Difference between revisions of "Resource:Seminar"

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===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=LoRa has emerged as one of the promising long-range and low-power wireless communication technologies for Internet of Things (IoT). With the massive deployment of LoRa networks, the ability to perform Firmware Update Over-The-Air (FUOTA) is becoming a necessity for unattended LoRa devices. LoRa Alliance has recently dedicated the specification for FUOTA, but the existing solution has several drawbacks, such as low energy efficiency, poor transmission reliability, and biased multicast grouping. In this paper, we propose a novel energy-efficient, reliable, and beamforming-assisted FUOTA system for LoRa networks named FLoRa, which is featured with several techniques, including delta scripting, channel coding, and beamforming. In particular, we first propose a novel joint differencing and compression algorithm to generate the delta script for processing gain, which unlocks the potential of incremental FUOTA in LoRa networks. Afterward, we design a concatenated channel coding scheme to enable reliable transmission against dynamic link quality. The proposed scheme uses a rateless code as outer code and an error detection code as inner code to achieve coding gain. Finally, we design a beamforming strategy to avoid biased multicast and compromised throughput for power gain. Experimental results on a 20-node testbed demonstrate that FLoRa improves network transmission reliability by up to 1.51 × and energy efficiency by up to 2.65 × compared with the existing solution in LoRaWAN.
|abstract=In this paper, we focus on the problem of efficiently locating a target object described with free-form text using a mobile robot equipped with vision sensors (e.g., an RGBD camera). Conventional active visual search predefines a set of objects to search for, rendering these techniques restrictive in practice. To provide added flexibility in active visual searching, we propose a system where a user can enter target commands using free-form text; we call this system Zero-shot Active Visual Search (ZAVIS). ZAVIS detects and plans to search for a target object inputted by a user through a semantic grid map represented by static landmarks (e.g., desk or bed). For efficient planning of object search patterns, ZAVIS considers commonsense knowledge-based co-occurrence and predictive uncertainty while deciding which landmarks to visit first. We validate the proposed method with respect to SR (success rate) and SPL (success weighted by path length) in both simulated and real-world environments. The proposed method outperforms previous methods in terms of SPL in simulated scenarios, and we further demonstrate ZAVIS with a Pioneer-3AT robot in real-world studies.
|confname=IPSN 2023
|confname=ICRA 2023
|link=https://dl.acm.org/doi/10.1145/3583120.3586963
|link=https://ieeexplore.ieee.org/document/10161345
|title=FLoRa: Energy-Efficient, Reliable, and Beamforming-Assisted Over-The-Air Firmware Update in LoRa Networks
|title=Zero-shot Active Visual Search (ZAVIS): Intelligent Object Search for Robotic Assistants
|speaker=Kai Chen
|speaker=Zhenhua
|date=2024-05-10}}
|date=2024-05-24}}
{{Latest_seminar
{{Latest_seminar
|abstract=As a promising infrastructure, edge storage systems have drawn many attempts to efficiently distribute and share data among edge servers. However, it remains open to meeting the increasing demand for similarity retrieval across servers. The intrinsic reason is that the existing solutions can only return an exact data match for a query while more general edge applications require the data similar to a query input from any server. To fill this gap, this paper pioneers a new paradigm to support high-dimensional similarity search at network edges. Specifically, we propose Prophet, the first known architecture for similarity data indexing. We first divide the feature space of data into plenty of subareas, then project both subareas and edge servers into a virtual plane where the distances between any two points can reflect not only data similarity but also network latency. When any edge server submits a request for data insert, delete, or query, it computes the data feature and the virtual coordinates; then iteratively forwards the request through greedy routing based on the forwarding tables and the virtual coordinates. By Prophet, similar high-dimensional features would be stored by a common server or several nearby servers. Compared with distributed hash tables in P2P networks, Prophet requires logarithmic servers to access for a data request and reduces the network latency from the logarithmic to the constant level of the server number. Experimental results indicate that Prophet achieves comparable retrieval accuracy and shortens the query latency by 55%~70% compared with centralized schemes.
|abstract=Network monitoring systems struggle with the issue that the measurement data is incomplete, with only a subset of origin-destination (OD) pairs or time slots observed, due to the high deployment and measurement cost. Recent studies show that the missing data can be inferred from partial measurements using neural network models and tensor methods. However, these recovery approaches fail to achieve accuracy, adaptability and high speed, simultaneously. In this paper, we propose RecMon, a deep learning-based data recovery system that satisfies the above three criteria. A global spatio-temporal attention mechanism and a data augmentation algorithm are proposed to improve the recovery accuracy. A semi-supervised learning-based scheme is devised for fast and effective model updates. We conduct extensive experiments on three real-world datasets to compare RecMon with four state-of-the-art methods in terms of online recovery performance. The experimental results show that RecMon can adapt to the latest state of the network and accurately recover network measurement data in less than 100 milliseconds. When 90% of the data is missing, the recovery accuracy of RecMon improves over the strongest baseline method by 22.7%, 16.0%, and 8.2% in the three datasets, respectively.
|confname=INFOCOM 2023
|confname=INFOCOM 2023
|link=https://ieeexplore.ieee.org/abstract/document/10228941/
|link=https://xplorestaging.ieee.org/document/10229025
|title=Prophet: An Efficient Feature Indexing Mechanism for Similarity Data Sharing at Network Edge
|title=RecMon: A Deep Learning-based Data Recovery System for Network Monitoring
|speaker=Rong Cong
|speaker=Zhenguo
|date=2024-05-10}}
|date=2024-05-24}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 16:10, 22 May 2024

