鐘 雷

鐘 雷

Lei Zhong

  • 無線通信ネットワーク

  • 所属

    • 情報通信企画部
      InfoTech-IS
  • 学位・経歴

    • 2018-

      トヨタ自動車(株)
      大規模車両データ収集・配信のための多段分散通信技術の研究開発・規格化

    • 2015-
      2017

      国立情報学研究所、東京大学
      ビッグデータ分析による耐災害無線通信ネットワークに関する研究

    • 2012-
      2015

      情報通信研究機構
      新世代ネットワークにおける無線通信ネットワーク仮想化とIoTに関する研究開発

    • 2011-
      2012

      情報・システム研究機構
      パーソナルデータ利活用のためにプライバシー保護について技術を研究

    • 2011

      総合研究大学院大学 博士(情報学)
      次世代無線ネットワークにおけるマルチユーザー空間分割多重化方式の最適化に関する研究

  • 主な論文・著書・講演

    論文、技術報告

    • LEO Satellite Simulation Framework for Connected Vehicles, IEEE GLOBECOM 2023

    • A scalable approach to optimize traffic signal control with federated reinforcement learning, Nature Scientific Reports

    • Communication-efficient Federated Learning for UAV Networks with Knowledge Distillation and Transfer Learning, IEEE, IEEE Global Communications Conference

    • エッジ・クラウド連携を向上させるネットワークスライス動的切り替え方式, 電子情報通信学会, 電子情報通信学会NS研究会

    • Blockchain-based Edge-assisted Knowledge Base Management for Semantic Communication in Remote Driving, IEEE, IEEE ICNP workshop

    • Low-Latency Perception Sharing Services for Connected Autonomous Vehicles, IEEE, IEEE Vehicular Technology Conference

    • Semantic Communication for Efficient Image Transmission Tasks based on Masked Autoencoders, IEEE, IEEE Vehicular Technology Conference

    • DRL-assisted Network Selection for Federated IoV, IEEE, IEEE Internet of Things Magazine

    • Meta-Networking: Beyond the Shannon Limit with Multi-faceted Information, IEEE, IEEE Network

    • Semantic Segmentation-based Semantic Communication System for Image Transmission, Elsevier, Digital Communications and Networks

    • コネクティッドカーでの利用を想定したLEO衛星通信シミュレータの開発, 電子情報通信学会, 電子情報通信学会 衛星通信研究会(2023年3月)

    • On-device Federated Learning with Fuzzy Logic based Client Selection, ACM, ACM RACS 2022

    • An Communication-Efficient Distributed Machine Learning Scheme in Vehicular Network, ACM, ACM RACS 2022

    • Toward Efficient Blockchain for the Internet of Vehicles with Hierarchical Blockchain Resource Scheduling, MDPI, MDPI Electronics, 2022

    • Fuzzy Logic based Client Selection for Federated Learning in Vehicular Networks, IEEE, IEEE Open Journal of the Computer Society, 2022

    • Communication Resources Management based on Spectrum Sensing for Vehicle Platooning, IEEE, IEEE Transactions on Intelligent Transportation Systems, 2022

    • Intelligent Network Slicing with Edge Computing for Internet of Vehicles, IEEE Access journal

    • "Driving Data to the Edge: The Challenge of Traffic Distribution", Techinical Report, Automotive Edge Computing Consortium, 2019.

    • "Building Dynamic Mapping with CUPS for Next Generation Automotive Edge Computing", IEEE CloudNet 2019.

    • "Mission Planning for UAV-based Opportunistic Disaster Recovery Networks", IEEE CCNC, Jan. 2018.

    • "Population-aware relay placement for wireless multi-hop based network disaster recovery", IEEE GLOBECOM , Dec. 2017.

    • "Spatio-temporal data-driven analysis of mobile network availability during natural disasters", IEEE ICT-DM, Dec. 2016. (Best Paper Award)

    • ほか、30件

    講演

    • Plenary Session: Wi-Fi driving innovation in the automotive market, Wi-Fi Alliance, Wi-Fi Alliance member meeting

    • AECC Panel Discussion: Edge Computing in the Automotive Industry, Informa, 5G World Summit

    • A Scalable Blockchain-based High-Definition Map Update Management System, IEEE International Smart Cities Conference 2021

    • Service-aware 5G/B5G Cellular Networks for Future Connected Vehicles, IEEE International Smart Cities Conference 2021

    • "Driving Data to the Edge - Challenges and Solutions in Automotive Edge Computing", Industry Panel, IEEE Global Communications Conference, 2019.

    • "Automotive Edge Computing for Connected Vehicles", 依頼講演, 電子情報通信学会ソサイエティ大会, 2019.

    • "Insights from the applied edge", Industry Panel, ACM/IEEE Symposium on Edge Computing, 2018.

    • Future Vehicles Summit, Industry Panel, Mobile World Congress Shanghai 2018.

    その他

    • データローカライゼーション時代への対処:連合学習タスクフォースの取り組み, Woven by Toyota, Toyota Global Summit 2023