Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time=2021-09-17 8:40
|time='''Friday 10:30-12:00'''
|addr=Main Building B1-612
|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]].
}}
}}
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===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=Drowsy driving is one of the biggest threats to driving safety, which has drawn much public attention in recent years. Thus, a simple but robust system that can remind drivers of drowsiness levels with off-the-shelf devices (e.g., smartphones) is very necessary. With this motivation, we explore the feasibility of using acoustic sensors on smartphones to detect drowsy driving. Through analyzing real driving data to study characteristics of drowsy driving, we find some unique patterns of Doppler shift caused by three typical drowsy behaviours (i.e., nodding, yawning and operating steering wheel), among which operating steering wheels is also related to drowsiness levels. Then, a real-time Drowsy Driving Detection system named D^3 -Guard is proposed based on the acoustic sensing abilities of smartphones. We adopt several effective feature extraction methods, and carefully design a high-accuracy detector based on LSTM networks for the early detection of drowsy driving. Besides, measures to distinguish drowsiness levels are also introduced in the system by analyzing the data of operating steering wheel. Through extensive experiments with five drivers in real driving environments, D 3 -Guard detects drowsy driving actions with an average accuracy of 93.31%, as well as classifies drowsiness levels with an average accuracy of 86%.
|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=TMC2021
|confname=IPSN 2023
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9055089
|link=https://dl.acm.org/doi/10.1145/3583120.3586963
|title=Real-Time Detection for Drowsy Driving via Acoustic Sensing on Smartphones
|title=FLoRa: Energy-Efficient, Reliable, and Beamforming-Assisted Over-The-Air Firmware Update in LoRa Networks
|speaker=Shiqi Hu
|speaker=Kai Chen
}}
|date=2024-05-10}}
{{Latest_seminar
{{Latest_seminar
|abstract=The emerging mobile-edge computing paradigm provides opportunities for the resource-hungry mobile devices (MDs) to migrate computation. In order to satisfy the requirements of MDs in terms of latency and energy consumption, recent researches proposed diverse computation offloading schemes. However, they either fail to consider the potential computing resources at the edge, or ignore the selfish behavior of users and the dynamic resource adaptability. To this end, we study the computation offloading problem and take into consideration the dynamic available resource of idle devices and the selfish behavior of users. Furthermore, we propose a game theoretic offloading method by regarding the computation offloading process as a resource contention game, which minimizes the individual task execution cost and the system overhead. Utilizing the potential game, we prove the existence of Nash equilibrium (NE), and give a lightweight algorithm to help the game reach a NE, wherein each user can find an optimal offloading strategy based on three contention principles. Additionally, we conduct analysis of computational complexity and the Price of Anarchy (PoA), and deploy three baseline methods to compare with our proposed scheme. Numerical results illustrate that our scheme can provide high-quality services to users, and also demonstrate the effectiveness, scalability and dynamic resource adaptability of our proposed algorithm in a multiuser network.
|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=IoTJ2021
|confname=INFOCOM 2023
|link=https://ieeexplore.ieee.org/abstract/document/9386238
|link=https://ieeexplore.ieee.org/abstract/document/10228941/
|title=D2D-Enabled Mobile-Edge Computation Offloading for Multiuser IoT Network
|title=Prophet: An Efficient Feature Indexing Mechanism for Similarity Data Sharing at Network Edge
|speaker=Wenjie Huang
|speaker=Rong Cong
}}
|date=2024-05-10}}
{{Latest_seminar
|abstract=The Long Range (LoRa) protocol for low-power wide-area networks (LPWANs) is a strong candidate to enable the massive roll-out of the Internet of Things (IoT) because of its low cost, impressive sensitivity (-137dBm), and massive scalability potential. As tens of thousands of tiny LoRa devices are deployed over large geographic areas, a key component to the success of LoRa will be the development of reliable and robust authentication mechanisms. To this end, Radio Frequency Fingerprinting (RFFP) through deep learning (DL) has been heralded as an effective zero-power supplement or alternative to energy-hungry cryptography. Existing work on LoRa RFFP has mostly focused on small-scale testbeds and low-dimensional learning techniques; however, many challenges remain. Key among them are authentication techniques robust to a wide variety of channel variations over time and supporting a vast population of devices.
In this work, we advance the state of the art by presenting (i) the first massive experimental evaluation of DL RFFP and (ii) new data augmentation techniques for LoRa designed to counter the degradation introduced by the wireless channel. Specifically, we collected and publicly shared more than 1TB of waveform data from 100 bit-similar devices (with identical manufacturing processes) over different deployment scenarios (outdoor vs. indoor) and spanning several days. We train and test diverse DL models (convolutional and recurrent neural networks) using either preamble or payload data slices. We compare three different representations of the received signal: (i) IQ, (ii) amplitude-phase, and (iii) spectrogram. Finally, we propose a novel data augmentation technique called DeepLoRa to enhance the LoRa RFFP performance. Results show that (i) training the CNN models with IQ representation is not always the best combo in fingerprinting LoRa radios; training CNNs and RNN-LSTMs with amplitude-phase and spectrogram representations may increase the fingerprinting performance in small and medium-scale testbeds; (ii) using only payload data in the fingerprinting process outperforms preamble only data, and (iii) DeepLoRa data augmentation technique improves the classification accuracy from 19% to 36% in the RFFP challenging case of training on data collected on a different day than the testing data. Moreover, DeepLoRa raises the accuracy from 82% to 91% when training and testing 100 devices with data collected on the same day.
|confname=MobiHoc2021
|link=https://dl.acm.org/doi/pdf/10.1145/3466772.3467054
|title=DeepLoRa: Fingerprinting LoRa Devices at Scale Through Deep Learning and Data Augmentation
|speaker=Wenliang Mao
}}
 
=== 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

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