# SAWEC: Sensing-Assisted Wireless Edge Computing

arXiv: https://arxiv.org/abs/2402.10021 · HTML: https://mlsanity.com/paper/2402.10021/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud](https://mlsanity.com/paper/2008.06402/) (2020)
- [Recent Advances in Deep Learning for Object Detection](https://mlsanity.com/paper/1908.03673/) (2019)
- [BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing](https://mlsanity.com/paper/2201.02693/) (2022)
- [MetaFi: Device-Free Pose Estimation via Commodity WiFi for Metaverse Avatar Simulation](https://mlsanity.com/paper/2208.10414/) (2022)
- [mD-Track: Leveraging Multi-Dimensionality in Passive Indoor Wi-Fi Tracking](https://mlsanity.com/paper/1812.03103/) (2018)
- [ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles](https://mlsanity.com/paper/2010.10754/) (2020)
- [Event Camera Based Real-Time Detection and Tracking of Indoor Ground Robots](https://mlsanity.com/paper/2102.11916/) (2021)
- [DensePose From WiFi](https://mlsanity.com/paper/2301.00250/) (2022)
- [Eliminating the Barriers: Demystifying Wi-Fi Baseband Design and Introducing the PicoScenes Wi-Fi Sensing Platform](https://mlsanity.com/paper/2010.10233/) (2020)
- [3D Human Mesh Construction Leveraging Wi-Fi](https://mlsanity.com/paper/2210.10957/) (2022)

## Abstract (arXiv)

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 360circ with a Wi-Fi 6 sensing system operating at 160 MHz and performing localization and tracking. We considered instance segmentation and object detection as benchmarking tasks for performance evaluation. We carried out experiments in an anechoic chamber and an entrance hall with two human subjects in six different setups. Experimental results show that SAWEC reduces both the channel occupation and end-to-end latency by more than 90% while improving the instance segmentation and object detection performance with respect to state-of-the-art WEC approaches.