Mobile and Edge Computing Systems

Edge computing remains a viable solution in task offloading to balance between network latency and computational power. Our research focuses on the co-design between mobile and edge systems to achieve better efficiency on mobile applications with heavy workload, such as mobile VR rendering.

FaceListener: Recognizing Human Facial Expressions via Acoustic Sensing on Commodity Headphones
FaceListener transforms the commodity headphone into an acoustic sensing device, which captures the face skin deformations caused by fa-cial muscle movements with different facial expressions. To ensure the recognition accuracy, FaceListener leverages the knowledge distillation technique to learn the subtle correlation between face skin deformation and the acoustic signal changes.
May 2022 In IPSN'22
PDF Cite DOI
FaceListener: Recognizing Human Facial Expressions via Acoustic Sensing on Commodity Headphones
Eavesdropping user credentials via GPU side channels on smartphones
This is one of the few works that demonstrate critical security vulnerabilities of mainstream GPUs (QualComm Adreno GPU on Snapdragon SoCs) on smartphones, which allow an unprivileged attacker to eavesdrop the user’s sensitive credentials such as app username and password.
March 2022 In ASPLOS'22
PDF Cite Code Video DOI Artifact
Eavesdropping user credentials via GPU side channels on smartphones
MagHacker: eavesdropping on stylus pen writing via magnetic sensing from commodity mobile devices
We present MagHacker, a new sensing system that realizes such eavesdropping attack over commodity mobile devices, which monitor and analyze the magnetic field being produced by the stylus pen’s internal magnet. It divides the continuous magnetometer readings into small segments that represent individual letters, and then translates these readings into writing trajectories for letter recognition.
June 2020 In MobiSys'20
PDF Cite Video DOI
MagHacker: eavesdropping on stylus pen writing via magnetic sensing from commodity mobile devices
DeltaVR: achieving high-performance mobile VR dynamics through pixel reuse
This work leverages the unique characteristics of image warping used in current VR applications, and fundamentally expand the scope of image warping to the entire VR lifespan to precisely capture the fluctuations of VR scene due to VR dynamics. We implemented our design over Android OS and Unity VR application engine, and demonstrated that our design can maximize the mobile VR performance over highly dynamic VR scenarios with 95% less amount of VR frame data being transmitted.
April 2019 In IPSN'18
PDF Cite DOI
DeltaVR: achieving high-performance mobile VR dynamics through pixel reuse
MUVR: Supporting Multi-User Mobile Virtual Reality with Resource Constrained Edge Cloud
MUVR aims to remove the performance constraint of highly dynamic VR appliations by adaptively reusing the redundant VR frames being rendered for different VR users. The redundancy in each frame is decided at run-time by the edge cloud, which further reuses its redundant pixels compared with other frames. The design implementation over Android OS and Unity VR demonstrated that the design can reduce edge computation burden and transmitted VR frame data.
October 2018 In SEC 2018
PDF Cite DOI
MUVR: Supporting Multi-User Mobile Virtual Reality with Resource Constrained Edge Cloud