智能计算研究所

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智能计算研究所（Institute of Intelligent Computing, IIC）是隶属于山东大学计算机科学与技术学院的科研机构。研究所由成秀珍教授（IEEE Fellow）创建，目前包含教授6人、副教授3人、助理教授7人、博士及硕士研究生90余人。研究所聚焦于面向未来网络系统的智能计算理论与技术，形成了边缘智能、区块链、系统安全三大前沿研究方向，着力构建自进化、自适应、安全可信的智能计算关键理论与技术体系。 Frequency-Invariant Sensor Selection for MVDR Beamforming in Wireless Acoustic Sensor Networks

Jie Zhang; Guanghui Zhang; Lirong Dai

IEEE Transactions on Wireless Communications

Year: 2022

BibTeX

@ARTICLE{9812488,
  author={Zhang, Jie and Zhang, Guanghui and Dai, Lirong},
  journal={IEEE Transactions on Wireless Communications}, 
  title={Frequency-Invariant Sensor Selection for MVDR Beamforming in Wireless Acoustic Sensor Networks}, 
  year={2022},
  volume={21},
  number={12},
  pages={10648-10661},
  doi={10.1109/TWC.2022.3185713}}
           <div class="author">Hu, Pengfei and Li, Wenhao and Ma, Yifan and Santhalingam, Panneer Selvam and Pathak, Parth et al.</div>
           <div class="journal">IEEE Transactions on Mobile Computing</div>
           <div class="date">Year: 2022</div>         
                <a>BibTeX<textarea>@ARTICLE{9970305,
  author={Hu, Pengfei and Li, Wenhao and Ma, Yifan and Santhalingam, Panneer Selvam and Pathak, Parth and Li, Hong and Zhang, Huanle and Zhang, Guoming and Cheng, Xiuzhen and Mohapatra, Prasant},
  journal={IEEE Transactions on Mobile Computing}, 
  title={Towards Unconstrained Vocabulary Eavesdropping With Mmwave Radar Using GAN}, 
  year={2022},
  volume={},
  number={},
  pages={1-14},
  doi={10.1109/TMC.2022.3226690}}
           <div class="author">Minghui Xu, Yihao Guo, Qin Hu, Zehui Xiong, Dongxiao Yu, Xiuzhen Cheng</div>
           <div class="journal">High-Confidence Computing</div>
           <div class="date">Year: 2023</div>         
                <a>BibTeX<textarea>@article{XU2023100088,
title = {A trustless architecture of blockchain-enabled metaverse},
journal = {High-Confidence Computing},
volume = {3},
number = {1},
pages = {100088},
year = {2023},
issn = {2667-2952},
doi = {https://doi.org/10.1016/j.hcc.2022.100088},
url = {https://www.sciencedirect.com/science/article/pii/S266729522200040X},
author = {Minghui Xu and Yihao Guo and Qin Hu and Zehui Xiong and Dongxiao Yu and Xiuzhen Cheng},
keywords = {Metaverse, Blockchain, Edge computing, Trust},
abstract = {Metaverse has rekindled human beings’ desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as a key building block of metaverses. it enables transparent and trusted computing environments using tamper-resistant decentralized ledgers. Currently, blockchain supports Decentralized Finance (DeFi) and Non-fungible Tokens (NFT) for metaverses. However, the power of a blockchain has not been sufficiently exploited. In this article, we propose a novel trustless architecture of blockchain-enabled metaverse, aiming to provide efficient resource integration and allocation by consolidating hardware and software components. To realize our design objectives, we provide an On-Demand Trusted Computing Environment (OTCE) technique based on local trust evaluation. Specifically, the architecture adopts a hypergraph to represent a metaverse, in which each hyperedge links a group of users with certain relationship. Then the trust level of each user group can be evaluated based on graph analytics techniques. Based on the trust value, each group can determine its security plan on demand, free from interference by irrelevant nodes. Besides, OTCEs enable large-scale and flexible application environments (sandboxes) while preserving a strong security guarantee.}
           <div class="author">Xiaoshuang Xing, Gaofei Sun, Jin Qian, Dongxiao Yu, Xiuzhen Cheng</div>
           <div class="journal">Vehicular Communications</div>
           <div class="date">Year: 2022</div>         
                <a>BibTeX<textarea>@article{XING2022100542,
title = {Intermittent jamming for eavesdropping defense in WAVE based vehicular networks},
journal = {Vehicular Communications},
volume = {38},
pages = {100542},
year = {2022},
issn = {2214-2096},
doi = {https://doi.org/10.1016/j.vehcom.2022.100542},
url = {https://www.sciencedirect.com/science/article/pii/S2214209622000894},
author = {Xiaoshuang Xing and Gaofei Sun and Jin Qian and Dongxiao Yu and Xiuzhen Cheng},
keywords = {Vehicular networks, Eavesdropping, Physical layer security, Intermittent friendly jamming},
abstract = {Wireless Access in Vehicular Environments (WAVE) based vehicular networking has been considered as a promising technology to improve road safety and provide diverse value-added services. However, it is vulnerable to eavesdropping attacks due to the broadcasting nature of wireless communications. Eavesdropping in WAVE based vehicular network can reveal drivers' private and can be taken as preamble to other critical attacks resulting in severe threats to drivers' personal safety and property safety. In this paper, we deal with this threat by proposing an intermittent jamming (IJ) based eavesdropping defense mechanism. The optimal IJ scheme is found by solving the problem of minimizing the jamming energy cost while disabling the eavesdropping activities. The practicability of the design is ensured by considering the network specific communication properties. Numerical analysis is conducted to investigate the relationship between the optimal IJ scheme and the network scenario. Simulation study is carried out to compare the performance of our design with that of the existing continuous jamming (CJ) based scheme. The results indicate that comparing with the CJ scheme, our design uses less energy to disable eavesdropping. IJ saves more than 34% energy while achieving an average packet error rate (PER) of 87.42% at the eavesdropper, and saves more than 22% energy while achieving an average PER of 94.95% at the eavesdropper.}
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