Personalized and Differential Privacy-Aware Video Stream Offloading in Mobile Edge Computing
P Zhao, Z Yang, G Zhang - IEEE Transactions on Cloud Computing, 2024 - computer.org
Abstract In Mobile Edge Computing (MEC), the collaboration between end devices and
servers guarantees the low-latency and high-accuracy video stream analysis. However …
servers guarantees the low-latency and high-accuracy video stream analysis. However …
Machine Learning Assisted Video Stream Offloading for 5G MBMS Mobile Edge Computing
With the advancement of multimedia technology, 5G MBMS has become a promising
technology that makes full and effective use of network resources to provide services. Mobile …
technology that makes full and effective use of network resources to provide services. Mobile …
Privacyeye: A privacy-preserving and computationally efficient deep learning-based mobile video analytics system
Large volumes of video data recorded by the increasing mobile devices and embedded
sensors can be leveraged to answer queries of our lives, physical world and our evolving …
sensors can be leveraged to answer queries of our lives, physical world and our evolving …
Quality-aware video offloading in mobile edge computing: A data-driven two-stage stochastic optimization
W Ma, L Mashayekhy - 2021 IEEE 14th International …, 2021 - ieeexplore.ieee.org
Most camera-based mobile devices require ultra low-latency video analytics such as object
detection and action recognition. These devices face severe resource constraints, and thus …
detection and action recognition. These devices face severe resource constraints, and thus …
VC-PPQ: privacy-preserving Q-learning based video caching optimization in mobile edge networks
Mobile edge caching has great advantages in alleviating video traffic pressure and reducing
transmission delay, which is considered as a hopeful solution to improve video resource …
transmission delay, which is considered as a hopeful solution to improve video resource …
Poster: Adaptive video offloading in mobile edge computing
W Ma, L Mashayekhy - 2021 IEEE 41st International …, 2021 - ieeexplore.ieee.org
By 2022, videos will account for 82% of global Internet traffic. Many camera-based mobile
devices, though with limited resources, require ultra low-latency video analytics such as …
devices, though with limited resources, require ultra low-latency video analytics such as …
Video Offloading in Mobile Edge Computing: Dealing with Uncertainty
W Ma, L Mashayekhy - IEEE Transactions on Mobile Computing, 2024 - ieeexplore.ieee.org
Videos are projected to account for roughly 80% of global mobile data traffic by 2028. Many
camera-equipped mobile devices, such as surveillance drones, require realtime video …
camera-equipped mobile devices, such as surveillance drones, require realtime video …
EPC: a video analytics system with efficient edge-side privacy control
Edge-cloud video analytics systems capture video streams by edge cameras and send the
video streams to the cloud for analytics to support applications like video surveillance …
video streams to the cloud for analytics to support applications like video surveillance …
Privacy-aware data offloading for mobile devices in edge computing
To fulfill peoples requirements for low latency and strong computing power in mobile
devices, edge computing emerges as a paradigm for realizing service provisioning in the …
devices, edge computing emerges as a paradigm for realizing service provisioning in the …
Privacy-preserving edge video analytics
Edge video analytics (EVA) emerges as a promising paradigm to decentralize video
analytics tasks to the edge of the network and thus improve user experience by enabling …
analytics tasks to the edge of the network and thus improve user experience by enabling …