Mobile computing

Our work has considered a range of issues in mobile computing, from processing visual information on mobile devices, to reliably programming them, and leverage cloud and edge resources for applications.

  1. SoCC
    Scrooge: A Cost-Effective Deep Learning Inference System
    Hu, Yitao, Ghosh, Rajrup, and Govindan, Ramesh
    In SoCC ’21: ACM Symposium on Cloud Computing, Seattle, WA, USA, November 1 - 4, 2021 2021
  2. IoTDI
    Rim: Offloading Inference to the Edge
    Hu, Yitao, Pang, Weiwu, Liu, Xiaochen, Ghosh, Rajrup, Ko, Bongjun, Lee, Wei-Han, and Govindan, Ramesh
    In Proceedings of the 6th ACM/IEEE Conference on Internet of Things Design and Implementation, 2021 2021
  3. Middleware
    Olympian: Scheduling GPU Usage in a Deep Neural Network Model Serving System
    Hu, Yitao, Rallapalli, Swati, Ko, Bongjun, and Govindan, Ramesh
    In Proceedings of the 19th International Middleware Conference 2018
  4. MobiSys
    Gnome: A Practical Approach to NLOS Mitigation for GPS Positioning in Smartphones
    Liu, Xiaochen, Nath, Suman, and Govindan, Ramesh
    In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services (Mobisys) 2018
  5. Ubicomp
    ALPS: Accurate Landmark Positioning at City Scales
    Hu, Yitao, Liu, Xiaochen, Nath, Suman, and Govindan, Ramesh
    In the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) se 2016
  6. MobiSys
    Efficient Privilege De-Escalation for Ad Libraries in Mobile Apps
    Liu, Bin, Liu, Bin, Jin, Hongxia, and Govindan, Ramesh
    In The 13th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys 2015) ma 2015
  7. MobiSys
    FlexiWeb: Network-Aware Compaction for Accelerating Mobile Web Transfers
    Singh, S., Madhyastha, H., Krishnamurthy, S., and Govindan, R.
    In Proc. ACM MobiCom ma 2015