SAWEC: Sensing-Assisted Wireless Edge Computing
CoRR(2024)
摘要
Emerging mobile virtual reality (VR) systems will require to continuously
perform complex computer vision tasks on ultra-high-resolution video frames
through the execution of deep neural networks (DNNs)-based algorithms. Since
state-of-the-art DNNs require computational power that is excessive for mobile
devices, techniques based on wireless edge computing (WEC) have been recently
proposed. However, existing WEC methods require the transmission and processing
of a high amount of video data which may ultimately saturate the wireless link.
In this paper, we propose a novel Sensing-Assisted Wireless Edge Computing
(SAWEC) paradigm to address this issue. SAWEC leverages knowledge about the
physical environment to reduce the end-to-end latency and overall computational
burden by transmitting to the edge server only the relevant data for the
delivery of the service. Our intuition is that the transmission of the portion
of the video frames where there are no changes with respect to previous frames
can be avoided. Specifically, we leverage wireless sensing techniques to
estimate the location of objects in the environment and obtain insights about
the environment dynamics. Hence, only the part of the frames where any
environmental change is detected is transmitted and processed. We evaluated
SAWEC by using a 10K 360^∘ camera with a Wi-Fi 6 sensing system
operating at 160 MHz and performing localization and tracking. We perform
experiments in an anechoic chamber and a hall room with two human subjects in
six different setups. Experimental results show that SAWEC reduces the channel
occupation, and end-to-end latency by 93.81
improving the instance segmentation performance by 46.98
state-of-the-art WEC approaches. For reproducibility purposes, we pledge to
share our whole dataset and code repository.
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