Difference between revisions of "Resource:Seminar"

From MobiNetS
Jump to: navigation, search
(wenliang updates seminars)
 
(72 intermediate revisions by 3 users not shown)
Line 1: Line 1:
{{SemNote
{{SemNote
|time='''2022-10-25 16:30'''
|time='''Friday 10:30-12:00'''
|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]].
}}
}}
Line 7: Line 7:
===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract = Barcodes and NFC have become the de facto standards in the field of automatic identification and data capture. These standards have been widely adopted for many applications, such as mobile payments, advertisements, social sharing, admission control, and so on. Recently, considerable demands require the integration of these two codes (barcode and NFC code) into a single tag for the functional complementation. To achieve the goal of "one tag, two codes" (OTTC), this work proposes CoilCode, which takes advantage of the printed electronics to fuse an NFC coil antenna into a QR code on a single layer. The proposed code could be identified by cameras and NFC readers. With the use of the conductive inks, QR code and NFC code have become an essential part of each other: the modules of the QR code facilitate the NFC chip in harvesting energy from the magnetic field, while the NFC antenna itself represents bits of the QR code. Compared to the prior dual-layer OTTC, CoilCode is more compact, cost-effective, flimsy, flexible, and environment-friendly, and also reduces the fabrication complexity considerably. We prototyped hundreds of CoilCodes and conducted comprehensive evaluations (across 4 models of NFC chips and 8 kinds of NFC readers under 13 different system configurations). CoilCode demonstrates high-quality identification results for QR code and NFC functions on a wide range of inputs and under different distortion effects.
|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.
|confname=MobiCom 2021
|confname=IPSN 2023
|link=https://dl.acm.org/doi/pdf/10.1145/3447993.3448631
|link=https://dl.acm.org/doi/10.1145/3583120.3586963
|title=One Tag, Two Codes: Identifying Optical Barcodes with NFC
|title=FLoRa: Energy-Efficient, Reliable, and Beamforming-Assisted Over-The-Air Firmware Update in LoRa Networks
|speaker=Jiangshu}}
|speaker=Kai Chen
|date=2024-05-10}}
{{Latest_seminar
{{Latest_seminar
|abstract = Recently, increasing investments in satellite-related technologies make the low earth orbit (LEO) satellite constellation a strong complement to terrestrial networks. To mitigate the limitations of the traditional satellite constellation “bent-pipe” architecture, satellite edge computing (SEC) has been proposed by placing computing resources at the LEO satellite constellation. Most existing works focus on space-air-ground integrated network architecture and SEC computing framework. Beyond these works, we are the first to investigate how to efficiently deploy services on the SEC nodes to realize robustness aware service coverage with constrained resources. Facing the challenges of spatial-temporal system dynamics and service coverage-robustness conflict, we propose a novel online service placement algorithm with a theoretical performance guarantee by leveraging Lyapunov optimization and Gibbs sampling. Extensive simulation results show that our algorithm can improve the service coverage by 4.3× compared with the baseline.
|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.
|confname=IoTJ 2022
|confname=INFOCOM 2023
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9444334
|link=https://ieeexplore.ieee.org/abstract/document/10228941/
|title=Service Coverage for Satellite Edge Computing
|title=Prophet: An Efficient Feature Indexing Mechanism for Similarity Data Sharing at Network Edge
|speaker=Qinyong}}
|speaker=Rong Cong
{{Latest_seminar
|date=2024-05-10}}
|abstract = Vehicular edge computing (VEC) is a promising paradigm based on the Internet of vehicles to provide computing resources for end users and relieve heavy traffic burden for cellular networks. In this paper, we consider a VEC network with dynamic topologies, unstable connections and unpredictable movements. Vehicles inside can offload computation tasks to available neighboring VEC clusters formed by onboard resources, with the purpose of both minimizing system energy consumption and satisfying task latency constraints. For online task scheduling, existing researches either design heuristic algorithms or leverage machine learning, e.g., deep reinforcement learning (DRL). However, these algorithms are not efficient enough because of their low searching efficiency and slow convergence speeds for large-scale networks. Instead, we propose an imitation learning enabled online task scheduling algorithm with near-optimal performance from the initial stage. Specially, an expert can obtain the optimal scheduling policy by solving the formulated optimization problem with a few samples offline. For online learning, we train agent policies by following the expert’s demonstration with an acceptable performance gap in theory. Performance results show that our solution has a significant advantage with more than 50 percent improvement compared with the benchmark.
|confname=TMC 2022
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9151371
|title=Imitation Learning Enabled Task Scheduling for Online Vehicular Edge Computing
|speaker=Zhenguo}}
 
 
=== History ===
 
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 20:19, 6 May 2024

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

Latest

  1. [IPSN 2023] FLoRa: Energy-Efficient, Reliable, and Beamforming-Assisted Over-The-Air Firmware Update in LoRa Networks, Kai Chen
    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.
  2. [INFOCOM 2023] Prophet: An Efficient Feature Indexing Mechanism for Similarity Data Sharing at Network Edge, Rong Cong
    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.

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

Template loop detected: Resource:Previous Seminars

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=2813"