• Journal of Korea Multimedia Society
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• v.22 no.6
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• pp.676-683
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• 2019

Wireless Sensor Networks (WSNs) consist of multiple tiny and power constrained sensors which use radio frequencies to carry out sensing in a designated sensor area. To effectively design and implement reliable WSN, it is critical to consider models, protocols, and algorithms that can optimize energy consumption of all the sensor nodes with optimal amount of packet delivery. It has been observed that deploying a single sink node comes with numerous challenges especially in a situation with high node density and congestion. Sensor nodes close to a single sink node receive more transmission traffic load compared to other sensors, thus causing quick depletion of energy which finally leads to an energy hole and sink hole problems. In this paper, we proposed the use of multiple energy efficient sink nodes with brute force technique under optimized parameters to improve on the number of packets delivered within a given time. Simulation results not only depict that, deploying N sink nodes in a sensor area has a maximum limit to offer a justifiable improvement in terms of packet delivery ratio but also offers a reduction in End to End delay and reliability in case of failure of a single sink node, and an improvement in the network lifetime rather than deploying a single static sink node.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.99-100
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• 2019

무선 센서 네트워크(Wireless Sensor Network, WSN)에서의 싱크 이동은 전력 문제와 관련하여 해결해야할 중요한 문제이다. 또한 고정된 싱크를 사용하면 노드의 과도한 에너지 소비로 인해 네트워크 성능이 저하될 수 있는 문제점이 있다. 본 논문에서는 센서 노드의 클러스터가 퍼지 로직(Fuzzy Logic)을 기반으로 형성되고 싱크 재배치가 동적 2단계 메커니즘을 사용하여 결정되는 에너지 효율적인 싱크 재배치 기법을 제안한다. 본 논문에서 제안하는 기법은 싱크 재배치에 필요한 통신 오버 헤드를 최소화하고 데이터 패킷의 손실이나 지연을 최소화하여 싱크 재배치를 허용한다. 시뮬레이션 결과 제안 기법이 기존의 WSN의 대표적인 싱크 재배치 기법보다 네트워크 수명과 처리량 측면에서 우수하다는 것을 확인할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.96-98
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• 2014

Wireless Sensor Network(WSN) is a wireless real-time information(Acquired from the sensor nodes that have the computing power and wireless communication capabilities.) collected, and to take advantage of processing techniques. Currently it is very diverse, such as environmental monitoring, health care, security, smart home, smart grid applications is that. Thus it is required in the wireless sensor network, the algorithm for the efficient use of the limited energy capacity. Suggested by the algorithm for selecting a cluster head node for a hybrid type and clustered, by comparing the amount of energy remaining and a connection between the nodes In this paper, we aim to increase efficiency and accuracy of the wireless sensor network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.216-239
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• 2021

The present work addresses the challenging problem of coordinating power allocation with interference management in multi-robot networks by applying the promising expansion capabilities of multiple-input multiple-output (MIMO) and full duplex systems, which achieves it for maximizing the throughput of networks under the impacts of Doppler frequency shifts and external jamming. The proposed power allocation with interference coordination formulation accounts for three types of the interference, including cross-tier, co-tier, and mixed-tier interference signals with cluster head nodes operating in different full-duplex modes, and their signal-to-noise-ratios are respectively derived under the impacts of Doppler frequency shifts and external jamming. In addition, various optimization algorithms, including two centralized iterative optimization algorithms and three decentralized optimization algorithms, are applied for solving the complex and non-convex combinatorial optimization problem associated with the power allocation and interference coordination. Simulation results demonstrate that the overall network throughput increases gradually to some degree with increasing numbers of MIMO antennas. In addition, increasing the number of clusters to a certain extent increases the overall network throughput, although internal interference becomes a severe problem for further increases in the number of clusters. Accordingly, applications of multi-robot networks require that a balance should be preserved between robot deployment density and communication capacity.

• ETRI Journal
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• v.44 no.3
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• pp.389-399
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• 2022

Wireless sensor networks (WSNs) are one of the basic building blocks of Internet of Things (IoT) systems. However, the wireless sensing nodes in WSNs suffer from energy constraint issues because the replacement/recharging of the batteries of the nodes tends to be difficult. Furthermore, a number of realistic IoT scenarios, such as habitat and battlefield monitoring, contain mobile sensing elements, which makes the energy issues more critical. This research paper focuses on realistic WSN scenarios that involve mobile sensing elements with the aim of mitigating the attendant energy constraint issues using the concept of radio-frequency (RF) energy extraction. The proposed technique incorporates a cluster head election workflow for WSNs that includes mobile sensing elements capable of RF energy harvesting. The extensive simulation analysis demonstrated the higher efficacy of the proposed technique compared with the existing techniques in terms of residual energy, number of functional nodes, and network lifetime, with approximately 50% of the nodes found to be functional at the 4000th, 5000th, and 6000th rounds for the proposed technique with initial energies of 0.25, 0.5 and 1 J, respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.139-148
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• 2022

