Difference between revisions of "Resource:Seminar"

From MobiNetS
Jump to: navigation, search
 
(88 intermediate revisions by 3 users not shown)
Line 1: Line 1:
{{SemNote
{{SemNote
|time='''Thursday 9:00-10:30'''
|time='''2025-04-11 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=LoRa and its enabled LoRa wide-area network (LoRaWAN) have been seen as an important part of the next-generation network for massive Internet-of-Things (IoT). Due to LoRa's low-power and long-range nature, LoRa signals are much weaker than the noise floor, particularly in complex urban or semi-indoor environments. Therefore, weak signal decoding is critical to achieve the desired wide-area coverage in general. Existing work has shown the advantages of exploring deep neural networks (DNN) for weak signal decoding. However, the existing single-gateway based DNN decoder is hard to fully leverage the spatial information in multi-gateway scenarios. In this paper, we propose SRLoRa, an efficient DNN LoRa decoder that fully utilizes the spatial information from multiple gateways to decode extremely weak LoRa signals. Specifically, we design interleaving denoising and merging layers to improve signal quality at ultra-low SNR. We develop efficient merging on feature maps extracted by denoising DNNs to tolerate time misalignments among different signals. We define max and min operations in the merging layer to efficiently extract salient features and reduce noise, merging the features extracted from multiple gateways to guide future DNN layers to gradually improve signal quality. We implement SRLoRa with USPR N210 and commercial LoRa nodes and evaluate its performance indoors and outdoors. The results show that with four gateways, SRLoRa achieves SNR gain at 4.53--4.82 dB, which is 2.51× of Charm, leading to a 1.84× coverage area compared to standard LoRa in an urban deployment.
|abstract = While existing strategies to execute deep learning-based classification on low-power platforms assume the models are trained on all classes of interest, this paper posits that adopting context-awareness i.e. narrowing down a classification task to the current deployment context consisting of only recent inference queries can substantially enhance performance in resource-constrained environments. We propose a new paradigm, CACTUS, for scalable and efficient context-aware classification where a micro-classifier recognizes a small set of classes relevant to the current context and, when context change happens (e.g., a new class comes into the scene), rapidly switches to another suitable micro-classifier. CACTUS features several innovations, including optimizing the training cost of context-aware classifiers, enabling on-the-fly context-aware switching between classifiers, and balancing context switching costs and performance gains via simple yet effective switching policies. We show that CACTUS achieves significant benefits in accuracy, latency, and compute budget across a range of datasets and IoT platforms.
|confname=MobiHoc '23
|confname = Mobisys'24
|link=https://dl.acm.org/doi/10.1145/3565287.3610254
|link = https://dl.acm.org/doi/abs/10.1145/3643832.3661888
|title=SRLoRa: Neural-enhanced LoRa Weak Signal Decoding with Multi-gateway Super Resolution
|title= CACTUS: Dynamically Switchable Context-aware micro-Classifiers for Efficient IoT Inference
|speaker=Pengfei
|speaker= Zhenhua
|date=2024-01-18}}
|date=2025-04-18
}}
{{Latest_seminar
{{Latest_seminar
|abstract=Various interconnected Internet of Things (IoT) devices have emerged, led by the intelligence of the IoT, to realize exceptional interaction with the physical world. In this context, UAV swarm-enabled Multiple Targets Tracking (UAV-MTT), which can sense and track mobile targets for many applications such as hit-and-run, is an appealing topic. Unfortunately, UAVs cannot implement real-time MTT based on the traditional centralized pattern due to the complicated road network environment. It is also challenging to realize low-overhead UAV swarm cooperation in a distributed architecture for the real-time MTT. To address the problem, we propose a cyber-twin-based distributed tracking algorithm to update and optimize a trained digital model for real-time MTT. We then design a distributed cooperative tracking framework to promote MTT performance. In the design, both short-distance and long-distance distributed tracking cooperation manners are first realized with low energy consumption in communication by integrating resources of sensing and communication. Resource integration promotes target sensing efficiency with a highly successful tracking ratio as well. Theoretical derivation proves our algorithmic convergence. Hardware-in-the-loop simulation results demonstrate that our proposed algorithm can remarkably save 65.7% energy consumption in communication compared to other benchmarks while efficiently promoting 20.0% sensing performance.
|abstract = Nowadays, volumetric videos have emerged as an attractive multimedia application providing highly immersive watching experiences since viewers could adjust their viewports at 6 degrees-of-freedom. However, the point cloud frames composing the video are prohibitively large, and effective compression techniques should be developed. There are two classes of compression methods. One suggests exploiting the conventional video codecs (2D-based methods) and the other proposes to compress the points in 3D space directly (3D-based methods). Though the 3D-based methods feature fast coding speeds, their compression ratios are low since the failure of leveraging inter-frame redundancy. To resolve this problem, we design a patch-wise compression framework working in the 3D space. Specifically, we search rigid moves of patches via the iterative closest point algorithm and construct a common geometric structure, which is followed by color compensation. We implement our decoder on a GPU platform so that real-time decoding and rendering are realized. We compare our method with GROOT, the state-of-the-art 3D-based compression method, and it reduces the bitrate by up to 5.98×. Moreover, by trimming invisible content, our scheme achieves comparable bandwidth demand of V-PCC, the representative 2D-based method, in FoV-adaptive streaming.
|confname=TMC '23
|confname = TC'24
|link=https://ieeexplore.ieee.org/document/9839387
|link = https://ieeexplore.ieee.org/document/10360355
|title=Integrated Sensing and Communication in UAV Swarms for Cooperative Multiple Targets Tracking
|title= A GPU-Enabled Real-Time Framework for Compressing and Rendering Volumetric Videos
|speaker=Kun Wang
|speaker=Mengfan
|date=2024-01-18}}
|date=2025-04-18
{{Latest_seminar
}}
|abstract=This paper tries to answer a question: "Can we achieve spatial-selective transmission on IoT devices?" A positive answer would enable more secure data transmission among IoT devices. The challenge, however, is how to manipulate signal propagation without relying on beamforming antenna arrays which are usually unavailable on low-end IoT devices. We give an affirmative answer by introducing SpotSound, a novel acoustic communication system that exploits the diversity of multi-path indoors as a natural beamformer. By judiciously controlling the way how the information is embedded into the signal, SpotSound can make the signal decodable only when this signal propagates along a certain multipath channel. Since the multipath channel decorrelates rapidly over the distance between different receivers, Spot-Sound can ensure the signal is decodable only at the target position, achieving precise physical isolation. SpotSound is a purely software-based solution that can run on most IoT devices where speakers and microphones are widely used. We implement SpotSound on Raspberry Pi connected with COTS microphone and speaker. Experimental results show that SpotSound achieves a 0.25m2 location isolation.
 
