distributed hop by hop rate adjustment for congestion control in sensor networks
Download
Skip this Video
Download Presentation
Distributed Hop-by-Hop Rate Adjustment for Congestion Control in Sensor Networks

Loading in 2 Seconds...

play fullscreen
1 / 7

Distributed Hop-by-Hop Rate Adjustment for Congestion Control in Sensor Networks - PowerPoint PPT Presentation


  • 107 Views
  • Uploaded on

Distributed Hop-by-Hop Rate Adjustment for Congestion Control in Sensor Networks. Presented by: Vipul Bhasin Sapna Dixit Vishal Kumar Singh Manmohan Voniyadka. Goal. Implement a distributed rate adjustment strategy to minimize congestion

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Distributed Hop-by-Hop Rate Adjustment for Congestion Control in Sensor Networks' - venus


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
distributed hop by hop rate adjustment for congestion control in sensor networks

Distributed Hop-by-Hop Rate Adjustment for Congestion Control in Sensor Networks

Presented by:

Vipul Bhasin

Sapna Dixit

Vishal Kumar Singh

Manmohan Voniyadka

slide2
Goal
  • Implement a distributed rate adjustment strategy to minimize congestion
  • Adjust the source rate using congestion factor for better response
design
Design
  • Calculate congestion factor
    • Used queue length, channel load, and retransmission counter
  • Adjust source rate when consecutive congestion factor found to increase
  • Propagate cumulative congestion factor upstream
    • Propagated only when threshold exceeded
code changes
Code Changes
  • Suppress Message Behavior
    • sendSupressMsg
    • suppressMsgRcvd
    • calcCongestionFactor
  • Source Rate Adjustment (via AIMD)
    • suppressMsgRcvd
  • NAK Implementation (packet dropped from queue as criteria to post NAK)
    • sendNakMsg
    • NakMsgRcvd
experiments
Experiments
  • Simple Model of VC vs. EC
    • Varied source rates from 5 to 20
    • EC outperformed VC
  • Lossy Model of VC vs. EC
    • Two models – one-hop and generated (from lossy builder)
    • Comparable results in generated and EC outperformed VC in one-hop
  • Lossy Model Topology of VC vs. EC
    • EC outperformed VC in new lossy topology
pros cons
Pros/Cons
  • Pros
    • Higher Responsiveness
    • Local decisions can be made based on global congestion scenario
    • NAK counter better estimates congestion
  • Cons
    • More suppress messages due to greater propagation of congestion factor
ad