Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time=2021-11-05 8:40
|time=2021-11-12 8:40
|addr=Main Building B1-612
|addr=Main Building B1-612
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
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|link=https://ieeexplore.ieee.org/document/9525630
|link=https://ieeexplore.ieee.org/document/9525630
|title=Adaptive Configuration Selection and Bandwidth Allocation for Edge-Based Video Analytics
|title=Adaptive Configuration Selection and Bandwidth Allocation for Edge-Based Video Analytics
|speaker=Rong Cong
|speaker=Rong
}}
}}
{{Latest_seminar
{{Latest_seminar
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|link=https://arxiv.org/abs/2101.09752
|link=https://arxiv.org/abs/2101.09752
|title=AQuA: Analytical Quality Assessment for Optimizing Video Analytics Systems
|title=AQuA: Analytical Quality Assessment for Optimizing Video Analytics Systems
|speaker=Rong Cong
|speaker=Rong
}}
}}
{{Latest_seminar
{{Latest_seminar
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|link= https://dl.acm.org/doi/abs/10.1145/3447993.3483268
|link= https://dl.acm.org/doi/abs/10.1145/3447993.3483268
|title=Defuse: PCube: scaling LoRa concurrent transmissions with reception diversities
|title=Defuse: PCube: scaling LoRa concurrent transmissions with reception diversities
|speaker=Kaiwen Zheng
|speaker=Kaiwen
}}
}}


=== History ===
=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 12:56, 10 November 2021

Time: 2021-11-12 8:40
Address: Main Building B1-612
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [IEEE/ACM Transactions on Networking ( Early Access )] Adaptive Configuration Selection and Bandwidth Allocation for Edge-Based Video Analytics, Rong
    Abstract: Major cities worldwide have millions of cameras deployed for surveillance, business intelligence, traffic control, crime prevention, etc. Real-time analytics on video data demands intensive computation resources and high energy consumption. Traditional cloud-based video analytics relies on large centralized clusters to ingest video streams. With edge computing, we can offload compute-intensive analysis tasks to nearby servers, thus mitigating long latency incurred by data transmission via wide area networks. When offloading video frames from the front-end device to an edge server, the application configuration (i.e., frame sampling rate and frame resolution) will impact several metrics, such as energy consumption, analytics accuracy and user-perceived latency. In this paper, we study the configuration selection and bandwidth allocation for multiple video streams, which are connected to the same edge node sharing an upload link. We propose an efficient online algorithm, called JCAB, which jointly optimizes configuration adaption and bandwidth allocation to address a number of key challenges in edge-based video analytics systems, including edge capacity limitation, unknown network variation, intrusive dynamics of video contents. Our algorithm is developed based on Lyapunov optimization and Markov approximation, works online without requiring future information, and achieves a provable performance bound. We also extend the proposed algorithms to the multi-edge scenario in which each user or video stream has an additional choice about which edge server to connect. Extensive evaluation results show that the proposed solutions can effectively balance the analytics accuracy and energy consumption while keeping low system latency in a variety of settings.
  2. [ACM/IEEE Symposium on Edge Computing 2021] AQuA: Analytical Quality Assessment for Optimizing Video Analytics Systems, Rong
    Abstract: Millions of cameras at edge are being deployed to power a variety of different deep learning applications. However, the frames captured by these cameras are not always pristine - they can be distorted due to lighting issues, sensor noise, compression etc. Such distortions not only deteriorate visual quality, they impact the accuracy of deep learning applications that process such video streams. In this work, we introduce AQuA, to protect application accuracy against such distorted frames by scoring the level of distortion in the frames. It takes into account the analytical quality of frames, not the visual quality, by learning a novel metric, classifier opinion score, and uses a lightweight, CNN-based, object-independent feature extractor. AQuA accurately scores distortion levels of frames and generalizes to multiple different deep learning applications. When used for filtering poor quality frames at edge, it reduces high-confidence errors for analytics applications by 17%. Through filtering, and due to its low overhead (14ms), AQuA can also reduce computation time and average bandwidth usage by 25%.
  3. [MobiCom'21] Defuse: PCube: scaling LoRa concurrent transmissions with reception diversities, Kaiwen
    Abstract: This paper presents the design and implementation of PCube, a phase-based parallel packet decoder for concurrent transmissions of LoRa nodes. The key enabling technology behind PCube is a novel air-channel phase measurement technique which is able to extract phase differences of air-channels between LoRa nodes and multiple antennas of a gateway. PCube leverages the reception diversities of multiple receiving antennas of a gateway and scales the concurrent transmissions of a large number of LoRa nodes, even exceeding the number of receiving antennas at a gateway. As a phase-based parallel decoder, PCube provides a new dimension to resolve collisions and supports more concurrent transmissions by complementing time and frequency based parallel decoders. PCube is implemented and evaluated with synchronized software defined radios and off-the-shelf LoRa nodes in both indoors and outdoors. Results demonstrate that PCube can substantially outperform state-of-the-art works in terms of aggregated throughput by 4.9× and the number of concurrent nodes by up to 5×. More importantly, PCube scales well with the number of receiving antennas of a gateway, which is promising to break the barrier of concurrent transmissions.

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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