• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.4977-4996
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• 2016

The existence of excessively large and too filled network buffers, known as bufferbloat, has recently gained attention as a major performance problem for delay-sensitive applications. Researchers have made three types of suggestions to solve the bufferbloat problem. One is End to End (E2E) congestion control, second is deployment of Active Queue Management (AQM) techniques and third is the combination of above two. However, these solutions either seem impractical or could not obtain good bandwidth utilization. In this paper, we propose a Transmission Control Protocol（TCP）delayed window update mechanism which uses a congestion detection approach to predict the congestion level of networks. When detecting the network congestion is coming, a delayed window update control strategy is adopted to maintain good protocol performance. If the network is non-congested, the mechanism stops work and congestion window is updated based on the original protocol. The simulation experiments are conducted on both high bandwidth and long delay scenario and low bandwidth and short delay scenario. Experiment results show that TCP delayed window update mechanism can effectively improve the performance of the original protocol, decreasing packet losses and queuing delay while guaranteeing transmission efficiency of the whole network. In addition, it can perform good fairness and TCP friendliness.

• 인터넷정보학회논문지
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• 제22권5호
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• pp.27-33
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• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.

• 한국산학기술학회논문지
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• 제11권10호
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• pp.3958-3963
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• 2010

인터넷 메신저는 개인뿐만 아니라 기업에서도 많이 사용하고 있다. 메신저는 메일, 쪽지, SMS 서비스 등의 많은 기능을 제공하고 있다. 이러한 서비스를 제공하고 있는 메신저는 보안상의 많은 문제점이 제기되고 있다. 기존 Nate-on은 사용자의 개인 정보를 서버 DB에 저장을 하여 내부자 공격에 노출되고, 클라이언트간의 데이터 또한 네트워크상에 단순히 전달되었다. 이러한 정보 노출 문제점을 해결하기 위한 프로토콜을 제안한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.140-142
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• 2023

Federated learning (FL) is a new paradigm in machine learning (ML) that enables multiple devices to collaboratively train a shared ML model without sharing their local data. FL is well-suited for applications where data is sensitive or difficult to transmit in large volumes, or where collaborative learning is required. The Internet of Underwater Things (IoUT) is a network of underwater devices that collect and exchange data. This data can be used for a variety of applications, such as monitoring water quality, detecting marine life, and tracking underwater vehicles. However, the harsh underwater environment makes it difficult to collect and transmit data in large volumes. FL can address these challenges by enabling devices to train a shared ML model without having to transmit their data to a central server. This can help to protect the privacy of the data and improve the efficiency of training. In this view, this paper provides a brief overview of Fed-IoUT, highlighting its various applications, challenges, and opportunities.

• 한국시뮬레이션학회논문지
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• 제25권2호
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• pp.31-40
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• 2016

무선센서네트워크는 해양환경, 군사시설 등 다양한 분야에서 활용되고 있다. 이러한 활용은 센서 데이터를 기반으로 이루어지기 때문에 센서 데이터는 굉장히 중요하다. 무선센서네트워크에서의 통신은 주위 환경에 매우 민감하게 영향을 받기 때문에 통신장애가 발생할 확률이 높다. 특히 수중통신의 경우 좁은 대역폭과 느린 전송 속도, 주변 환경의 잡음 등으로 인해 전파통신에 비해 통신장애는 더 빈번하게 발생한다. 통신장애가 발생하면 센서 데이터 전달과정에서 데이터가 손실될 수 있고, 이는 화재감지 시스템과 같이 실시간성이 중요한 분야에서는 큰 피해를 입을 수 있다. 이를 위해 센서 데이터의 저장 및 압축을 위한 연구를 진행하였지만 이를 위한 프레임워크가 존재하지 않아 그 실현에 어려움이 있었다. 따라서 본 논문에서는 센서 데이터의 손실 감소를 위한 프레임워크를 제안하고 성능을 분석하였다. 분석 결과, 프레임워크를 적용하지 않은 경우에는 통신장애 발생 후 t 시간이 경과함에 따라 T/t(T는 통신장애 발생 시 데이터 저장에서 메모리가 full 상태가 되는 시간)의 복구율 감소를 보인다. 게다가, T 시간 이후의 센서 데이터는 모두 복구가 불가능한 오류에 해당한다. 그러나, 제안한 프레임워크를 적용한 경우는 100%의 데이터 복구율과 2~6%의 복구 후 데이터 오차율을 보인다.

