Resource: Seminar

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Time: 2021-12-17 8:40
Address: Main Building B1-612
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [MobiCom 2020] Nephalai: towards LPWAN C-RAN with physical layer compression, Wenliang
    Abstract: We propose Nephelai, a Compressive Sensing-based Cloud Radio Access Network (C-RAN), to reduce the uplink bit rate of the physical layer (PHY) between the gateways and the cloud server for multi-channel LPWANs. Recent research shows that single-channel LPWANs suffer from scalability issues. While multiple channels improve these issues, data transmission is expensive. Furthermore, recent research has shown that jointly decoding raw physical layers that are offloaded by LPWAN gateways in the cloud can improve the signal-to-noise ratio (SNR) of week radio signals. However, when it comes to multiple channels, this approach requires high bandwidth of network infrastructure to transport a large amount of PHY samples from gateways to the cloud server, which results in network congestion and high cost due to Internet data usage. In order to reduce the operation's bandwidth, we propose a novel LPWAN packet acquisition mechanism based on Compressive Sensing with a custom design dictionary that exploits the structure of LPWAN packets, reduces the bit rate of samples on each gateway, and demodulates PHY in the cloud with (joint) sparse approximation. Moreover, we propose an adaptive compression method that takes the Spreading Factor (SF) and SNR into account. Our empirical evaluation shows that up to 93.7% PHY samples can be reduced by Nephelai when SF = 9 and SNR is high without degradation in the packet reception rate (PRR). With four gateways, 1.7x PRR can be achieved with 87.5% PHY samples compressed, which can extend the battery lifetime of embedded IoT devices to 1.7.
  2. [MobiCom 2021] EMP: edge-assisted multi-vehicle perception, Jiangshu
    Abstract: Connected and Autonomous Vehicles (CAVs) heavily rely on 3D sensors such as LiDARs, radars, and stereo cameras. However, 3D sensors from a single vehicle suffer from two fundamental limitations: vulnerability to occlusion and loss of details on far-away objects. To overcome both limitations, in this paper, we design, implement, and evaluate EMP, a novel edge-assisted multi-vehicle perception system for CAVs. In EMP, multiple nearby CAVs share their raw sensor data with an edge server which then merges CAVs' individual views to form a more complete view with a higher resolution. The merged view can drastically enhance the perception quality of the participating CAVs. Our core methodological contribution is to make the sensor data sharing scalable, adaptive, and resource-efficient over oftentimes highly fluctuating wireless links through a series of novel algorithms, which are then integrated into a full-fledged cooperative sensing pipeline. Extensive evaluations demonstrate that EMP can achieve real-time processing at 24 FPS and end-to-end latency of 93 ms on average. EMP reduces the end-to-end latency by 49% to 65% compared to the traditional vehicle-to-vehicle (V2V) sharing approach without edge support. Our case studies show that cooperative sensing powered by EMP can detect hazards such as blind spots faster by 0.5 to 1.1 seconds, compared to a single vehicle's perception.

History

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

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