• 제목/요약/키워드: IoT applications

검색결과 477건 처리시간 0.023초

On Additive Signal Dependent Gaussian Noise Channel Capacity for NOMA in 5G Mobile Communication

  • Chung, Kyuhyuk
    • International Journal of Internet, Broadcasting and Communication
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    • 제12권2호
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    • pp.37-44
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    • 2020
  • The fifth generation (5G) mobile communication has been commercialized and the 5G applications, such as the artificial intelligence (AI) and the internet of things (IoT), are deployed all over the world. The 5G new radio (NR) wireless networks are characterized by 100 times more traffic, 1000 times higher system capacity, and 1 ms latency. One of the promising 5G technologies is non-orthogonal multiple access (NOMA). In order for the NOMA performance to be improved, sometimes the additive signal-dependent Gaussian noise (ASDGN) channel model is required. However, the channel capacity calculation of such channels is so difficult, that only lower and upper bounds on the capacity of ASDGN channels have been presented. Such difficulties are due to the specific constraints on the dependency. Herein, we provide the capacity of ASDGN channels, by removing the constraints except the dependency. Then we obtain the ASDGN channel capacity, not lower and upper bounds, so that the clear impact of ASDGN can be clarified, compared to additive white Gaussian noise (AWGN). It is shown that the ASDGN channel capacity is greater than the AWGN channel capacity, for the high signal-to-noise ratio (SNR). We also apply the analytical results to the NOMA scheme to verify the superiority of ASDGN channels.

디지털-피지컬 구조를 이용한 신체 항상성 유지 공간 연구 (A Study on Usage of Integrated Digital-Physical Structure on Physical Homeostasis Space for Stress Reduction)

  • 강민수
    • 한국멀티미디어학회논문지
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    • 제23권4호
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    • pp.574-580
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    • 2020
  • Stress induces change to the body functions and causes chronic problems such as worsening a disease. Thus, humans want to evade anxiety and would try any means to reduce stressful situations. Generally, a person would handle their stress by either regulating their emotions or merely coping with the situation, for which the former is most widely used. Our research aims to effectively reduce stress by using the emotional response structure developed by Plutichik and the vitalization method. We extracted the relevant components of the stress-reduction method that would be applicable in any space using digital technologies such as sensors, IoT, and augmented reality. An architect or designer may incorporate these structural components into any structure to effectively reduce people's stress. The research aims to provide a new perspective of architectural space and to show applications of the stress-reducing architectural spaces, which should also fulfill the people's needs. Further research is needed to develop an automatic system to utilize spatial components more effectively.

ENC-MAC: Energy-efficient Non-overlapping Channel MAC for Cognitive Radio enabled Sensor Networks

  • Kim, Bosung;Kim, Kwangsoo;Roh, Byeong-hee
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제9권11호
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    • pp.4367-4386
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    • 2015
  • The concept of Internet of Things (IoT) has shed new light on WSN technologies. MAC protocol issues improving the network performance are important in WSNs because of the increase in demand for various applications to secure spectrum resources. Cognitive radio (CR) technology is regarded as a solution to the problems in this future wireless network. In recent years, energy efficiency has become an issue in CR networks. However, few relevant studies have been conducted. In this paper, an energy-efficient non-overlapping channel MAC (ENC-MAC) for CR-enabled sensor networks (CRSNs) is proposed. Applying the dedicated control channel approach, ENC-MAC allows the SUs to utilize channels in a non-overlapping manner, and thus spectrum efficiency is improved. Moreover, the cooperative spectrum sensing that allows an SU to use only two minislots in the sensing phase is addressed to en-hance energy efficiency. In addition, an analytical model for evaluating the performance, such as saturation throughput, average packet delay, and network lifetime, is developed. It is shown in our results that ENC-MAC remarkably outperforms existing MAC protocols.

NodeJS와 MongoDB를 활용한 모터 동작 제어시스템 구현 (Implementation of motor control system using NodeJS and MongoDB)

  • 강진영;이영동
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2017년도 추계학술대회
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    • pp.748-750
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    • 2017
  • 사물인터넷이 고도의 지능화가 됨에 따라 다양한 분야로 적용되고 있다. 그중에 센서-서버-DB를 결합해 하나의 플랫폼을 만들어서 다양한 분야에 접목하면 데이터를 쉽게 관리 할 수 있는 사물인터넷 기반의 시스템이 구현 가능하다. 이에 따라, 본 논문에서는 사물인터넷 기반 NodeJS와 MongoDB를 활용하여 스마트폰 기울기에 따른 서보모터 동작 제어 시스템 구현을 위해 Rasberry Pi, 서보모터, 스마트폰으로 시스템을 구성하고, NodeJS, MongoDB, npm모듈, HTML을 활용한 서보모터 센서 값이 발생하면 NodeJS로 전송하여 데이터를 저장 관리 할 수 있도록 하였다.

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스마트기기용 강압형 DC-DC 변환기 특성해석 (Analysis of a Buck DC-DC Converter for Smart Electronic Applications)

  • 강보경;나재훈;송한정
    • 한국산업융합학회 논문집
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    • 제22권3호
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    • pp.373-379
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    • 2019
  • Nowadays, the IoT portable electronic devices have become more useful and diverse, so they require various supply voltage levels to operate. This paper presents a DC-DC buck converter with pulse width modulation (PWM) for portable electronic devices. The proposed step-down DC-DC converter consists of passive elements such as capacitors, inductors, and resistors and an integrated chip (IC) for signal control to reduce power consumption and improves ripple voltage with the resolution. The proposed DC-DC converter is simulated and analyzed in PSPICE circuit design platform, and implemented on the prototype PCB board with a Texas Instruments LM5165 IC. The proposed buck converter is showed 92.6% of peak efficiency including a load current range of 4-10 mA, 3.29 mV of the voltage ripple at 5 V output voltage for the supply voltage 12 V. Measured and Simulated power efficiency are made good agreement with each other.

