• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권10호
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• pp.3394-3408
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• 2014

Conventional mobile state (MS) and base station (BS) association based on average signal strength often results in imbalance of cell load which may require more powerful processor at BSs and degrades the perceived transmission rate of MSs. To deal with this problem, a Markov decision process (MDP) for load balancing in a multi-cell system with multi-carriers is formulated. To solve the problem, exploiting Sarsa algorithm of on-line learning type [12], ${\alpha}$-controllable load balancing algorithm is proposed. It is designed to control tradeoff between the cell load deviation of BSs and the perceived transmission rates of MSs. We also propose an ${\varepsilon}$-differential soft greedy policy for on-line learning which is proven to be asymptotically convergent to the optimal greedy policy under some condition. Simulation results verify that the ${\alpha}$-controllable load balancing algorithm controls the behavior of the algorithm depending on the choice of ${\alpha}$. It is shown to be very efficient in balancing cell loads of BSs with low ${\alpha}$.

• Journal of information and communication convergence engineering
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• 제20권4호
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• pp.273-279
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• 2022

Owing to the need to establish a cooperative-intelligent transport system (C-ITS) environment in the transportation sector locally and abroad, various research and development efforts such as high-tech road infrastructure, connection technology between road components, and traffic information systems are currently underway. However, the current central control center-oriented information collection and provision service structure and the insufficient road infrastructure limit the realization of the C-ITS, which requires a diversity of traffic information, real-time data, advanced traffic safety management, and transportation convenience services. In this study, a network construction method based on the existing received signal strength indicator (RSSI) selected as a comparison target, and the experimental target and the proposed intelligent edge network compared and analyzed. The result of the analysis showed that the data transmission rate in the intelligent edge network was 97.48%, the data transmission time was 215 ms, and the recovery time of network failure was 49,983 ms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1854-1868
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• 2018

A wireless body-sensor network (WBSN) refers to a network-configured environment in which sensors are placed on both the inside and outside of the human body. The sensors are much smaller and the energy is more constrained when compared to traditional wireless sensor network (WSN) environments. The critical nature of the energy-constraint issue in WBSN environments has led to numerous studies on the reduction of energy consumption of WBSN sensors. The transmission-power-control (TPC) technique adjusts the transmission-power level (TPL) of sensors in the WBSN and reduces the energy consumption that occurs during communications. To elaborate, when transmission sensors and reception sensors are placed in various parts of the human body, the transmission sensors regularly send sensor data to the reception sensors. As the reception sensors receive data from the transmission sensors, real-time measurements of the received signal-strength indication (RSSI), which is the value that indicates the channel status, are taken to determine the TPL that suits the current-channel status. This TPL information is then sent back to the transmission sensors. The transmission sensors adjust their current TPL based on the TPL that they receive from the reception sensors. The initial TPC algorithm made linear or binary adjustments using only the information of the current-channel status. However, because various data in the WBSN environment can be utilized to create a more efficient TPC algorithm, many different types of TPC algorithms that combine human movements or fuse TPC with other algorithms have emerged. This paper defines and discusses the design and development process of an efficient TPC algorithm for WBSNs. We will describe the WBSN characteristics, model, and closed-loop mechanism, followed by an examination of recent TPC studies.

• 한국항행학회논문지
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• 제18권1호
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• pp.90-94
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• 2014

본 논문에서는 IEEE 802.15.4 기반 무선센서네트워크(WSN) 및 수신신호세기(RSSI)를 이용하여 건물 내에서의 소방대원의 조난을 감지하고 무선으로 조난신호를 보냄으로써 근처 동료 소방대원이 수신신호세기(RSSI)를 통해 조난신호의 송신방향을 읽고 위치를 추적하는 시스템을 제안하였다. 또한, 제안된 시스템을 이용한 실험에서 이론값과 실험값의 오차가 작기 때문에 RSSI를 통한 위치추적이 가능하다는 것을 확인하였다.

• 터널과지하공간
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• 제34권4호
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• pp.267-282
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• 2024

본 연구는 고준위 방사성 폐기물 심층 처분시설의 장기적 건전성 모니터링을 위한 무선 통신 시스템을 개발하는 것을 목표로 한다. 기존의 유선 센서는 전원 공급과 데이터 전송을 위해 전선을 사용함으로써 완충재의 품질 저하 및 관리의 어려움을 초래할 수 있다. 이를 해결하기 위해 본 연구에서는 무선 통신 시스템의 도입을 제안하였으며, WiFi, ZigBee, LoRa 등의 모듈을 사용하여 벤토나이트 내 수신 감도를 비교 분석하였다. 벤토나이트의 건조밀도 증가와 송수신기 간 거리 증가에 따라 수신 감도가 감소하는 경향을 확인하였으며, 저주파수 대역에서 적은 신호 감쇠를 확인하였다. 이를 바탕으로 무선 네트워크 기반의 THM 통합 무선 센서 시스템의 개념 설계를 제안하였다. 이는 처분 구조물의 장기 건전성 모니터링을 위한 기초자료로 활용 가능할 것으로 기대된다.