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

Latest

  1. [ICRA 2023] Zero-shot Active Visual Search (ZAVIS): Intelligent Object Search for Robotic Assistants, Zhenhua
    Abstract: In this paper, we focus on the problem of efficiently locating a target object described with free-form text using a mobile robot equipped with vision sensors (e.g., an RGBD camera). Conventional active visual search predefines a set of objects to search for, rendering these techniques restrictive in practice. To provide added flexibility in active visual searching, we propose a system where a user can enter target commands using free-form text; we call this system Zero-shot Active Visual Search (ZAVIS). ZAVIS detects and plans to search for a target object inputted by a user through a semantic grid map represented by static landmarks (e.g., desk or bed). For efficient planning of object search patterns, ZAVIS considers commonsense knowledge-based co-occurrence and predictive uncertainty while deciding which landmarks to visit first. We validate the proposed method with respect to SR (success rate) and SPL (success weighted by path length) in both simulated and real-world environments. The proposed method outperforms previous methods in terms of SPL in simulated scenarios, and we further demonstrate ZAVIS with a Pioneer-3AT robot in real-world studies.
  2. [INFOCOM 2023] RecMon: A Deep Learning-based Data Recovery System for Network Monitoring, Zhenguo
    Abstract: Network monitoring systems struggle with the issue that the measurement data is incomplete, with only a subset of origin-destination (OD) pairs or time slots observed, due to the high deployment and measurement cost. Recent studies show that the missing data can be inferred from partial measurements using neural network models and tensor methods. However, these recovery approaches fail to achieve accuracy, adaptability and high speed, simultaneously. In this paper, we propose RecMon, a deep learning-based data recovery system that satisfies the above three criteria. A global spatio-temporal attention mechanism and a data augmentation algorithm are proposed to improve the recovery accuracy. A semi-supervised learning-based scheme is devised for fast and effective model updates. We conduct extensive experiments on three real-world datasets to compare RecMon with four state-of-the-art methods in terms of online recovery performance. The experimental results show that RecMon can adapt to the latest state of the network and accurately recover network measurement data in less than 100 milliseconds. When 90% of the data is missing, the recovery accuracy of RecMon improves over the strongest baseline method by 22.7%, 16.0%, and 8.2% in the three datasets, respectively.

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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