. Performance evaluation and proposed techniques have improved delay time and processing time by 7.6% and 10.1% on average compared to existing techniques.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.83-88
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• 2022

Internet of Things (hereinafter referred to as IoT) related technologies are continuously developing in line with the recent development of information and communication technologies. IoT system sends and receives unique data through network based on various sensors. Data generated by IoT systems can be defined as big data in that they occur in real time, and that the amount is proportional to the amount of sensors installed. Until now, IoT systems have applied data storage, processing and computation through centralized processing methods. However, existing centralized processing servers can be under load due to bottlenecks if the deployment grows in size and a large amount of sensors are used. Therefore, in this paper, we propose a distributed processing system for applying a data importance-based algorithm aimed at the high availability of the system to efficiently handle real-time sensor data arising in IoT environments.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.279-282
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• 2020

IoT 서비스는 이기종의 다양한 IoT 장치들로부터 수집된 데이터를 목적에 맞게 가공, 저장, 처리하여 사용자에게 서비스를 제공한다. 본 연구에서는 이기종의 IoT 서비스에서 공유 가능한 접근 제어를 위해 스마트 컨트렉트를 사용하고자 한다. 이를 위해 IoT 데이터 공유를 위하여 탱글 네트워크 환경에서 실행되는 스마트 컨트렉트(Smart Contract)를 사용한 IoT 서비스 접근 제어를 설계하였다. 본 연구를 통해 이기종의 다양한 사물인터넷 서비스들이 탱글 네트워크를 통해 스마트 컨트렉트를 공유함으로써, 중앙 제어 없이 IoT 데이터 접근이 안전하게 이루어 질 수 있다.

• Journal of Integrative Natural Science
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• v.13 no.3
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• pp.114-119
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• 2020

IoT 및 사물인터넷 기반 빅데이터 시스템을 구축하는 경우 발생하는 빈번한 전송에 따른 데이터 오류율과 자원의 비효율적 이용율을 극복하기 위하고 오픈소스기반 하둡시스템의 문제점을 극복하기 위한 본 연구에서는 순수 하둡을 기반으로 적용된 결과를 분석하고 하둡 2.x대 버전을 기준으로 빅데이터 시스템의 용량을 산정한 가이드를 제시하고 용량 산정의 기준을 에코 소프트웨어 적용 플랫폼을 제안한다.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.1-4
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• 2019

Edge Computing 환경에서는 데이터 처리와 시스템 제어를 위한 별도의 서버가 존재하지 않는다. 서버를 통한 중앙통제 방식이 아닌 Edge computing에 사용된 IoT기기들이 연동되어 데이터 분산 처리와 연산을 통해 전체 시스템이 동작된다. 이러한 Edge computing 시스템 구조 특성상 전체 시스템이 과부하를 피하기 위해 각 IoT 기기에서 동시다발적으로 감지되는 실시간 상황 정보를 효율적으로 처리 하여야한다. 이에 따라 실시간 상황 정보를 효율적으로 처리하거나, 다양한 데이터 분석처리 알고리즘들이 연구 개발되어 데이터 처리에 적용되어 왔다. 하지만 데이터의 정보 흐름과 타입에 초점을 맞춘 것이 아니라 예상분석 및 획일화된 알고리즘을 통해서 분석되기 때문에 해당 플랫폼이 주로 지향하는 데이터 형식에 맞지 않으면 성능저하를 수반하며 사용에 제약이 많은 문제점이 있다. 따라서 본 논문에서는 IoT 환경에서 실시간 반응성 향상을 목표로 오픈소스 기반 스트림 데이터 처리 방법에 대한 비교 분석과 Fast-reaction을 위한 데이터 처리 도구 비교 분석을 연구를 진행한다.