Zhishang Wang

Zhishang Wang

Postdoctoral Researcher

Division of Computer Engineering
Department of Computer Science and Engineering
The University of Aizu

Email: zwang@u-aizu.ac.jp
Tel.: +81 0242-37-2500 (Int. 3204)

Experience

Postdoctoral Researcher, The University of Aizu, 2023 April – now.
AI Engineer, Aizu Computer Science Laboratories, 2020 January – 2021 March.

Education

  • Ph.D. in Computer Science and Engineering, The University of Aizu, Japan, 2023
  • M.Sc. in Computer Science, University of Freiburg, Germany, 2019
  • B.Sc. in Computer Science, Wuhan University, China, 2014
  • Research

    AEBiS

    A Virtual Power Plant (VPP) is a network of distributed power generating units, flexible power consumers, and storage systems. A VPP balances the load on the grid by allocating the power generated by different linked units during periods of peak load. Demand-side energy equipment, such as Electric Vehicles (EVs) and mobile robots, can also balance the energy supply-demand when effectively deployed. However, fluctuation of the power generated by the various power units makes the supply power balance a challenging goal. Moreover, the communication security between a VPP aggregator and end facilities is critical and has not been carefully investigated. An AI-enabled, blockchain-based electric vehicle integration system is developed for power management in a smart grid platform based on EV and solar carport. We have developed a low-power AI-chip and various software tools for EV charge prediction, in which the EV fleet is employed as a consumer and as a supplier of electrical energy.

    Trustworthy Multi-Blockchain-Based Collaborative Learning

    Collaborative edge learning has emerged in various domains like vehicular networks and medical care, allowing local model training on edge devices while preserving privacy. A hybrid clustered blockchain method (HCB) is proposed for collaborative edge learning, where model transmission is performed in an on-chain-merge-off-chain manner. Each cluster performs an off-chain transmission of local model updates and an on-chain distribution of global model updates, and elects a delegate node to serve as a model aggregator. The delegate nodes form a main blockchain in which the global model updates of each cluster are exchanged. A delegate-based adaptive model aggregation for robust collaborative learning called DAMA-RCL ensures high-quality model selection and aggregation during collaborative learning. A disassembling-reassembling method is also introduced to enable practical model transmission on the blockchain network.

    Patent

    Patent Pending

  • A. Ben Abdallah, Zhishang Wang, Masayuki Hisada, ”An electricity trading system and an electricity trading method [電力取引システム及び電力取引方法に関する]”, 特願2022-022472
  • A. Ben Abdallah, Zhishang Wang, Khanh N. Dang, Masayuki Hisada, ”EV Power Consumption Prediction Method and System for Power Management in Smart Grid [スマートグリッドにおける電力管理のためのEV消費電力予測方法とシステム]”, 特願2023-020162
  • A. Ben Abdallah, Zhishang Wang, K. N. Dang, Masayuki Hisada, ”Self Controlled Urushi Painting System/自己制御漆ロボット手描きプラッ トフォームとシステム”, 特願2024-TC

    Selected Publications

    • Z. Wang, M. Hisada and A. B. Abdallah, "A Hybrid Clustered Approach for Enhanced Communication and Model Performance in Blockchain-Based Collaborative Learning," in IEEE Access, doi: 10.1109/ACCESS.2024.3359272.
    • Z. Wang and A. Ben Abdallah, "A Robust Multi-Stage Power Consumption Prediction Method in a Semi-Decentralized Network of Electric Vehicles,” in IEEE Access, vol. 10, pp. 37082-37096, 2022, doi: 10.1109/ACCESS.2022.3163455.
    • Z. Wang, M. Ogbodo, H. Huang, C. Qiu, M. Hisada and A. B. Abdallah, “AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform," in IEEE Access, vol. 8, pp. 226409-226421, 2020, doi: 10.1109/ACCESS.2020.3044612.
    • Y. Liang, Z. Wang and A. B. Abdallah, "Robust Vehicle-to-Grid Energy Trading Method Based on Smart Forecast and Multi-Blockchain Network," in IEEE Access, vol. 12, pp. 8135-8153, 2024, doi: 10.1109/ACCESS.2024.3352631.
    • Y. Liang, Z. Wang and A. B. Abdallah, "V2GNet: Robust Blockchain-Based Energy Trading Method and Implementation in Vehicle-to-Grid Network," in IEEE Access, vol. 10, pp. 131442-131455, 2022, doi: 10.1109/ACCESS.2022.3229432.
    • Z. Wang, K. N. Dang and A. Ben Abdallah, "Interlinked Chain Method for Blockchain-Based Collaborative Learning in Vehicular Networks," 2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), Singapore, 2023, pp. 354-359, doi: 10.1109/MCSoC60832.2023.00059.
    • H. Huang, M. Ogbodo, Z. Wang, C. Qiu, M. Hisada and A. B. Abdallah, “Smart Energy Management System based on Reconfigurable AI Chip and Electrical Vehicles,” 2021 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju Island, Korea (South), 2021, pp. 233-238, doi: 10.1109/BigComp51126.2021.00051.

    Oral Presentation

    • Postgraduate Forum of ACM International Conference on Research in Adaptive and Convergent Systems 2022, Aizuwakamatsu, Japan. October 3-6, 2022.

    Service

    • Special Session/Issue
    • IEEE MCSoC 2023: ‘‘Special Session on Parallel/Distributed, Grid, and Cloud Computing’’, [URL]

    Awards

  • Outstanding Research Assistant (AY2021), The University of Aizu.
  • Best Presentation Award. Postgraduate Forum of ACM International Conference on Research in Adaptive and Convergent Systems 2022, Aizuwakamatsu, Japan. October 3-6, 2022.