Difference between revisions of "Resource:Previous Seminars"

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=== History ===
=== History ===
{{Hist_seminar
|abstract = Immersive telepresence has the potential to revolutionize remote communication by offering a highly interactive and engaging user experience. However, state-of-the-art exchanges large volumes of 3D content to achieve satisfactory visual quality, resulting in substantial Internet bandwidth consumption. To tackle this challenge, we introduce MagicStream, a first-of-its-kind semantic-driven immersive telepresence system that effectively extracts and delivers compact semantic details of captured 3D representation of users, instead of traditional bit-by-bit communication of raw content. To minimize bandwidth consumption while maintaining low end-to-end latency and high visual quality, MagicStream incorporates the following key innovations: (1) efficient extraction of user's skin/cloth color and motion semantics based on lighting characteristics and body keypoints, respectively; (2) novel, real-time human body reconstruction from motion semantics; and (3) on-the-fly neural rendering of users' immersive representation with color semantics. We implement a prototype of MagicStream and extensively evaluate its performance through both controlled experiments and user trials. Our results show that, compared to existing schemes, MagicStream can drastically reduce Internet bandwidth usage by up to 1195X while maintaining good visual quality.
|confname = Sensys'24
|link = https://dl.acm.org/doi/10.1145/3666025.3699344
|title= MagicStream: Bandwidth-conserving Immersive Telepresence via Semantic Communication
|speaker= Mengfan Wang
|date=2025-10-31
}}{{Hist_seminar
|abstract =To fulfill computing demands of numerous Internet of Things (IoT) devices in infrastructure-free regions, low earth orbit (LEO) satellite edge computing has been proposed in recent years, to circumvent the latency arising from long backhaul and link congestion in traditional cloud computing mode. This article proposes a novel time-varying graph-based collaborative task offloading strategy for LEO satellite IoT to reduce task computing latency. To this end, a computing coordinate graph (CCG) is designed to characterize the time-varying topology and resource distribution of LEO satellite networks. When a task is offloaded to LEO satellite networks because local computing capability is unable to meet latency constraint, the position of the task access satellite in the CCG is determined first. Then, the expanded hop counts from all satellite nodes to the access satellite are calculated, which informs the partitioning of different node sets. Afterwards, considering both link and on-board computing resources, with the access satellite as the reference node, the minimum total task computing latency for each node set is obtained in an ascending order of the expanded hop counts. Finally, the minimum one among obtained latency values is the anticipated total task computing latency. Simulation results demonstrate the effectiveness of the proposed task offloading strategy in reducing task computing latency.
|confname = Systems Joural
|link = https://ieeexplore.ieee.org/document/11024019
|title= Collaborative Task Offloading for LEO Satellite Internet of Things: A Novel Computing Coordinate Graph-Based Approach
|speaker= Yifei Zhou
|date=2025-10-31
}}
{{Hist_seminar
{{Hist_seminar
|abstract = Unlike traditional data collection applications (e.g., environment monitoring) that are dominated by uplink transmissions, the newly emerging applications (e.g., device actuation, firmware update, packet reception acknowledgement) also pose ever-increasing demands on downlink transmission capabilities. However, current LoRaWAN falls short in supporting such applications primarily due to downlink-uplink asymmetry. While the uplink can concurrently receive multiple packets, downlink transmission is limited to a single logical channel at a time, which fundamentally hinders the deployment of downlink-hungry applications. To tackle this practical challenge, FDLoRa develops the first-of-its-kind in-band full-duplex LoRa gateway design with novel solutions to mitigate the impact of self-interference (i.e., strong downlink interference to ultra-weak uplink reception), which unleashes the full spectrum for in-band downlink transmissions without compromising the reception of weak uplink packets. Built upon the full-duplex gateways, FDLoRa introduces a new downlink framework to support concurrent downlink transmissions over multiple logical channels of available gateways. Evaluation results demonstrate that FDLoRa boosts downlink capacity by 5.7x compared to LoRaWAN on a three-gateway testbed and achieves 2.58x higher downlink concurrency per gateway than the state-of-the-art.
|abstract = Unlike traditional data collection applications (e.g., environment monitoring) that are dominated by uplink transmissions, the newly emerging applications (e.g., device actuation, firmware update, packet reception acknowledgement) also pose ever-increasing demands on downlink transmission capabilities. However, current LoRaWAN falls short in supporting such applications primarily due to downlink-uplink asymmetry. While the uplink can concurrently receive multiple packets, downlink transmission is limited to a single logical channel at a time, which fundamentally hinders the deployment of downlink-hungry applications. To tackle this practical challenge, FDLoRa develops the first-of-its-kind in-band full-duplex LoRa gateway design with novel solutions to mitigate the impact of self-interference (i.e., strong downlink interference to ultra-weak uplink reception), which unleashes the full spectrum for in-band downlink transmissions without compromising the reception of weak uplink packets. Built upon the full-duplex gateways, FDLoRa introduces a new downlink framework to support concurrent downlink transmissions over multiple logical channels of available gateways. Evaluation results demonstrate that FDLoRa boosts downlink capacity by 5.7x compared to LoRaWAN on a three-gateway testbed and achieves 2.58x higher downlink concurrency per gateway than the state-of-the-art.

Revision as of 01:48, 7 November 2025

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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