Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2023-05-04 9:30'''
|time='''2023-05-11 9:30'''
|addr=4th Research Building A527-B
|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]].
}}
}}
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===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=In vehicular ad hoc networks (VANETs), quick and reliable multi-hop broadcasting is important for the dissemination of emergency warning messages. By scheduling multiple nodes to transmit messages concurrently and cooperatively, cooperative transmission based broadcast schemes may yield much better broadcast performance than conventional broadcast schemes. However, a cooperative transmission requires multiple relays to achieve strict synchronization on both time and frequency, which may induce high cost for a cooperative transmission process. In this paper, we analyze the cost and benefit of a cooperative transmission for data broadcasting in vehicular networks, and introduce a new metric called the single-hop broadcast efficiency (SBE) to evaluate the overall broadcast performance. We propose an efficient, non-deterministic cooperation mechanism to reduce the cooperation cost. The mechanism maximizes the expected broadcast performance by selecting cooperators with the largest expected SBE value for a lead relay, and initiates cooperative broadcasting process when the expected SBE value is larger than that of a single-relay based broadcasting. Based on the non-deterministic mechanism, we propose an efficient, cooperative transmission based opportunistic broadcast (ECTOB) scheme which further utilizes rebroadcast to improve the reliability of the broadcast scheme. Simulation results show that the proposed scheme outperforms the conventional ones.
|abstract=This paper presents a Long Range (LoRa) physical-layer data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average, min, max) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the phase-asynchronous superimposed signal. Different from the analog approach which requires additional power synchronization and phase synchronization, our MRP-based digital approach is compatible with commercial LoRa nodes and is more reliable. Different from traditional MPR approaches that are designed for the collision decoding scenario, our new MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses a new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol, 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes' data, and 3) a soft-decision packet decoding algorithm that utilizes the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms the state-of-the-art MPR decoder by 5.3× in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms the state-of-the-art MPR scheme by at least 2.1× for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.
|confname=TMC 2023
|confname=INFOCOM 2023
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9519523
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9519523
|title=An Efficient Cooperative Transmission Based Opportunistic Broadcast Scheme in VANETs
|title=Quick and Reliable LoRa Physical-layer Data Aggregation through Multi-Packet Reception
|speaker=Luwei}}
|speaker=Kaiwen}}
{{Latest_seminar
{{Latest_seminar
|abstract = Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance. Most existing approaches only tackle the heterogeneity challenge by restricting the local model update in client, ignoring the performance drop caused by direct global model aggregation. Instead, we propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG), which relieves the issue of direct model aggregation. Concretely, FedFTG explores the input space of local models through a generator, and uses it to transfer the knowledge from local models to the global model. Besides, we propose a hard sample mining scheme to achieve effective knowledge distillation throughout the training. In addition, we develop customized label sampling and class-level ensemble to derive maximum utilization of knowledge, which implicitly mitigates the distribution discrepancy across clients. Extensive experiments show that our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
|abstract = Real-time depth estimation is critical for the increasingly popular augmented reality and virtual reality applications on mobile devices. Yet existing solutions are insufficient as they require expensive depth sensors or motion of the device, or have a high latency. We propose MobiDepth, a real-time depth estimation system using the widely-available on-device dual cameras. While binocular depth estimation is a mature technique, it is challenging to realize the technique on commodity mobile devices due to the different focal lengths and unsynchronized frame flows of the on-device dual cameras and the heavy stereo-matching algorithm.
|confname=CVPR 2022
To address the challenges, MobiDepth integrates three novel techniques: 1) iterative field-of-view cropping, which crops the field-of-views of the dual cameras to achieve the equivalent focal lengths for accurate epipolar rectification; 2) heterogeneous camera synchronization, which synchronizes the frame flows captured by the dual cameras to avoid the displacement of moving objects across the frames in the same pair; 3) mobile GPU-friendly stereo matching, which effectively reduces the latency of stereo matching on a mobile GPU. We implement MobiDepth on multiple commodity mobile devices and conduct comprehensive evaluations. Experimental results show that MobiDepth achieves real-time depth estimation of 22 frames per second with a significantly reduced depth-estimation error compared with the baselines. Using MobiDepth, we further build an example application of 3D pose estimation, which significantly outperforms the state-of-the-art 3D pose-estimation method, reducing the pose-estimation latency and error by up to 57.1% and 29.5%, respectively.
|link=https://arxiv.org/pdf/2203.09249.pdf
|confname=Mobicom 2022
|title=Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning
|link=https://dl.acm.org/doi/pdf/10.1145/3495243.3560517
|speaker=Jiaqi}}
|title=MobiDepth: real-time depth estimation using on-device dual cameras
|speaker=Wenjie}}
{{Latest_seminar
{{Latest_seminar
|abstract = Visible light communication (VLC) systems relying on commercial-off-the-shelf (COTS) devices have gathered momentum recently, due to the pervasive adoption of LED lighting and mobile devices. However, the achievable throughput by such practical systems is still several orders below those claimed by controlled experiments with specialized devices. In this paper, we engineer CoLight aiming to boost the data rate of the VLC system purely built upon COTS devices. CoLight adopts COTS LEDs as its transmitter, but it innovates in its simple yet delicate driver circuit wiring an array of LED chips in a combinatorial manner. Consequently, modulated signals can directly drive the on-off procedures of individual chip groups, so that the spatially synthesized light emissions exhibit a varying luminance following exactly the modulation symbols. To obtain a readily usable receiver, CoLight interfaces a COTS PD with a smartphone through the audio jack, and it also has an alternative MCU-driven circuit to emulate a future integration into the phone. The evaluations on CoLight are both promising and informative: they demonstrate a throughput up to 80 kbps at a distance of 2 m, while suggesting various potentials to further enhance the performance.judiciously allocating 15.81 -- 37.67% idle resources on frames that tend to yield greater marginal benefits from enhancement.
|abstract = Collaborative edge computing (CEC) is an emerging paradigm enabling sharing of the coupled data, computation, and networking resources among heterogeneous geo-distributed edge nodes. Recently, there has been a trend to orchestrate and schedule containerized application workloads in CEC, while Kubernetes has become the de-facto standard broadly adopted by the industry and academia. However, Kubernetes is not preferable for CEC because its design is not dedicated to edge computing and neglects the unique features of edge nativeness. More specifically, Kubernetes primarily ensures resource provision of workloads while neglecting the performance requirements of edge-native applications, such as throughput and latency. Furthermore, Kubernetes neglects the inner dependencies of edge-native applications and fails to consider data locality and networking resources, leading to inferior performance. In this work, we design and develop ENTS, the first edge-native task scheduling system, to manage the distributed edge resources and facilitate efficient task scheduling to optimize the performance of edge-native applications. ENTS extends Kubernetes with the unique ability to collaboratively schedule computation and networking resources by comprehensively considering job profile and resource status. We showcase the superior efficacy of ENTS with a case study on data streaming applications. We mathematically formulate a joint task allocation and flow scheduling problem that maximizes the job throughput. We design two novel online scheduling algorithms to optimally decide the task allocation, bandwidth allocation, and flow routing policies. The extensive experiments on a real-world edge video analytics application show that ENTS achieves 43% -220% higher average job throughput compared with the state-of-the-art.
|confname=TMC 2021
|confname=SEC 2022
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8978742
|link=https://ieeexplore.ieee.org/abstract/document/9996714
|title=Pushing the Data Rate of Practical VLC via Combinatorial Light Emission
|title=ENTS: An Edge-native Task Scheduling System for Collaborative Edge Computing
|speaker=Mengyu}}
|speaker=Qinyong}}