|confname=MobiCom '23
|link=https://dl.acm.org/doi/10.1145/3570361.3592496
|title=Towards Spatial Selection Transmission for Low-end IoT devices with SpotSound
|speaker=Jiajun
|date=2024-01-18}}
{{Latest_seminar
|abstract=Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth overheads and privacy violations, but in doing so, face an ever-growing resource tension. Most notably, edge-box GPUs lack the memory needed to concurrently house the growing number of (increasingly complex) models for real-time inference. Unfortunately, existing solutions that rely on time/space sharing of GPU resources are insufficient as the required swapping delays result in unacceptable frame drops and accuracy loss. We present model merging, a new memory management technique that exploits architectural similarities between edge vision models by judiciously sharing their layers (including weights) to reduce workload memory costs and swapping delays. Our system, Gemel, efficiently integrates merging into existing pipelines by (1) leveraging several guiding observations about per-model memory usage and inter-layer dependencies to quickly identify fruitful and accuracy-preserving merging configurations, and (2) altering edge inference schedules to maximize merging benefits. Experiments across diverse workloads reveal that Gemel reduces memory usage by up to 60.7%, and improves overall accuracy by 8-39% relative to time or space sharing alone.
|confname=NSDI '23
|link=https://www.usenix.org/conference/nsdi23/presentation/padmanabhan
|title=Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge
|speaker=Mengqi
|date=2024-01-18}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 10:54, 18 April 2025

Time: 2025-04-11 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [Mobisys'24] CACTUS: Dynamically Switchable Context-aware micro-Classifiers for Efficient IoT Inference, Zhenhua
    Abstract: While existing strategies to execute deep learning-based classification on low-power platforms assume the models are trained on all classes of interest, this paper posits that adopting context-awareness i.e. narrowing down a classification task to the current deployment context consisting of only recent inference queries can substantially enhance performance in resource-constrained environments. We propose a new paradigm, CACTUS, for scalable and efficient context-aware classification where a micro-classifier recognizes a small set of classes relevant to the current context and, when context change happens (e.g., a new class comes into the scene), rapidly switches to another suitable micro-classifier. CACTUS features several innovations, including optimizing the training cost of context-aware classifiers, enabling on-the-fly context-aware switching between classifiers, and balancing context switching costs and performance gains via simple yet effective switching policies. We show that CACTUS achieves significant benefits in accuracy, latency, and compute budget across a range of datasets and IoT platforms.
  2. [TC'24] A GPU-Enabled Real-Time Framework for Compressing and Rendering Volumetric Videos, Mengfan
    Abstract: Nowadays, volumetric videos have emerged as an attractive multimedia application providing highly immersive watching experiences since viewers could adjust their viewports at 6 degrees-of-freedom. However, the point cloud frames composing the video are prohibitively large, and effective compression techniques should be developed. There are two classes of compression methods. One suggests exploiting the conventional video codecs (2D-based methods) and the other proposes to compress the points in 3D space directly (3D-based methods). Though the 3D-based methods feature fast coding speeds, their compression ratios are low since the failure of leveraging inter-frame redundancy. To resolve this problem, we design a patch-wise compression framework working in the 3D space. Specifically, we search rigid moves of patches via the iterative closest point algorithm and construct a common geometric structure, which is followed by color compensation. We implement our decoder on a GPU platform so that real-time decoding and rendering are realized. We compare our method with GROOT, the state-of-the-art 3D-based compression method, and it reduces the bitrate by up to 5.98×. Moreover, by trimming invisible content, our scheme achieves comparable bandwidth demand of V-PCC, the representative 2D-based method, in FoV-adaptive streaming.

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

Instructions

请使用Latest_seminar和Hist_seminar模板更新本页信息.

    • 修改时间和地点信息
    • 将当前latest seminar部分的code复制到这个页面中
    • 将{{Latest_seminar... 修改为 {{Hist_seminar...，并增加对应的日期信息|date=
    • 填入latest seminar各字段信息
    • link请务必不要留空，如果没有link则填本页地址 https://mobinets.org/index.php?title=Resource:Seminar
  • 格式说明
    • Latest_seminar:

{{Latest_seminar
|confname=
|link=
|title=
|speaker=
}}

    • Hist_seminar

{{Hist_seminar
|confname=
|link=
|title=
|speaker=
|date=
}}

Retrieved from "http://mobinets.cn/site/index.php?title=Resource:Seminar&oldid=3357"