Difference between revisions of "Resource:Seminar"

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===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=We develop a Multi-Agent Reinforcement Learning (MARL) method to learn scalable control policies for target tracking. Our method can handle an arbitrary number of pursuers and targets; we show results for tasks consisting up to 1000 pursuers tracking 1000 targets. We use a decentralized, partially-observable Markov Decision Process framework to model pursuers as agents receiving partial observations (range and bearing) about targets which move using fixed, unknown policies. An attention mechanism is used to parameterize the value function of the agents; this mechanism allows us to handle an arbitrary number of targets. Entropy-regularized off-policy RL methods are used to train a stochastic policy, and we discuss how it enables a hedging behavior between pursuers that leads to a weak form of cooperation in spite of completely decentralized control execution. We further develop a masking heuristic that allows training on smaller problems with few pursuers-targets and execution on much larger problems. Thorough simulation experiments and comparisons to state of the art algorithms are performed to study the scalability of the approach and robustness of performance to varying numbers of agents and targets.
|confname=MobiHoc '23
|confname=IROS 2021
|link=https://dl.acm.org/doi/10.1145/3565287.3610254
|link=https://ieeexplore.ieee.org/abstract/document/9636344
|title=SRLoRa: Neural-enhanced LoRa Weak Signal Decoding with Multi-gateway Super Resolution
|title=Scalable Reinforcement Learning Policies for Multi-Agent Control
|speaker=Pengfei
|speaker=Xianyang
|date=2024-01-18}}
|date=2024-03-22}}
{{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=With the popularity of LED infrastructure and the camera on smartphone, LED-Camera visible light communication (VLC) has become a realistic and promising technology. However, the existing LED-Camera VLC has limited throughput due to the sampling manner of camera. In this paper, by introducing a polarization dimension, we propose a hybrid modulation scheme with LED and polarization signals to boost throughput. Nevertheless, directly mixing LED and polarized signals may suffer from channel conflict. We exploit well-designed packet structure and Symmetric Return-to-Zero Inverted (SRZI) coding to overcome the conflict. In addition, in the demodulation of hybrid signal, we alleviate the noise caused by polarization on the LED signals by polarization background subtraction. We further propose a pixel-free approach to correct the perspective distortion caused by the shift of view angle by adding polarizers around the liquid crystal array. We build a prototype of this hybrid modulation scheme using off-the-shelf optical components. Extensive experimental results demonstrate that the hybrid modulation scheme can achieve reliable communication, achieving 13.4 kbps throughput, which is 400 % of the existing state-of-the-art LED-Camera VLC.
|confname=TMC '23
|confname=INFOCOM 2023
|link=https://ieeexplore.ieee.org/document/9839387
|link=https://ieeexplore.ieee.org/document/10228936/
|title=Integrated Sensing and Communication in UAV Swarms for Cooperative Multiple Targets Tracking
|title=Breaking the Throughput Limit of LED-Camera Communication via Superposed Polarization
|speaker=Kun Wang
|speaker=Mengyu
|date=2024-01-18}}
|date=2024-03-22}}
{{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}}

Revision as of 11:29, 14 March 2024

Time: Thursday 9:00-10:30
Address: 4th Research Building A518
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [IROS 2021] Scalable Reinforcement Learning Policies for Multi-Agent Control, Xianyang
    Abstract: We develop a Multi-Agent Reinforcement Learning (MARL) method to learn scalable control policies for target tracking. Our method can handle an arbitrary number of pursuers and targets; we show results for tasks consisting up to 1000 pursuers tracking 1000 targets. We use a decentralized, partially-observable Markov Decision Process framework to model pursuers as agents receiving partial observations (range and bearing) about targets which move using fixed, unknown policies. An attention mechanism is used to parameterize the value function of the agents; this mechanism allows us to handle an arbitrary number of targets. Entropy-regularized off-policy RL methods are used to train a stochastic policy, and we discuss how it enables a hedging behavior between pursuers that leads to a weak form of cooperation in spite of completely decentralized control execution. We further develop a masking heuristic that allows training on smaller problems with few pursuers-targets and execution on much larger problems. Thorough simulation experiments and comparisons to state of the art algorithms are performed to study the scalability of the approach and robustness of performance to varying numbers of agents and targets.
  2. [INFOCOM 2023] Breaking the Throughput Limit of LED-Camera Communication via Superposed Polarization, Mengyu
    Abstract: With the popularity of LED infrastructure and the camera on smartphone, LED-Camera visible light communication (VLC) has become a realistic and promising technology. However, the existing LED-Camera VLC has limited throughput due to the sampling manner of camera. In this paper, by introducing a polarization dimension, we propose a hybrid modulation scheme with LED and polarization signals to boost throughput. Nevertheless, directly mixing LED and polarized signals may suffer from channel conflict. We exploit well-designed packet structure and Symmetric Return-to-Zero Inverted (SRZI) coding to overcome the conflict. In addition, in the demodulation of hybrid signal, we alleviate the noise caused by polarization on the LED signals by polarization background subtraction. We further propose a pixel-free approach to correct the perspective distortion caused by the shift of view angle by adding polarizers around the liquid crystal array. We build a prototype of this hybrid modulation scheme using off-the-shelf optical components. Extensive experimental results demonstrate that the hybrid modulation scheme can achieve reliable communication, achieving 13.4 kbps throughput, which is 400 % of the existing state-of-the-art LED-Camera VLC.

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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