WEI, Bo

写真a

Affiliation

Faculty of Science and Engineering, School of Fundamental Science and Engineering

Job title

Assistant Professor(without tenure)

Education 【 display / non-display

  • 2015.04
    -
    2019.02

    Waseda University   Graduate School of Fundamental Science and Engineering  

  • 2012.09
    -
    2015.01

    Tianjin University   Graduate School of Computer Science and Communicaiton Engineering  

  • 2008.09
    -
    2012.06

    Tianjin University   School of Electrical and Electronic Engineering  

Research Experience 【 display / non-display

  • 2019.04
    -
    Now

    Waseda University   Faculty of Science and Engineering   Assistant Professor

  • 2020.02
    -
    2020.03

    University of Washington

Professional Memberships 【 display / non-display

  •  
     
     

    IEEE

  •  
     
     

    IEICE

 

Research Areas 【 display / non-display

  • Information network

  • Perceptual information processing

  • High performance computing

Research Interests 【 display / non-display

  • Computer Networks

  • Multimedia Communications

  • IoT

  • Deep learning

  • Quantum Computing

Papers 【 display / non-display

  • High-QoE DASH live streaming using reinforcement learning

    Bo Wei, Hang Song, Jiro Katto

    2021 IEEE/ACM International Symposium on Quality of Service (IWQoS)    2021.06

  • FRAB: A Flexible Relaxation Method for Fair, Stable, Efficient Multi-user DASH Video Streaming

    Bo Wei, Hang Song, Jiro Katto

    2021 IEEE International Conference on Communications (ICC)    2021.06

  • Performance Analysis of Adaptive Bitrate Algorithms for Multi-user DASH Video Streaming

    Bo Wei, Hang Song, Shangguang Wang, Jiro Katto

    2021 IEEE Wireless Communications and Networking Conference (WCNC)    2021.03

  • Blockchain-based data collection with efficient anomaly detection for estimating battery state-of-health

    Ruochen Jin, Bo Wei, Yongmei Luo, Tao Ren, Ruoqian Wu

    IEEE Sensors Journal    2021

     View Summary

    The number of electric vehicles in various countries has shown exponential growth so that the related industries to face the tremendous pressure of power batteries disposal. Efficient secondary use and recycling of power batteries require effective collection of battery data and reasonable estimation of battery state-of-health (SOH). In this paper, we propose a framework to collect battery charging data from different stakeholders with an anomaly detection method based on Isolation Forest with two features. Besides a score-based mechanism is adopted to do data screening and capture the data with good quality. Unlike prior works, our proposed method can exploit crowdsourced data to reduce the significant effort of battery data sensing and provide a data source scoring mechanism based on blockchain to improve the data quality and meet the requirement of reasonable estimation. In order to verify the effectiveness of the proposed collection method, a charge data test set is constructed based on the NASA battery data set. The simulation results indicate that the method increases the F-measure criteria up to 25.65% compared to the well-known anomaly detection algorithms. In addition, the proposed collection method outperforms the traditional method up to 10.9% in reducing the relative error when being used for SOH estimation.

    DOI

  • WiEps: Measurement of Dielectric Property with Commodity WiFi Device - An Application to Ethanol/Water Mixture

    Hang Song, Bo Wei, Qun Yu, Xia Xiao, Takamaro Kikkawa

    IEEE Internet of Things Journal   7 ( 12 ) 11667 - 11677  2020.12

     View Summary

    WiFi signal has become accessible everywhere, providing high-speed data transmission experience. Besides the communication service, channel state information (CSI) of the WiFi signals is widely employed for numerous Internet-of-Things (IoT) applications. Recently, most of these applications are based on the analysis of the microwave reflections caused by the physical movement of the objective. In this article, a novel contactless wireless sensing technique named WiEps is developed to measure the dielectric properties of the material, exploiting the transmission characteristics of the WiFi signals. In WiEps, the material under test is placed between the transmitter antenna and receiver antenna. A theoretical model is proposed to quantitatively describe the relationship between CSI data and dielectric properties of the material. During the experiment, the phase and amplitude of the transmitted WiFi signals are extracted from the measured CSI data. The parameters of the theoretical model are calculated using measured data from the known materials. Then, WiEps is utilized to estimate the dielectric properties of unknown materials. The proposed technique is first applied to the ethanol/water mixtures. Then, additional liquids are measured for further verification. The estimated permittivities and conductivities show good agreement with the actual values, with the average error of 4.0% and 8.9%, respectively, indicating the efficacy of WiEps. By measuring the dielectric property, this technique is promising to be applied to new IoT applications using ubiquitous WiFi signals, such as food engineering, material manufacturing process monitoring, and security check.

    DOI

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Awards 【 display / non-display

  • The World Economic Forum Global Future Council

    2019   Fellow

  • Women Techmakers Scholar

    2018   Google  

  • Best Paper Award of English Session in IEICE Technical Committee on Information and Communication Management

    2016   IEICE  

  • Otsuki Memorial Scholarship for Asia and Africa Students

    2016   Japan  

  • Best Paper Award of English Session in IEICE Technical Committee on Information and Communication Management

    2015   IEICE  

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Research Projects 【 display / non-display

  • Adaptive bitrate control strategy for ensuring high-QoE and fair video streaming in multi-user networks

Specific Research 【 display / non-display

  • Adaptive bitrate control strategy for ensuring high-QoE and fair video streaming in multi-user networks

    2020   Jiro Katto

     View Summary

    With the increasing video demand in dailynetwork traffic, it is an urgent task to develop effective algorithms tofacilitate high-quality content delivery service. Recently, numerous adaptivestreaming algorithms have been proposed to improve the user perceivedexperience. We tested the performance of current adaptation methods inmulti-user network. It is found that current algorithms perform inconsistentlyin various network scenarios. In the excessive user and limited bandwidthcases, machine learning and scheduling techniques show superiority in providinghigh and equal QoE for all users. While in the high-delay case, thebuffer-based approaches show robust performance. We also proposed a new client-sideABR control method, flexible relaxation assisted by buffer (FRAB), to achievefair, stable and efficient video streaming among different users. FRAB isevaluated in real experiments under different network conditions and comparedwith conventional multi-user ABR algorithms. The experiment resultsdemonstrated that the proposed method has superior performances in multi-userDASH video streaming.

 

Committee Memberships 【 display / non-display

  • 2021.03
    -
    Now

    IEEE CTSoc MDT Technical Committee  Member

Academic Activities 【 display / non-display

  • IEEE Internet of Things Journal

  • IEEE Transactions on Vehicular Technology

  • China Communications

  • IEEE Access