Revision as of 15:48, 10 May 2023

Time: 2023-05-11 9:30
Address: 4th Research Building A518
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [INFOCOM 2023] Quick and Reliable LoRa Physical-layer Data Aggregation through Multi-Packet Reception, Kaiwen
    Abstract: This paper presents a Long Range (LoRa) physical-layer data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average, min, max) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the phase-asynchronous superimposed signal. Different from the analog approach which requires additional power synchronization and phase synchronization, our MRP-based digital approach is compatible with commercial LoRa nodes and is more reliable. Different from traditional MPR approaches that are designed for the collision decoding scenario, our new MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses a new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol, 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes' data, and 3) a soft-decision packet decoding algorithm that utilizes the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms the state-of-the-art MPR decoder by 5.3× in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms the state-of-the-art MPR scheme by at least 2.1× for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.
  2. [Mobicom 2022] MobiDepth: real-time depth estimation using on-device dual cameras, Wenjie
    Abstract: Real-time depth estimation is critical for the increasingly popular augmented reality and virtual reality applications on mobile devices. Yet existing solutions are insufficient as they require expensive depth sensors or motion of the device, or have a high latency. We propose MobiDepth, a real-time depth estimation system using the widely-available on-device dual cameras. While binocular depth estimation is a mature technique, it is challenging to realize the technique on commodity mobile devices due to the different focal lengths and unsynchronized frame flows of the on-device dual cameras and the heavy stereo-matching algorithm.

To address the challenges, MobiDepth integrates three novel techniques: 1) iterative field-of-view cropping, which crops the field-of-views of the dual cameras to achieve the equivalent focal lengths for accurate epipolar rectification; 2) heterogeneous camera synchronization, which synchronizes the frame flows captured by the dual cameras to avoid the displacement of moving objects across the frames in the same pair; 3) mobile GPU-friendly stereo matching, which effectively reduces the latency of stereo matching on a mobile GPU. We implement MobiDepth on multiple commodity mobile devices and conduct comprehensive evaluations. Experimental results show that MobiDepth achieves real-time depth estimation of 22 frames per second with a significantly reduced depth-estimation error compared with the baselines. Using MobiDepth, we further build an example application of 3D pose estimation, which significantly outperforms the state-of-the-art 3D pose-estimation method, reducing the pose-estimation latency and error by up to 57.1% and 29.5%, respectively.

  1. [SEC 2022] ENTS: An Edge-native Task Scheduling System for Collaborative Edge Computing, Qinyong
    Abstract: Collaborative edge computing (CEC) is an emerging paradigm enabling sharing of the coupled data, computation, and networking resources among heterogeneous geo-distributed edge nodes. Recently, there has been a trend to orchestrate and schedule containerized application workloads in CEC, while Kubernetes has become the de-facto standard broadly adopted by the industry and academia. However, Kubernetes is not preferable for CEC because its design is not dedicated to edge computing and neglects the unique features of edge nativeness. More specifically, Kubernetes primarily ensures resource provision of workloads while neglecting the performance requirements of edge-native applications, such as throughput and latency. Furthermore, Kubernetes neglects the inner dependencies of edge-native applications and fails to consider data locality and networking resources, leading to inferior performance. In this work, we design and develop ENTS, the first edge-native task scheduling system, to manage the distributed edge resources and facilitate efficient task scheduling to optimize the performance of edge-native applications. ENTS extends Kubernetes with the unique ability to collaboratively schedule computation and networking resources by comprehensively considering job profile and resource status. We showcase the superior efficacy of ENTS with a case study on data streaming applications. We mathematically formulate a joint task allocation and flow scheduling problem that maximizes the job throughput. We design two novel online scheduling algorithms to optimally decide the task allocation, bandwidth allocation, and flow routing policies. The extensive experiments on a real-world edge video analytics application show that ENTS achieves 43% -220% higher average job throughput compared with the state-of-the-art.


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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