mc² – Mobile Cloud Computing

DataFog: A data-driven platform for capacity and resource management in vehicular fog computation

Research Topics

  • Algorithms for real-time task allocation in vehicular fog computing environments
  • Data-driven city-wide capacity planning for vehicular fog computing
  • Edge-powered cooperative driving in 5G networks

Project Team Members

  • Dr. Ozgur Akgul
  • Wencan Mao (doctoral student, graduated in 2023)
  • Aziza Zhanabatyrova (doctoral student, expected to graduate in 2024)
  • Xuebing Li (doctoral student, expected to graduate in 2024)
  • Dr. Byungjin Cho
  • Dr. Chao Zhu (doctoral student, graduated in 2020)
  • Dr. Marius Noreikis (doctoral student, graduated in 2019)

Research Collaborations

Open Source

Publications

  • [1] Marius Noreikis, Yu Xiao, Yuming Jiang. Edge capacity planning for real-time compute-intensive applications. in Proceedings of IEEE International Conference on Fog Computing (ICFC’19), 10 pages. Prague, Czech Republic, June 24-26, 2019. [pdf]
  • [2] Di, X., Zhao, Q, Xiao, Y., Rao, W. Traffic congestion prediction by spatiotemporal propagation patterns. In Proceedings of 20th IEEE International Conference on Mobile Data Management (MDM’19), 6 pages, Hong Kong, June 10-13, 2019. [pdf]
  • [3] Chao Zhu, Yi-Han Chiang, Abbas Mehrabi, Yu Xiao*, Antti Ylä-Jääski, and Yusheng Ji. Chameleon: Latency and resolution aware task offloading for visual-based assisted driving. IEEE Transactions on Vehicular Technology. 11 pages. 2019. [pdf]
  • [4] Chao Zhu, Abbas Mehrabi, Yu Xiao, Yinghong Wen. CrowParking: Crowdsourcing based parking navigation in autonomous driving era. InInternational Conference on Electromagnetics in Advanced Applications (ICEAA 2019), 5 pages,  September 2019 [pdf]
  • [5] Zhang, Y., Xiao, Y., Zhao, K., and Rao, W. DeepLoc: Deep Neural Network-based Telco Localization. In Proceedings of 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Mobiquitous 2019), November 12–14, 2019, Houston, United States. ACM, New York, NY, USA, 10 pages. Best paper award [pdf]
  • [6] Gao, H., Xiao, Y., Han, Y., Tian, Y., and Wang, W. A Learning-based Credible Participant Recruitment Strategy for Mobile Crowd Sensing. IEEE Internet of Things Journal. Feb 2020. 10.1109/JIOT.2020.2976778 [pdf]
  • [7] Xie, Q., Guo, T., Chen, Y., Xiao, Y., Wang, X., and Zhao, B. Deep graph convolutional networks for incident-driven traffic speed prediction. Proceedings of 29th ACM International Conference on Information and Knowledge Management (CIKM’20). [pdf]
  • [8] Zhu, C., Chiang, Y., Xiao, Y., and Ji, Y. FlexSensing: A QOI and Latency Aware Task Allocation Scheme for Vehicle-based Visual Crowdsourcing via Deep Q-Network. IEEE Internet of Things Journal. 2020. [pdf]
  • [9] B. Cho and Y. Xiao, “Learning-based decentralized offloading decision making in an adversarial environment,” in IEEE Transactions on Vehicular Technology. [pdf]
  • [10] X. Li, B. Cho and Y. Xiao, “Balancing latency and accuracy on deep video analytics at the edge”, in Proceedings of IEEE Consumer Communications & Networking Conference(CCNC 2022), January 2022, 8 pages. [pdf]
  • [11] W. Mao, O. U. Akgul, A. Mehrabi, B. Cho, Y. Xiao and A. Ylä-Jääski, “Data-Driven Capacity Planning for Vehicular Fog Computing,” in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2022.3143872. [pdf]
  • [12] H. Gao, J. Feng, Y. Xiao, B. Zhang, and W. Wang. “A UAV-assisted Multi-task Allocation Method for Mobile Crowd Sensing”, IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2022.3147871. [pdf]
  • [13] W. Mao, “PhD Forum Abstract: Capacity Planning for Vehicular Fog Computing,” 2022 IEEE International Conference on Smart Computing (SMARTCOMP), 2022, pp. 186-187, doi: 10.1109/SMARTCOMP55677.2022.00047. [pdf]
  • [14] B. Cho and Y. Xiao, “A repeated unknown game: Decentralized task offloading in vehicular fog computing,” in IEEE Transactions on Vehicular Technology, 2023. doi: 10.1109/TVT.2023.3275120.
  • [15] O. U. Akgul, W. Mao, B. Cho, and Y. Xiao, “VFogSim: A Data-driven Platform for Simulating Vehicular Fog Computing Environment,” IEEE Systems Journal (accepted for publication). 2023.
  • [16] W. Mao, O.U.Akgul, B. Cho, Y. Xiao and A. Ylä-Jääski, “On-demand Vehicular Fog Computing for Beyond 5G Networks”, IEEE Transactions on Vehicular Technology (accepted for publication). 2023.
  • [17] A. Zhanabatyrova, C. Leite, and Y. Xiao. “Automatic map update using dashcam videos”, in IEEE Internet of Things Journal. 2023. doi: 10.1109/JIOT.2023.3244693.