Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-10-18 16:30'''
|time='''2022-10-25 16:30'''
|addr=4th Research Building A527-B
|addr=4th Research Building A527-B
|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 = As a representative technology of low power wide area network, LoRa has been widely adopted to many appli-cations. A fundamental question in LoRa is how to improve its reception quality in ultra-low SNR scenarios. Different from existing studies that exploit either spatial or temporal correlation for LoRa reception recovery, this paper jointly leverages the fine-grained spatial-temporal correlation among multiple gateways. We exploit the spatial and temporal correlation in LoRa packets to jointly process received signals so that the fine-grained offsets including Central Frequency Offset (CFO), Sampling Time Offset (STO) and Sampling Frequency Offset (SFO) are well compensated, and signals from multiple gateways are combined coherently. Moreover, a deep learning based soft decoding scheme is developed to integrate the energy distribution of each symbol into the decoder to further enhance the coding gain in a LoRa packet. We evaluate our work with commodity LoRa devices (i.e., Semtech SX1278) and gateways (i.e., USRP-B210) in both indoor and outdoor environments. Extensive experiment results show that our work achieves 4.6dB higher signal-to-noise ratio (SNR) and 1.5× lower bit error rate (BER) compared with existing approaches.  
|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.
|confname=ICNP 2022
|confname=MobiCom 2021
|link=https://www.jianguoyun.com/p/DXDTOyEQ_LXjBxiLjt8EIAA
|link=https://dl.acm.org/doi/pdf/10.1145/3447993.3448631
|title=CONST: Exploiting Spatial-Temporal Correlation for Multi-Gateway based Reliable LoRa Reception
|title=One Tag, Two Codes: Identifying Optical Barcodes with NFC
|speaker=Kaiwen}}
|speaker=Jiangshu}}
{{Latest_seminar
{{Latest_seminar
|abstract = This paper proposes Mandheling, the first system that enables highly resource-efficient on-device training by orchestrating the mixed-precision training with on-chip Digital Signal Processing (DSP) offloading. Mandheling fully explores the advantages of DSP in integer-based numerical calculation by four novel techniques: (1) a CPU-DSP co-scheduling scheme to mitigate the overhead from DSP-unfriendly operators; (2) a self-adaptive rescaling algorithm to reduce the overhead of dynamic rescaling in backward propagation; (3) a batch-splitting algorithm to improve the DSP cache efficiency; (4) a DSP-compute subgraph reusing mechanism to eliminate the preparation overhead on DSP. We have fully implemented Mandheling and demonstrated its effectiveness through extensive experiments. The results show that, compared to the state-of-the-art DNN engines from TFLite and MNN, Mandheling reduces the per-batch training time by 5.5× and the energy consumption by 8.9× on average. In end-to-end training tasks, Mandheling reduces up to 10.7× convergence time and 13.1× energy consumption, with only 1.9%–2.7% accuracy loss compared to the FP32 precision setting.
|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.
|confname=Mobicom 2022
|confname=IoTJ 2022
|link=https://arxiv.org/pdf/2206.07509.pdf
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9444334
|title=Mandheling: Mixed-Precision On-Device DNN Training with DSP Offloading
|title=Service Coverage for Satellite Edge Computing
|speaker=Wenjie}}
|speaker=Qinyong}}
{{Latest_seminar
{{Latest_seminar
|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.
|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.

Revision as of 11:11, 24 October 2022

Time: 2022-10-25 16:30
Address: 4th Research Building A527-B
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [MobiCom 2021] One Tag, Two Codes: Identifying Optical Barcodes with NFC, Jiangshu
    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.
  2. [IoTJ 2022] Service Coverage for Satellite Edge Computing, Qinyong
    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.
  3. [TMC 2022] Imitation Learning Enabled Task Scheduling for Online Vehicular Edge Computing, Zhenguo
    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.


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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