Resource: Seminar

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Time: 2022-11-08 16:30
Address: 4th Research Building A527-B
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [ICNP2022] Ostinato: Combating LoRa Weak Links in Real Deployments, Wenliang
    Abstract: Low Power Wide Area Networks (LPWAN) have become one of the key techniques to provide long-range, low-power communication for large-scale devices in the Internet of Things. However, LPWAN devices in real deployments (e.g.,in buildings and basements) suffer from low-quality links due to signal attenuation, leading to coverage holes and significant deployment overhead. In this work, we propose Ostinato to enable communication for weak links and to enhance the coverage for real deployments of COTS LoRa. The key idea of Ostinato is to transform the original packet to a pseudo packet with repeated symbols and to concentrate the energy of multiple symbols to enhance the signal SNR. To address practical challenges, we reverse engineer the entire coding and modulation process of LoRa and propose a method to generate repeated symbols on COTS LoRa by manipulating input data bits. Thus, Ostinato can be directly used for widely deployed LoRa nodes without hardware modification. We achieve weak packet detection, synchronization, and effective decoding on the receiver side by concentrating energy from multiple symbols with phase offsets. We implement Ostinato on Software Defined Radio (SDR) platform and extensively evaluate its performance. The evaluation results show that Ostinato achieves an 8.5 dB gain on receiving sensitivity and 2.88× gain on the coverage compared with COTS LoRa.
  2. [TMC2022] A Unified Framework for Joint Sensing and Communication in Resource Constrained Mobile Edge Networks, Xianyang
    Abstract: Mobile crowd sensing (MCS) is a promising paradigm which leverages sensor-embedded mobile devices to collect and share data. The key challenging issues in designing an MCS system include selecting appropriate users to participate in a specific sensing task and designing efficient data sensing and transmission policies for data aggregation. In mobile edge networks, the limitation on network resources including bandwidth and energy affects the design of MCS significantly. Specifically, the limited resources affect whether and how to select users for a sensing task, and the bandwidth allocated to a user affects its data sensing and transmission policies. Since user selection, bandwidth allocation, data sensing and transmission are closely coupled issues in MCS, we focus on designing a unified framework for joint sensing and communication in this paper, by jointly optimizing the aforementioned four policies under resource constraints. Simulation results show that the proposed unified framework significantly outperforms several baseline solutions without considering wireless link vulnerability and/or resource limitations.
  3. [CVPR 2022] Federated Class-Incremental Learning, Jianqi
    Abstract: Federated learning (FL) has attracted growing attentions via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume object classes of the overall framework are fixed over time. It makes the global model suffer from significant catastrophic forgetting on old classes in real-world scenarios, where local clients often collect new classes continuously and have very limited storage memory to store old classes. Moreover, new clients with unseen new classes may participate in the FL training, further aggravating the catastrophic forgetting of global model. To address these challenges, we develop a novel Global-Local Forgetting Compensation (GLFC) model, to learn a global class-incremental model for alleviating the catastrophic forgetting from both local and global perspectives. Specifically, to address local forgetting caused by class imbalance at the local clients, we design a class-aware gradient compensation loss and a class-semantic relation distillation loss to balance the forgetting of old classes and distill consistent inter-class relations across tasks. To tackle the global forgetting brought by the non-i.i.d class imbalance across clients, we propose a proxy server that selects the best old global model to assist the local relation distillation. Moreover, a prototype gradient-based communication mechanism is developed to protect the privacy. Our model outperforms state-of-the-art methods by 4.4% 15.1% in terms of average accuracy on representative benchmark datasets. The code is available at https://github.com/conditionWang/FCIL.


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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