Resource: Seminar

From MobiNetS
Revision as of 21:14, 12 February 2023 by Wenliang (talk | contribs) (wenliang updates seminars)
Jump to: navigation, search

Time: 2023-02-13 9:30
Address: 4th Research Building A527-B
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [Sensys2022] HyLink: Towards High Throughput LPWANs with LoRa Compatible Communication, Mengyu
    Abstract: This paper presents the design and implementation of HyLink which aims to fill the gap between limited link capacity of LoRa and the diverse bandwidth requirements of IoT systems. At the heart of HyLink is a novel technique named parallel Chirp Spread Spectrum modulation, which tunes the number of modulated symbols to adapt bitrates according to channel conditions. Over strong link connections, HyLink fully exploits the link capability to transmit more symbols and thus transforms good channel SNRs to high link throughput. While for weak links, it conservatively modulates one symbol and concentrates all transmit power onto the symbol to combat poor channels, which can achieve the same performance as legacy LoRa. HyLink addresses a series of technical challenges on encoding and decoding of multiple payloads in a single packet, aiming at amortizing communication overheads in terms of channel access, radio-on power, transmission air-time, etc. We perform extensive experiments to evaluate the effectiveness of HyLink. Evaluations show that HyLink produces up to 10× higher bit rates than LoRa when channel SNRs are higher than 5 dB. HyLink inter-operates with legacy LoRa devices and can support new emerging traffic-intensive IoT applications.
  2. [TMC 2023] Multi-Task Allocation in Mobile Crowd SensingWith Mobility Prediction, Zhenguo
    Abstract: Mobile crowd sensing (MCS) is a popular sensing paradigm that leverages the power of massive mobile workers to perform various location-based sensing tasks. To assign workers with suitable tasks, recent research works investigated mobility prediction methods based on probabilistic and statistical models to estimate the worker’s moving behavior, based on which the allocation algorithm is designed to match workers with tasks such that workers do not need to deviate from their daily routes and tasks can be completed as many as possible. In this paper, we propose a new multi-task allocation method based on mobility prediction, which differs from the existing works by (1) making use of workers’ historical trajectories more comprehensively by using the fuzzy logic system to obtain more accurate mobility prediction and (2) designing a global heuristic searching algorithm to optimize the overall task completion rate based on the mobility prediction result, which jointly considers workers’ and tasks’ spatiotemporal features. We evaluate the proposed prediction method and task allocation algorithm using two real-world datasets. The experimental results validate the effectiveness of the proposed methods compared against baselines.


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

Template loop detected: Resource:Previous Seminars

Instructions

请使用Latest_seminar和Hist_seminar模板更新本页信息.

    • 修改时间和地点信息
    • 将当前latest seminar部分的code复制到这个页面中
    • 将{{Latest_seminar... 修改为 {{Hist_seminar...，并增加对应的日期信息|date=
    • 填入latest seminar各字段信息
    • link请务必不要留空，如果没有link则填本页地址 https://mobinets.org/index.php?title=Resource:Seminar
  • 格式说明
    • Latest_seminar:

{{Latest_seminar
|confname=
|link=
|title=
|speaker=
}}

    • Hist_seminar

{{Hist_seminar
|confname=
|link=
|title=
|speaker=
|date=
}}

Retrieved from "http://mobinets.cn/site/index.php?title=Resource:Seminar&oldid=2594"