Research into wireless sensor networks (WSNs) is a trendy issue with a wide range of applications. With hundreds to thousands of nodes, most wireless sensor networks interact with each other through radio waves. Limited computational power, storage, battery, and transmission bandwidth are some of the obstacles in designing WSNs. Clustering and routing procedures have been proposed to address these concerns. The wireless sensor network's most complex and vital duty is routing. With the Greedy Perimeter Stateless Routing method (GPSR), an efficient and responsive routing protocol is built. In packet forwarding, the nodes' locations are taken into account while making choices. In order to send a message, the GPSR always takes the shortest route between the source and destination nodes. Weighted directed graphs may be constructed utilising four distinct distance metrics, such as Euclidean, city block, cosine, and correlation distances, in this study. NS-2 has been used for a thorough simulation. Additionally, the GPSR's performance with various distance metrics is evaluated and verified. When compared to alternative distance measures, the proposed GPSR with correlation distance performs better in terms of packet delivery ratio, throughput, routing overhead and average stability time of the cluster head.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.4
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• pp.347-358
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• 2021

Purpose: The purpose of this study is to explore the experiences of nurses who have suffered from hurt feelings in their relationships with medical personnel in intensive care units (ICUs). Methods: Data were collected from May 2019 to August 2020 through individual in-depth interviews with 8 ICU nurses. Verbatim transcripts were analyzed using the MAXQDA program, employing a phenomenological method designed by Colaizzi. Results: Four theme clusters and nine themes were identified. The themes cluster that emerged were as follows: high-handedness from a lack of understanding and consideration, not being respected as a professional, having to endure sadness alone, and strategies for accepting pain. Conclusion: In ICUs, nurses are being emotionally wounded, resulting in a variety of hurt feelings, not only by doctors, but also by other nurses. Nurses were heavy-hearted because they wanted the participants to do their part and care for critically ill patients proficiently. However, they did not treat ICU nurses as specialized professionals. The participating nurses said they felt pressure, fear, and intimidated. In addition, they felt lonely because their head nurses, seniors, or colleagues did not protect or support them. This study helped develop a program to decrease the nurses' emotional distress and create a safe work environment where they respected and cared for each other without undergoing anguish.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.599-601
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• 2011

무선 센서 네트워크를 구성하는 센서노드들이 클러스터를 구성하고 선출된 클러스터 헤드가 클러스터 내의 센서노드들로부터 데이터를 받아서 병합한 다음, 기지국으로 데이터를 전달하는 클러스터 기반 라우팅 방법이 연구되어 왔다. 이 클러스터 기반 라우팅 방법에서 클러스터 헤드에 고장이 발생한다면, 해당 클러스터의 데이터는 기지국으로 전달할 수 없어 데이터 신뢰성에 문제가 생긴다. 이러한 문제를 해결하기 위해, 본 논문에서는 고장감내를 지원하는 클러스터 기반 라우팅 방법을 제안한다. 제안한 방법은 각 클러스터마다 백업 클러스터 헤드를 지정하여 원래의 클러스터 헤드에 고장이 발생한다면 백업 클러스터 헤드가 그 역할을 대신하도록 함으로써 데이터 전달의 신뢰성을 보장한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.936-940
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• 2007

본 논문은 무선 센서 네트워크의 에너지 효율적인 운영을 위해 무선 센서 네트워크 환경에 적합한 클러스터 헤드 선출 메커니즘을 제안한다. LEACH 와 같은 기존의 확률 모델 기반의 클러스터 헤드 선출 메커니즘들은 각 라운드마다 클러스터 헤드로 선출될 확률과 라운드 횟수 등을 바탕으로 클러스터 헤드를 선출한다. 그러나 이와 같은 방법은 각 노드의 상황을 고려하지 않아 네트워크의 수명을 단축시킬 수 있다. 이러한 문제점을 해결하기 위해서는 각 센서 노드의 에너지 및 노드 분포 상황을 고려하여 클러스터 헤드를 선출해야 한다. 하지만 실제 무선 센서 네트워크 환경에서는 클러스터 헤드 선출을 위해 정확한 정보를 수집하고 이를 계산하는데 있어 큰 오버헤드가 발생하는 문제점이 있다. 이에 본 논문에서는 정보 수집 및 계산에 있어서 오버헤드를 줄이고 네트워크의 수명을 극대화하기 위하여 퍼지 논리를 이용한 퍼지 논리 기반의 클러스터 헤드 선출 메커니즘을 제안한다. Matlab 을 통한 시뮬레이션 결과 LEACH 에 비해 퍼지 논리 기반의 클러스터 헤드 선출 메커니즘을 이용했을 경우 네트워크 수명이 약 16.3% 향상되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1462-1477
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• 2024

With the evolving complexity of connected vehicle features, the volume and diversity of data generated during driving continue to escalate. Enabling data sharing among interconnected vehicles holds promise for improving users' driving experiences and alleviating traffic congestion. Yet, the unintentional disclosure of users' private information through data sharing poses a risk, potentially compromising the interests of vehicle users and, in certain cases, endangering driving safety. Federated learning (FL) is a newly emerged distributed machine learning paradigm, which is expected to play a prominent role for privacy-preserving learning in autonomous vehicles. While FL holds significant potential to enhance the architecture of the Internet of Vehicles (IoV), the dynamic mobility of vehicles poses a considerable challenge to integrating FL with vehicular networks. In this paper, a novel clustered FL framework is proposed which is efficient for reducing communication and protecting data privacy. By assessing the similarity among feature vectors, vehicles are categorized into distinct clusters. An optimal vehicle is elected as the cluster head, which enhances the efficiency of personalized data processing and model training while reducing communication overhead. Simultaneously, the Local Differential Privacy (LDP) mechanism is incorporated during local training to safeguard vehicle privacy. The simulation results obtained from the 20newsgroups dataset and the MNIST dataset validate the effectiveness of the proposed scheme, indicating that the proposed scheme can ensure data privacy effectively while reducing communication overhead.