Difference between revisions of "Resource:Seminar"

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===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=In the last decade, LoRa has emerged and prevailed as a promising technology to offer the long range and low power communication service. The packet collisions caused by concurrent transmissions(CTs) severely limit the LoRa network capacity, which becomes the key obstacle to releasing the potential of LoRa. The existing collision-resolution researches need frequency domain features to separate different packets in the collision. When there exists multiple packets in the collision, these features are more likely to overlap with each other and cannot be distinguished, which leads to performance degradation of these studies. To address this issue, in this paper, we propose channel hopping LoRa (CHLoRa) as a physical approach that utilize the multi-channel diversity to against multi-packet collisions. In CHLoRa, the LoRa chirp is divided into several subchirps and spread into different channels. As all the subchirp-pieces of the original chirp are likely to be collided with the subchirps with different bins, CHLoRa can recover the original chirp’s bin through merging the same bins of its subchirps. However, it is hard to obtain precise demodulation results of subchirps especially in collision, as using shorter time-span subchirps decreases the frequency resolution. We propose a subchirp merging scheme to group and merge subchirps’ bins according to their collision-free confidence. We conduct simulation experiments to
|abstract=In the last decade, LoRa has emerged and prevailed as a promising technology to offer the long range and low power communication service. The packet collisions caused by concurrent transmissions(CTs) severely limit the LoRa network capacity, which becomes the key obstacle to releasing the potential of LoRa. The existing collision-resolution researches need frequency domain features to separate different packets in the collision. When there exists multiple packets in the collision, these features are more likely to overlap with each other and cannot be distinguished, which leads to performance degradation of these studies. To address this issue, in this paper, we propose channel hopping LoRa (CHLoRa) as a physical approach that utilize the multi-channel diversity to against multi-packet collisions. In CHLoRa, the LoRa chirp is divided into several subchirps and spread into different channels. As all the subchirp-pieces of the original chirp are likely to be collided with the subchirps with different bins, CHLoRa can recover the original chirp’s bin through merging the same bins of its subchirps. However, it is hard to obtain precise demodulation results of subchirps especially in collision, as using shorter time-span subchirps decreases the frequency resolution. We propose a subchirp merging scheme to group and merge subchirps’ bins according to their collision-free confidence. We conduct simulation experiments to evaluate the performance of CHLoRa. The results show that ...
evaluate the performance of CHLoRa. The results show that ...
|confname=INFOCOM 2024
|confname=INFOCOM 2024
|link=https://mobinets.org/index.php?title=Resource:Seminar
|link=https://mobinets.org/index.php?title=Resource:Seminar

Revision as of 17:27, 31 May 2023

Time: 2023-06-01 9:30
Address: 4th Research Building A518
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [INFOCOM 2024] CHLoRa: Pushing the Limits of LoRa Concurrent Transmissions with Channel Hopping Subchirps, Wenliang
    Abstract: In the last decade, LoRa has emerged and prevailed as a promising technology to offer the long range and low power communication service. The packet collisions caused by concurrent transmissions(CTs) severely limit the LoRa network capacity, which becomes the key obstacle to releasing the potential of LoRa. The existing collision-resolution researches need frequency domain features to separate different packets in the collision. When there exists multiple packets in the collision, these features are more likely to overlap with each other and cannot be distinguished, which leads to performance degradation of these studies. To address this issue, in this paper, we propose channel hopping LoRa (CHLoRa) as a physical approach that utilize the multi-channel diversity to against multi-packet collisions. In CHLoRa, the LoRa chirp is divided into several subchirps and spread into different channels. As all the subchirp-pieces of the original chirp are likely to be collided with the subchirps with different bins, CHLoRa can recover the original chirp’s bin through merging the same bins of its subchirps. However, it is hard to obtain precise demodulation results of subchirps especially in collision, as using shorter time-span subchirps decreases the frequency resolution. We propose a subchirp merging scheme to group and merge subchirps’ bins according to their collision-free confidence. We conduct simulation experiments to evaluate the performance of CHLoRa. The results show that ...
  2. [SEC 2023] EdgeLight: Smart Traffic Lights with Ambient Edge Intelligence, Xianyang
    Abstract: Accurate, real-time object detection on resource-constrained devices enables autonomous mobile vision applications such as traffic surveillance. However, analyzing real-time video poses severe challenges to today’s network and computation systems. Rather than either pure local processing or offloading, we merge large objects across the boundary locally and objects from the edge. To balance accuracy, latency, payment and reliability, we present EdgeLight, a crowd-assisted real-time video analytics framework, which coordinates computationally weak cameras with more powerful edge servers to enable video analytics under the accuracy, latency and payment requirements of applications. Furthermore, we design a connectionless service discovery protocol to reduce invalid wifi connections.


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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