연구동향 분석
박사

멀티 에이전트 강화학습 기반 대규모 고신뢰 산업용 무선 센서 네트워크 = Multi-Agent Reinforcement Learning-basedLarge Scale Reliable Industrial Wireless Sensor Networks

박희웅 2020년

논문상세정보
인용/피인용
' 멀티 에이전트 강화학습 기반 대규모 고신뢰 산업용 무선 센서 네트워크 = Multi-Agent Reinforcement Learning-basedLarge Scale Reliable Industrial Wireless Sensor Networks' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Q-학습
  • TSCH
  • 강화학습
  • 머신 러닝
  • 무선 센서 네트워크
  • 산업용 사물인터넷
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
1,210 0

0.0%

' 멀티 에이전트 강화학습 기반 대규모 고신뢰 산업용 무선 센서 네트워크 = Multi-Agent Reinforcement Learning-basedLarge Scale Reliable Industrial Wireless Sensor Networks' 의 참고문헌

  • ‘ ZigBee® network protocols and applications ’
    [2014]
  • ‘ WirelessHART : Applying Wireless Technology in Real-Time Industrial ProcessControl ’
    [2008]
  • ‘ RF Mesh Systems for Smart Metering : System Architecture and Performance ’
    [2010]
  • ‘ On-the-Fly Bandwidth Reservation for 6TiSCH Wireless Industrial Networks ’
    16 ( 2 ) , 550-560 . [2016]
  • ‘ Lightweight Reinforcement Learning for Energy EfficientCommunications in Wireless Sensor Networks ’
    7 , 29355-29364 [2019]
  • ‘ Frame-Type-Aware Static Time SlottedChannel Hopping Scheduling Scheme for Large-Scale Smart Metering Networks ’
    7 , 2200-2209 . [2019]
  • ‘ Deep learning– based real-time query processing for wireless sensor network ’
    13 ( 5 ) . [2017]
  • [27] Texas Instruments (2013), ‘CC1200 Low-Power, High-Performance RF Transceiver’, Retrieved January 1, 2020, from: http://www.ti.com/lit/ds/symlink/cc1200.pdf.
  • [22] R. A. Alshinina and K. M. Elleithy (2018), ‘A Highly Accurate Deep Learning Based Approach for Developing Wireless Sensor Network Middleware’, IEEE Access, 6, 29885-29898.
  • [20] S. Rekik, N. Baccour, M. Jmaiel and K. Drira (2017), ‘A performance analysis of Orchestra scheduling for time-slotted channel hopping networks’, Internet Technology Letters.
  • [19] K. PisterQ, T. Watteyne, and X. Vilajosana, Ed (2017), ‘Minimal IPv6 over the TSCH Mode of IEEE 802.15.4e (6TiSCH)Configuration’, IETF, RFC8180.
    [2017]
  • [18] D. De Guglielmo, S. Brienza and G. Anastasi (2016), ‘IEEE 802.15.4e: A survey’, Computer Communications, 88, 1-24.
  • [15] S. Hung and S. N. Givigi (2017), ‘A Q-Learning Approach to Flocking With UAVs in a Stochastic Environment’, IEEE Transactions on Cybernetics, 47(1), 186-197.
  • [11] A. Aijaz and U. Raza (2017), ‘DeAMON: A Decentralized Adaptive Multi-Hop Scheduling Protocol for 6TiSCH Wireless Networks’, in IEEE Sensors Journal, 17(20), 6825-6836.
  • Zero Shot Transfer Learning for Robot Soccer ’ , In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems ( AAMAS '18 )
    [2018]
  • Two-phase dissemination scheme for CoAP-based firmware-over-the-air update of wireless sensor networks : demo abstract ’ , In Proceedings of the 17th Conference on Embedded Networked Sensor Systems ( ACM SenSys '19 )
    [2019]
  • Transmission of IPv6 Packets over IEEE 802.15.4 Networks ’
    [2017]
  • Traffic light control using deep policy-gradient and value-function-based reinforcement learning ’
    11 ( 7 ) , 417-423 [2017]
  • Traffic Aware Scheduling Algorithm for reliable low-power multi-hop IEEE 802.15.4e networks ’
    [2012]
  • The constrained application protocol ( CoAP )
    [2014]
  • The Impact of Packet Fragmentation and Reassembly in Resource Constrained Wireless Networks ’
    21 ( 2 ) , 97 [2013]
  • Reliability through frequency diversity : WhyChannel hopping makes sense ’
    [2017]
  • RPL : IPv6 Routing Protocol for Low-Power and Lossy Networks
    [2012]
  • Performance evaluation of the 6LoWPAN protocol on MICAz and TelosB motes ’
    [2009]
  • Orchestra : Robust Mesh Networks Through Autonomously Scheduled TSCH ’
    [2015]
  • On the stability of slotted aloha systems . Communications ’
    28 ( 11 ) , 1936-1939 . [1980]
  • On Optimal Scheduling in Duty-Cycled Industrial IoT Applications Using IEEE802.15.4e TSCH ’
    13 ( 10 ) , 3655-3666 . [2013]
  • Neighbor Discovery Optimization for IPv6 Over Low-Power Wireless Personal Area Networks ( 6LoWPANs ) ’
    [2012]
  • Light-weight Device Platform supporting Large-scale Device Network ’
    IEIE , ( * ACCEPTED * ) .
  • ISO. (1995). Microbiology of Food and Animal Feeding Stuffs – Horizontal Method for Detection of Thermotolerant Campylobacter. Geneva: International Organization for Standardization. [ISO 10272: 1995. E].
  • IEEE Standard for Low-Rate Wireless Networks ’
    [2016]
  • Deep multi-user reinforcement learning for distributed dynamic spectrum access ,
    18 ( 1 ) , 310-323 [2019]
  • Deep Reinforcement Learning for Multi-Agent Systems : A Review of Challenges , Solutions and Applications ’
    [2018]
  • Decentralized Traffic Aware Scheduling in 6TiSCH Networks : Design and Experimental Evaluation ’
    2 ( 6 ) , 455-470 [2015]
  • Compression format for IPv6 datagrams over IEEE 802.15 . 4-based networks ’
    [2011]
  • Communication network requirements for major smart grid applications in HAN
    67 , 74-88 [2014]
  • Analysis and Experimental Evaluation of IEEE 802.15.4e TSCH CSMA-CA Algorithm ’
    66 ( 2 ) , 1573-1588 . [2017]
  • An adaptive channel selection scheme for reliable TSCH-based communication ’
    [2015]