• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.127-132
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• 2022

As the IoT technology is more developed, it is more important for the accuracy of IoT data. Since the IoT data supports a different formats and protocols, it is often happened that the IoT system is failed or the incorrect data is generated with the unreliable IoT devices(sensor, actuator). Because the abnormality of IoT device or the user situation is not detected correctly, this problem makes the user to be unsatisfied with the IoT system. This study proposes the decision methodology of IoT data consistency whether the IoT data is generated in normal range or not by using the mathematical functions('gradient descent function' and 'linear regression function'). It may be concluded that the gradient function method is suitable for the IoT data which the 'increasing velocity' is related with the next generated pattern(eg. sensor devices), the linear regression function method is suitable for the IoT data which the 'the difference from linear regression function' is related with the next generated pattern in case the data has a linear pattern(eg. water meter, electric meter).

• Journal of Digital Convergence
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• v.19 no.11
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• pp.351-357
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• 2021

IoT devices, which are used in various ways in distributed environments, are becoming more important in data transmitted and received from IoT devices as fields of use such as medical, environment, transportation, bio, and public places are diversified. In this paper, as a method to ensure the reliability of IoT data, an autoencoder-based IoT-linked processing technique is proposed to classify and process numerous data by various important attributes. The proposed technique uses correlation indices for each IoT data so that IoT data is grouped and processed by blockchain by characteristics for IoT linkage processing based on autoencoder. The proposed technique expands and operates into a blockchain-based n-layer structure applied to the correlation index to ensure the reliability of IoT data. In addition, the proposed technique can not only select IoT data by applying weights to IoT collection data according to the correlation index of IoT data, but also reduce the cost of verifying the integrity of IoT data in real time. The proposed technique maintains the processing cost of IoT data so that IoT data can be expanded to an n-layer structure.

• Journal of Industrial Convergence
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• v.20 no.3
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• pp.33-40
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• 2022

Recently, IoT-linked services have been used in various environments, and IoT and artificial intelligence technologies are being fused. However, since technologies that process IoT data stably are not fully supported, research is needed for this. In this paper, we propose a processing technique that can optimize IoT data after generating embedded vectors based on machine learning for IoT data. In the proposed technique, for processing efficiency, embedded vectorization is performed based on QR such as index of IoT data, collection location (binary values of X and Y axis coordinates), group index, type, and type. In addition, data generated by various IoT devices are integrated and managed so that load balancing can be performed in the IoT data collection process to asymmetrically link IoT data. The proposed technique processes IoT data to be orthogonalized based on hash so that IoT data can be asymmetrically grouped. In addition, interference between IoT data may be minimized because it is periodically generated and grouped according to IoT data types and characteristics. Future research plans to compare and evaluate proposed techniques in various environments that provide IoT services.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.325-331
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• 2022

As IoT devices are used in various ways in an edge network environment, multiple studies are being conducted that utilizes the information collected from IoT devices in various applications. However, it is not easy to apply accurate IoT data immediately as IoT data collected according to network environment (interference, interference, etc.) are frequently missed or error occurs. In order to minimize mistakes in IoT data collected in an edge network environment, this paper proposes a management technique that ensures the reliability of IoT data by randomly generating signature values of IoT data and allocating only Security Information (SI) values to IoT data in bit form. The proposed technique binds IoT data into a blockchain by applying multiple hash chains to asymmetrically link and process data collected from IoT devices. In this case, the blockchainized IoT data uses a probability function to which a weight is applied according to a correlation index based on deep learning. In addition, the proposed technique can expand and operate grouped IoT data into an n-layer structure to lower the integrity and processing cost of IoT data.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.8-14
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• 2019

Due to the development of computer technology, IoT technology is being used in various fields of industry, economy, medical service and education. However, multimedia information processed through IoT equipment is still one of the major issues in the application sector. In this paper, a big data protection model for users of IoT based IoT is proposed to ensure integrity of users' multimedia information processed through IoT equipment. The proposed model aims to prevent users' illegal exploitation of big data information collected through IoT equipment without users' consent. The proposed model uses signatures and authentication information for IoT users in a hybrid cryptographic method. The proposed model feature ensuring integrity and confidentiality of users' big data collected through IoT equipment. In addition, the user's big data is not abused without the user's consent because the user's signature information is encrypted using a steganography-based cryptography-based encryption technique.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.331-332
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• 2016

In the IoT environment, The mass IoT data can be collected in a variety of IoT devices. Due to mass IoT data, the services for data processing and providing are needed that fit user requirements. So we propose the design of IoT data management system based on ontology using context information.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.965-968
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• 2016

IoT 환경이 도래하면서 데이터 수집, 데이터 전송, 데이터 처리와 같이 IoT 서비스 제공을 위한 인프라에 대한 연구는 활발하게 진행되고 있지만 이러한 인프라에서 실제 IoT 서비스를 사용하는데 필요한 데이터 분석은 상대적으로 연구가 미흡하다. IoT 서비스를 효율적으로 제공하고, 효과적으로 사용하기 위해서는 데이터 분석에 대한 연구가 선행돼야 한다. 특히 IoT 서비스 중에서도 IoT 홈 서비스의 경우 데이터에 내재된 개인정보 문제 때문에 데이터의 수집이 어렵고, 분석에 필요한 형태를 갖추기도 어렵기 때문에 기계학습기법의 적용이 용이하지 않다. 본 논문에서는 이진형(Discret) IoT 홈 서비스의 데이터 분석을 위해 DAG기반 데이터 시각화 기법을 제안한다. 해당 기법을 통해 IoT 홈 서비스 디바이스들간 연관성, 각 디바이스의 사용 지속성 및 사용패턴과 같은 데이터 분석을 위한 메타데이터를 제공한다.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.756-761
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• 2015

With the emergence of the Internet-of-Things (IoT) paradigm, there is a growing demand for personalized services using IoT contexts acquired from heterogeneous IoT devices. However, due to the mismatch between IoT context exchange schemes of context-aware services and IoT devices, IoT applications can acquire IoT contexts only from IoT devices that support the same IoT context exchange schemes. In this paper, we propose dynamic methods to mediate those mismatches on the IoT context exchange schemes. With the proposed mediation methods, context-aware services can collect IoT contexts from heterogeneous IoT devices without considering their IoT context exchange schemes.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.4
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• pp.103-110
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• 2018

Services that enhance user convenience by using various IoT devices are increasing with the development of Internet of Things(IoT) technology. Also, since the price of IoT sensors has become cheaper, companies providing services by collecting and utilizing data from various sensors are increasing. The smart IoT home system is a representative use case that improves the user convenience by using IoT devices. To improve user convenience of Smart IoT home system, this paper proposes a method for the control of related devices based on data analysis. Internal environment measurement data collected from IoT sensors, device control data collected from device control actuators, and user judgment data are learned to predict the current home state and control devices. Especially, differently from previous approaches, it uses deep neural network to analyze the data to determine the inner state of the home and provide information for maintaining the optimal inner environment. In the experiment, we compared the results of the long-term measured data with the inferred data and analyzed the discrimination performance of the proposed method.

• 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.