Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''Thursday 16:20-18:00'''
|time='''2024-12-06 10:30-12:00'''
|addr=4th Research Building A518
|addr=4th Research Building A518
|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]].
}}
}}


===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=Obtaining urban-scale vehicle trajectories is essential to understand the urban mobility and benefits various downstream applications. The mobility knowledge obtained from existing vehicle trajectory sensing techniques is typically incomplete. To fill the gap, we propose F3VeTrac , an efficient deep-learning-based vehicle trajectory recovery system that utilizes complementary characteristics of the Camera Surveillance System and the Vehicle Tracking System to obtain fine-grained, fully-road-covered, and fully-individual-penetrative ( F3 ) trajectories. F3VeTrac utilizes five well-designed modules to model the co-occurrence relationships hidden in both coarse-grained and fine-grained trajectories from the two complementary sensing systems and fuse them to recover the coarse-grained trajectories. We implement and evaluate F3VeTrac with two real-world datasets from over 100 million regular vehicle trajectories and 16 million commercial vehicle trajectories in two cities of China, together with an on-field case study based on 251 regular vehicle trajectories collected by 17 volunteers, demonstrating its great advantages over six state-of-the-art alternative schemes. Source codes are available in https://github.com/UrbanComp-BUPT/F3VeTrac . Moreover, we present a downstream application of F3VeTrac for traffic condition estimation, which obtains obvious performance gains.
|abstract = Packet routing in virtual networks requires virtual-to-physical address translation. The address mappings are updated by a single party, i.e., the network administrator, but they are read by multiple devices across the network when routing tenant packets. Existing approaches face an inherent read-write performance tradeoff: they either store these mappings in dedicated gateways for fast updates at the cost of slower forwarding or replicate them at end-hosts and suffer from slow updates.SwitchV2P aims to escape this tradeoff by leveraging the network switches to transparently cache the address mappings while learning them from the traffic. SwitchV2P brings the mappings closer to the sender, thus reducing the first packet latency and translation overheads, while simultaneously enabling fast mapping updates, all without changing existing routing policies and deployed gateways. The topology-aware data-plane caching protocol allows the switches to transparently adapt to changing network conditions and varying in-switch memory capacity.Our evaluation shows the benefits of in-network address mapping, including an up to 7.8× and 4.3× reduction in FCT and first packet latency respectively, and a substantial reduction in translation gateway load. Additionally, SwitchV2P achieves up to a 1.9× reduction in bandwidth overheads and requires order-of-magnitude fewer gateways for equivalent performance.
|confname=TMC '23
|confname =SIGCOMM'24
|link=https://ieeexplore.ieee.org/abstract/document/10209220
|link = https://dl.acm.org/doi/abs/10.1145/3651890.3672213
|title=F3VeTrac: Enabling Fine-grained, Fully-road-covered, and Fully-individual penetrative Vehicle Trajectory Recovery
|title= In-Network Address Caching for Virtual Networks
|speaker=Zhenguo
|speaker=Dongting
|date=2023-11-30}}
|date=2024-12-06
{{Latest_seminar
}}{{Latest_seminar
|abstract=In cloud gaming, interactive latency is one of the most important factors in users' experience. Although the interactive latency can be reduced through typical network infrastructures like edge caching and congestion control, the interactive latency of current cloud-gaming platforms is still far from users' satisfaction. This paper presents ZGaming, a novel 3D cloud gaming system based on image prediction, in order to eliminate the interactive latency in traditional cloud gaming systems. To improve the quality of the predicted images, we propose (1) a quality-driven 3D-block cache to reduce the "hole" artifacts, (2) a server-assisted LSTM-predicting algorithm to improve the prediction accuracy of dynamic foreground objects, and (3) a prediction-performance-driven adaptive bitrate strategy which optimizes the quality of predicted images. The experiment on the real-world cloud gaming network conditions shows that compared with existing methods, ZGaming reduces the interactive latency from 23 ms to 0 ms when providing the same video quality, or improves the video quality by 5.4 dB when keeping the interactive latency as 0 ms.
|abstract = Visible light communication (VLC) has become an important complementary means to electromagnetic communications due to its freedom from interference. However, existing Internet-of-Things (IoT) VLC links can reach only <10 meters, which has significantly limited the applications of VLC to the vast and diverse scenarios. In this paper, we propose ChirpVLC, a novel modulation method to prolong VLC distance from ≤10 meters to over 100 meters. The basic idea of ChirpVLC is to trade throughput for prolonged distance by exploiting Chirp Spread Spectrum (CSS) modulation. Specifically, 1) we modulate the luminous intensity as a sinusoidal waveform with a linearly varying frequency and design different spreading factors (SF) for different environmental conditions. 2) We design range adaptation scheme for luminance sensing range to help receivers achieve better signal-to-noise ratio (SNR). 3) ChirpVLC supports many-to-one and non-line-of-sight communications, breaking through the limitations of visible light communication. We implement ChirpVLC and conduct extensive real-world experiments. The results show that ChirpVLC can extend the transmission distance of 5W COTS LEDs to over 100 meters, and the distance/energy utility is increased by 532% compared to the existing work.
|confname=SIGCOMM '23
|confname = IDEA
|link=https://dl.acm.org/doi/pdf/10.1145/3603269.3604819
|link = https://uestc.feishu.cn/file/Pbq3bWgKJoTQObx79f3cf6gungb
|title=ZGaming: Zero-Latency 3D Cloud Gaming by Image Prediction
|title= ChirpVLC：Extending The Distance of Low-cost Visible Light Communication with CSS Modulation
|speaker=Wenjie
|speaker=Mengyu
|date=2023-11-30}}
|date=2024-12-06
{{Latest_seminar
}}
|abstract=Given the central role mobile core plays in supporting mobile network operations, the efficiency, cost-effective dynamic scalability and resilience of the core control plane are paramount. Achieving these goals, however, presents two main challenges: (i) decoupling core network state from processing; (ii) decoupling control plane processing in the core from its interface to the radio access network (RAN). To overcome them, we present CoreKube, a novel message focused and cloud-native mobile core system design, which features truly stateless workers (processing units) that interface with a common database (to hold the core network state) and with the RAN through a frontend. The fully stateless and generic nature of the workers to process any control plane message enables efficient message handling. Orchestration of containerized CoreKube components using Kubernetes, allows leveraging the latter's autoscaling and self-healing properties. We develop 4G and 5G standard-compliant CoreKube implementations, exploiting the agile development methodology enabled by CoreKube's message focused design. Results from our extensive experimental evaluations over the Powder platform relative to prior art show that CoreKube efficiently processes control plane messages, scales dynamically while using minimal compute resources and recovers seamlessly from failures.
 