• 인터넷정보학회논문지
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• 제23권3호
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• pp.13-20
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• 2022

차세대 와이파이 표준기술인 IEEE 802.11ay는 밀리미터파 대역에서 AP (Access Point)가 다수의 STA (Station)로 동시에 데이터를 전송하도록 MU-MIMO (Multiple User Multiple Input Multiple Output) 통신을 지원한다. 이를 위해, 주기적으로 MU-MIMO 빔포밍 훈련을 수행해야 하고, 효율적인 빔포밍 훈련을 위해서는 AP가 다수의 안테나로 다수의 빔을 동시에 전송할 때, 각 STA에서 측정되는 신호 세기를 정확히 예측하는 것이 중요하다. 본 논문에서는 딥러닝 기반 다중 빔 전송링크 성능 예측기법을 제안한다. 제안한 예측기법은 특정 실내 또는 실외 환경에서 미리 학습된 딥러닝 모델을 이용하여 다수의 빔이 동시에 전송될 때 STA에서 측정되는 신호 세기 예측의 정확성을 높인다. 이때, 딥러닝의 입력으로 개별 빔이 전송될 때 STA에서 측정되는 신호 세기 정보를 이용하고, 개별 빔의 신호 세기 정보를 얻는 과정은 이미 기존의 빔포밍 훈련에 포함되어 있으므로 정보 수집을 위해 추가적인 비용을 발생하지 않는다. 성능평가를 위해 NIST (National Institute of Standards and Technology)에 의해 개발된 Q-D 채널구현 (Quasi-Deterministic Channel Realization) 오픈소스 소프트웨어를 활용하였고 실측 데이터 기반으로 밀리미터파 채널을 구현하였다. 실험결과에서는 제안한 예측기법이 다른 비교기법보다 향상된 예측성능을 보였다.

• 한국음향학회지
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• 제22권6호
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• pp.472-481
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• 2003

천해에서 포물선 근사 (parabolic approximation)에 기초한 저주파 양상태 잔향음 모델 (LHYREV-B, Low-frequency Hanyang Univ. Reverberation model-Bistatic)을 제안하였다. LHYREV-B 모델에서는 해저와 해저지형 내의 상호작용을 포함하는 음향모델에 수평입사에 독립인 산란함수를 이용하여 양상태 잔향음을 계산하였다. 모델의 검증을 위하여 실측 잔향음 신호와 비교하였으며, 비교 결과 LHYREV-B 모델이 저주파 잔향음 예측에 적합함을 확인할 수 있다.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• 대한임베디드공학회논문지
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• 제11권6호
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• pp.379-392
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• 2016

This paper presents the design and simulation of a laser power beaming (LPB) system for an electric vehicle that establishes an optimal power transmission path based on the received signal strength. The LPB system is possible to transfer power from multiple transmitters to a single receiver according to the characteristics of the laser and the solar panel. When the laser beams of multiple transmitters aim at a solar panel at the same time, the received power is the sum of all energy at a solar panel. Our proposed LPB system consists of multiple transmitters and multiple receivers. The transmitter sends its power characteristics as optically coded pulses with a class 1 laser beam and powers as a high-intensity laser beam. By using the attenuated power level, the receiver can estimate the maximum receivable powers from the transmitters and select optimal transmitters. Throughout the simulation, we verified the possibility that different LPB receivers were achieved their required power by the optimal allocation of the transmitter among the various transmitters.

• 한국통신학회논문지
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• 제38B권4호
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• pp.279-290
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• 2013

본 논문에서는 UWB의 광대역 주파수와 낮은 신호세기의 특성을 이용해서 기밀한 음성 통신이 가능하고, 이동 ad hoc 네트워크로 동작하도록 하여 단말의 이동이 가능한 전술망용 MAC 프로토콜의 효과적인 음성 데이터 전송 기법을 제안하였다. 기존의 UWB MAC 프로토콜은 대부분 단말 간 동기화를 지원하여 전력 소모, 대역폭과 같은 자원 활용에 중점을 두었으나, 본 논문의 UWB MAC 프로토콜은 단말의 이동성에 중점을 두어 비동기 방식으로 구동된다. 또한 효과적인 음성 데이터 전송을 위해서 RTS/CTS 패킷을 사용하지 않고, 재전송 과정이 없다. 해당 음성 데이터 전송 방식의 시뮬레이션을 통하여 성능을 측정하였다.