  Advanced wearable devices are increasingly incorporating high-resolution
multi-camera systems. As state-of-the-art neural networks for processing the
resulting image data are computationally demanding, there has been growing
interest in leveraging fifth generation (5G) wireless connectivity and mobile
edge computing for offloading this processing to the cloud. To assess this
possibility, this paper presents a detailed simulation and evaluation of 5G
wireless offloading for object detection within a powerful, new smart wearable
called VIS4ION, for the Blind-and-Visually Impaired (BVI). The current VIS4ION
system is an instrumented book-bag with high-resolution cameras, vision
processing and haptic and audio feedback. The paper considers uploading the
camera data to a mobile edge cloud to perform real-time object detection and
transmitting the detection results back to the wearable. To determine the video
requirements, the paper evaluates the impact of video bit rate and resolution
on object detection accuracy and range. A new street scene dataset with labeled
objects relevant to BVI navigation is leveraged for analysis. The vision
evaluation is combined with a detailed full-stack wireless network simulation
to determine the distribution of throughputs and delays with real navigation
paths and ray-tracing from new high-resolution 3D models in an urban
environment. For comparison, the wireless simulation considers both a standard
4G-Long Term Evolution (LTE) carrier and high-rate 5G millimeter-wave (mmWave)
carrier. The work thus provides a thorough and realistic assessment of edge
computing with mmWave connectivity in an application with both high bandwidth
and low latency requirements.