|confname=MobiCom '23
|link=https://dl.acm.org/doi/abs/10.1145/3570361.3592522
|title=CoreKube: An Efficient, Autoscaling and Resilient Mobile Core System
|speaker=Qinyong
|date=2023-11-30}}
{{Latest_seminar
|abstract=Maximum target coverage by adjusting the orientation of distributed sensors is an important problem in directional sensor networks (DSNs). This problem is challenging as the targets usually move randomly but the coverage range of sensors is limited in angle and distance. Thus, it is required to coordinate sensors to get ideal target coverage with low power consumption, e.g. no missing targets or reducing redundant coverage. To realize this, we propose a Hierarchical Target-oriented Multi-Agent Coordination (HiT-MAC), which decomposes the target coverage problem into two-level tasks: targets assignment by a coordinator and tracking assigned targets by executors. Specifically, the coordinator periodically monitors the environment globally and allocates targets to each executor. In turn, the executor only needs to track its assigned targets. To effectively learn the HiT-MAC by reinforcement learning, we further introduce a bunch of practical methods, including a self-attention module, marginal contribution approximation for the coordinator, goal-conditional observation filter for the executor, etc. Empirical results demonstrate the advantage of HiT-MAC in coverage rate, learning efficiency, and scalability, comparing to baselines. We also conduct an ablative analysis on the effectiveness of the introduced components in the framework.
|confname=NeurIPS '20
|link=https://proceedings.neurips.cc/paper/2020/hash/7250eb93b3c18cc9daa29cf58af7a004-Abstract.html
|title=Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
|speaker=Jiahui
|date=2023-11-30}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 11:28, 6 December 2024

Time: 2024-12-06 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [SIGCOMM'24] In-Network Address Caching for Virtual Networks, Dongting
    Abstract: Packet routing in virtual networks requires virtual-to-physical address translation. The address mappings are updated by a single party, i.e., the network administrator, but they are read by multiple devices across the network when routing tenant packets. Existing approaches face an inherent read-write performance tradeoff: they either store these mappings in dedicated gateways for fast updates at the cost of slower forwarding or replicate them at end-hosts and suffer from slow updates.SwitchV2P aims to escape this tradeoff by leveraging the network switches to transparently cache the address mappings while learning them from the traffic. SwitchV2P brings the mappings closer to the sender, thus reducing the first packet latency and translation overheads, while simultaneously enabling fast mapping updates, all without changing existing routing policies and deployed gateways. The topology-aware data-plane caching protocol allows the switches to transparently adapt to changing network conditions and varying in-switch memory capacity.Our evaluation shows the benefits of in-network address mapping, including an up to 7.8× and 4.3× reduction in FCT and first packet latency respectively, and a substantial reduction in translation gateway load. Additionally, SwitchV2P achieves up to a 1.9× reduction in bandwidth overheads and requires order-of-magnitude fewer gateways for equivalent performance.
  2. [IDEA] ChirpVLC：Extending The Distance of Low-cost Visible Light Communication with CSS Modulation, Mengyu
    Abstract: Visible light communication (VLC) has become an important complementary means to electromagnetic communications due to its freedom from interference. However, existing Internet-of-Things (IoT) VLC links can reach only <10 meters, which has significantly limited the applications of VLC to the vast and diverse scenarios. In this paper, we propose ChirpVLC, a novel modulation method to prolong VLC distance from ≤10 meters to over 100 meters. The basic idea of ChirpVLC is to trade throughput for prolonged distance by exploiting Chirp Spread Spectrum (CSS) modulation. Specifically, 1) we modulate the luminous intensity as a sinusoidal waveform with a linearly varying frequency and design different spreading factors (SF) for different environmental conditions. 2) We design range adaptation scheme for luminance sensing range to help receivers achieve better signal-to-noise ratio (SNR). 3) ChirpVLC supports many-to-one and non-line-of-sight communications, breaking through the limitations of visible light communication. We implement ChirpVLC and conduct extensive real-world experiments. The results show that ChirpVLC can extend the transmission distance of 5W COTS LEDs to over 100 meters, and the distance/energy utility is increased by 532% compared to the existing work.

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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