• 디지털산업정보학회논문지
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• 제11권2호
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• pp.113-120
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• 2015

Under the penetration of smartphones equipped with a variety of features grows globally, the efficient using of a variety of functions of smartphones has been increased. In accordance with this trend, a lot of researches on the remote control method using the smart phone for consumer products in home networks. Input methods of the current smpartphoes are typically button-based inputs through touching. The button input methods are inconvenient for people who are not familiar touch. Therefore, the researches on the different input schemes to replace the touch methods are required. In this paper, we propose a gesture based input method to replace the touch-sensitive input that of the existing smartphone applications, and a way to apply it to home networks. The proposed method uses three-axis acceleration sensor which is built into smatphones, and it also defines six kinds of gestures patterns that may be applied to home network systems by measuring the recognition rates.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제2권11호
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• pp.489-494
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• 2013

다양한 서비스를 제공하는 인터넷 환경에서 수많은 개인정보가 활용되고 있으나, 개인 사용자는 자신의 어떠한 개인 정보가 전송되는지 모르고 있다. 본 논문에서는 네트워크 패킷 내에 개인정보를 지속적으로 모니터링하고, 유출 여부를 사용자에게 알려주는 모니터링 도구 개발을 목표로 한다. 이를 위하여 개인정보 모니터링 도구를 구현하고, 그 결과를 분석한다. 또한 기업 기밀정보 유출 방지, 의료정보 유출 방지 등 직접 활용할 수 있는 응용 분야에 대해 제시한다. 본 연구 결과는 개인정보 유출 방지에 기여함으로써 개인정보를 대상으로 하는 다양한 사이버 침해를 줄이는데 기여할 수 있을 것으로 판단한다.

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.261-269
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• 2023

We propose to design a Holochain-based security and privacy protection system for resource-constrained IoT healthcare systems. Through analysis and performance evaluation, the proposed system confirmed that these characteristics operate effectively in the IoT healthcare environment. The system proposed in this paper consists of four main layers aimed at secure collection, transmission, storage, and processing of important medical data in IoT healthcare environments. The first PERCEPTION layer consists of various IoT devices, such as wearable devices, sensors, and other medical devices. These devices collect patient health data and pass it on to the network layer. The second network connectivity layer assigns an IP address to the collected data and ensures that the data is transmitted reliably over the network. Transmission takes place via standardized protocols, which ensures data reliability and availability. The third distributed cloud layer is a distributed data storage based on Holochain that stores important medical information collected from resource-limited IoT devices. This layer manages data integrity and access control, and allows users to share data securely. Finally, the fourth application layer provides useful information and services to end users, patients and healthcare professionals. The structuring and presentation of data and interaction between applications are managed at this layer. This structure aims to provide security, privacy, and resource efficiency suitable for IoT healthcare systems, in contrast to traditional centralized or blockchain-based systems. We design and propose a Holochain-based security and privacy protection system through a better IoT healthcare system.

• 한국정보과학회논문지:정보통신
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• 제32권6호
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• pp.676-686
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• 2005

네트워크 제공자들뿐만 아니라 인터넷 사용자들도 최선 서비스(best-effort service)를 뛰어 넘어 사용자들 간에 서로 다른 서비스 품질을 받기를 열망하고 있다. 이 논문은 사용자 세션 단위로 차별화 서비스를 제공할 수 있는 알고리즘을 제안한다. 제안하는 알고리즘은 결손 보완 라운드-로빈 알고리즘을 기반으로 확장한 계층적 결손 보완 라운드-로빈 알고리즘이다. 이 알고리즘의 주 장점은 응용의 타입을 별도로 구분하지 않으면서도 FTP와 같이 대역폭에 민감한 응용뿐만 아니라 VoIP와 같이 지연에 민감한 응용의 품질 차별화를 제공할 수 있다는 점이다. 품질 차별화 서비스를 제공함에 있어서 네트워크 제공자 측면에서 중요한 점은 예측성과 제어성이다. 우리는 수학적 분석과 모의실험 실험을 통해서 제안하는 알고리즘이 예측성과 제어성이 기존의 결손 보완 라운드-로빈 알고리즘보다 월등히 뛰어남을 보인다. 그럼에도 불구하고 계층적 결손 보완 라운드-로빈 알고리즘의 구현 복잡도는 O(1)이다.

• International Journal of Computer Science & Network Security
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• 제21권1호
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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%.