Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time=2021-06-09 16:00
|time='''Friday 10:30-12:00'''
|addr=Main Building B1-612
|addr=4th Research Building A518
|note=The reading list could be found [[Resource:Reading_List|here]]. Schedules are [[Resource:Seminar_schedules|here]]. Previous seminars can be found [[Resource:Previous_Seminars|here]].
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
}}
}}


=== Latest ===
===Latest===
{{Latest_seminar
{{Latest_seminar
|confname=Topic
|abstract=Truck-drone systems, wherein trucks carrying drones drive to pre-planned positions and then free drones equipped with cameras to monitor a known number of objects with reported positions, have been used for various scenarios. An object's quality of monitoring (QoM) by a camera is defined as a function of camera focal length and monitoring distance. Improving the QoM would help downstream tasks, including object detection and recognition. The monitoring utility is the fusion of all the QoMs of an object from multiple cameras. This paper optimizes the D eployment O f T rucks A nd D rones for O bject monitoring (DOTADO) problem, i.e. , deploying a truck-drone system, where each drone is equipped with a varifocal camera, to maximize the overall monitoring utility for all objects. Firstly, we model the hybrid system and define monitoring quality and utility. Then, we discretize the solution space into deployment strategies with performance bound. To select deployment strategies, we prove the submodularity of the problem and propose a two-level greedy algorithm with a bounded approximation ratio. Finally, we devise an optimal method to adjust the strategy for energy saving and communication improvement without losing monitoring utility. We perform both simulations and field experiments to verify the proposed framework.
|link=https://mobinets.org/index.php?title=Resource:Seminar
|confname=TMC'24
|title= Path Reconstruction in Wireless Network
|link=https://ieeexplore.ieee.org/abstract/document/10440565
|speaker=Luwei Fu
|title=Joint Deployment of Truck-drone Systems for Camera-based Object Monitoring
|date=2021-06-08
|speaker=Luwei
}}
|date=2024-06-28}}
{{Latest_seminar
{{Latest_seminar
|confname=INFOCOM'2021
|abstract=Short video streaming applications have recently gained substantial traction, but the non-linear video presentation they afford swiping users fundamentally changes the problem of maximizing user quality of experience in the face of the vagaries of network throughput and user swipe timing. This paper describes the design and implementation of Dashlet, a system tailored for high quality of experience in short video streaming applications. With the insights we glean from an in-the-wild TikTok performance study and a user study focused on swipe patterns, Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages a simple, non machine learning-based model of users' swipe statistics to determine the pre-buffering order and bitrate. The net result is a system that outperforms TikTok by 28-101%, while also reducing by 30% the number of bytes wasted on downloaded video that is never watched.
|link=https://www.jianguoyun.com/p/DcPlW3AQ_LXjBxi31vkD
|confname=NSDI'23
|title= Mobility- and Load-Adaptive Controller Placement and Assignment in LEO Satellite Networks
|link=https://www.usenix.org/conference/nsdi23/presentation/li-zhuqi
|speaker=Linyuanqi Zhang
|title=Dashlet: Taming Swipe Uncertainty for Robust Short Video Streaming
|date=2021-06-08
|speaker=Mengqi
}}
|date=2024-06-28}}
 
 
=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 14:37, 26 June 2024

Time: Friday 10:30-12:00
Address: 4th Research Building A518
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [TMC'24] Joint Deployment of Truck-drone Systems for Camera-based Object Monitoring, Luwei
    Abstract: Truck-drone systems, wherein trucks carrying drones drive to pre-planned positions and then free drones equipped with cameras to monitor a known number of objects with reported positions, have been used for various scenarios. An object's quality of monitoring (QoM) by a camera is defined as a function of camera focal length and monitoring distance. Improving the QoM would help downstream tasks, including object detection and recognition. The monitoring utility is the fusion of all the QoMs of an object from multiple cameras. This paper optimizes the D eployment O f T rucks A nd D rones for O bject monitoring (DOTADO) problem, i.e. , deploying a truck-drone system, where each drone is equipped with a varifocal camera, to maximize the overall monitoring utility for all objects. Firstly, we model the hybrid system and define monitoring quality and utility. Then, we discretize the solution space into deployment strategies with performance bound. To select deployment strategies, we prove the submodularity of the problem and propose a two-level greedy algorithm with a bounded approximation ratio. Finally, we devise an optimal method to adjust the strategy for energy saving and communication improvement without losing monitoring utility. We perform both simulations and field experiments to verify the proposed framework.
  2. [NSDI'23] Dashlet: Taming Swipe Uncertainty for Robust Short Video Streaming, Mengqi
    Abstract: Short video streaming applications have recently gained substantial traction, but the non-linear video presentation they afford swiping users fundamentally changes the problem of maximizing user quality of experience in the face of the vagaries of network throughput and user swipe timing. This paper describes the design and implementation of Dashlet, a system tailored for high quality of experience in short video streaming applications. With the insights we glean from an in-the-wild TikTok performance study and a user study focused on swipe patterns, Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages a simple, non machine learning-based model of users' swipe statistics to determine the pre-buffering order and bitrate. The net result is a system that outperforms TikTok by 28-101%, while also reducing by 30% the number of bytes wasted on downloaded video that is never watched.

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

Template loop detected: Resource:Previous Seminars

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