타원형 압전 에너지 하베스터의 기계적 모델링 연구 (Study of Mechanical Modeling of Oval-shaped Piezoelectric Energy Harvester)

  • 최재훈;정인기;강종윤
    • 센서학회지
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    • 제28권1호
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    • pp.36-40
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    • 2019
  • Energy harvesting is an advantageous technology for wireless sensor networks (WSNs) that dispenses with the need for periodic replacement of batteries. WSNs are composed of numerous sensors for the collection of data and communication; hence, they are important in the Internet of Things (IoT). However, due to low power generation and energy conversion efficiency, harvesting technologies have so far been utilized in limited applications. In this study, a piezoelectric energy harvester was modeled in a vibration environment. This harvester has an oval-shaped configuration as compared to the conventional cantilever-type piezoelectric energy harvester. An analytical model based on an equivalent circuit was developed to appraise the advantages of the oval-shaped piezoelectric energy harvester in which several structural parameters were optimized for higher output performance in given vibration environments. As a result, an oval-shaped energy harvester with an average output power of 2.58 mW at 0.5 g and 60 Hz vibration conditions was developed. These technical approaches provided an opportunity to appreciate the significance of autonomous sensor networks.

A Multi-Scale Parallel Convolutional Neural Network Based Intelligent Human Identification Using Face Information

  • Li, Chen;Liang, Mengti;Song, Wei;Xiao, Ke
    • Journal of Information Processing Systems
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    • 제14권6호
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    • pp.1494-1507
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    • 2018
  • Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.

사물인터넷 산업 현황 및 정책적 대응방향 (A Study of the Internet of Thing Industry and Policy Implications)

  • 전황수
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2014년도 춘계학술대회
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    • pp.724-727
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    • 2014
  • 사물인터넷은 주변 사물들이 유무선 네트워크로 연결되어 유기적으로 정보를 수집 및 공유하면서 상호 작용하는 지능형 네트워킹 기술 및 환경을 의미한다. 사물인터넷은 다양한 산업과의 융복합을 통해 공공안전, 리테일 등을 중심으로 서비스 시장이 확대되고 있으며, 기본의 헬스케어, 스마트 에너지 관련 분야뿐만 아니라 지능형 교통서비스, 사회 인프라, 원격관리서비스 등으로 확장될 전망이다. 사물인터넷은 의료, 교통, 제조, 유통, 교육 등 다양한 분야에 도입되어 기존의 프로세서와 서비스에 획기적인 변화를 가져올 것이다. 사물인터넷이 성장하기 위해서는 보안 위협에 대한 개인정보보호의 강화, 킬러 애플리케이션 개발, IPv6 기반 차세대 인터넷으로 전환, 이종업계간 협력, 표준 플랫폼 구축 등이 필요하다.

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사이버 물리 시스템에서 효율적인 파일 전송을 위한 커널 레벨 분할 및 결합 연산의 설계와 구현 (Design and Implementation of Kernel-Level Split and Merge Operations for Efficient File Transfer in Cyber-Physical System)

  • 박현찬;장준희;이준석
    • 대한임베디드공학회논문지
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    • 제14권5호
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    • pp.249-258
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    • 2019
  • In the cyber-physical system, big data collected from numerous sensors and IoT devices is transferred to the Cloud for processing and analysis. When transferring data to the Cloud, merging data into one single file is more efficient than using the data in the form of split files. However, current merging and splitting operations are performed at the user-level and require many I / O requests to memory and storage devices, which is very inefficient and time-consuming. To solve this problem, this paper proposes kernel-level partitioning and combining operations. At the kernel level, splitting and merging files can be done with very little overhead by modifying the file system metadata. We have designed the proposed algorithm in detail and implemented it in the Linux Ext4 file system. In our experiments with the real Cloud storage system, our technique has achieved a transfer time of up to only 17% compared to the case of transferring split files. It also confirmed that the time required can be reduced by up to 0.5% compared to the existing user-level method.

An Intelligent MAC Protocol Selection Method based on Machine Learning in Wireless Sensor Networks

  • Qiao, Mu;Zhao, Haitao;Huang, Shengchun;Zhou, Li;Wang, Shan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권11호
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    • pp.5425-5448
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    • 2018
  • Wireless sensor network has been widely used in Internet of Things (IoT) applications to support large and dense networks. As sensor nodes are usually tiny and provided with limited hardware resources, the existing multiple access methods, which involve high computational complexity to preserve the protocol performance, is not available under such a scenario. In this paper, we propose an intelligent Medium Access Control (MAC) protocol selection scheme based on machine learning in wireless sensor networks. We jointly consider the impact of inherent behavior and external environments to deal with the application limitation problem of the single type MAC protocol. This scheme can benefit from the combination of the competitive protocols and non-competitive protocols, and help the network nodes to select the MAC protocol that best suits the current network condition. Extensive simulation results validate our work, and it also proven that the accuracy of the proposed MAC protocol selection strategy is higher than the existing work.