Congestion Control in Wireless Network Implementation

1
Congestion Control in Wireless Network
Implementation

• A. Al-Naamany and H. Bourdoucen
• Sultan Qaboos University
• Electrical Computer Engineering Department,
• PO Box 33, Al Khod, 123 Muscat, OMAN Emails
  naamany_at_squ.edu.om, hadj_at_squ.edu.om

2
Problem

• On going academic research being undertaken at
  Sultan Qaboos University
• Inspired by successful research at University of
  California, Berkley
• Initially started as Wired Network Congestion
  Control

3
Problem

• The Internet and most current intranet networks
  are experiencing a huge increase in the volume of
  traffic.
• This affects directly the network congestion by
  saturating the buffers at the routers and
  contributes to generating lots of data losses as
  well as reception and transmission delays.
• The existing TCP end-to-end congestion control
  uses Additive Increase Multiplicative Decrease
  (AIMD) approach, a time out and slow start
  behavior, which lead to data throughput with
  abrupt changes

4
Problem
5
Approach

• Therefore, developing new congestion control
  strategies based on non-analytical approaches
  will certainly help to overcome the current
  difficulties of the internet in particular which
  are due to
• network structural complexity,
• diversity of services supported,
• variety of parameters involved.
• This research presents a fuzzy logic-based
  approach for controlling the network congestion.
  Its main objective
• optimize the available bandwidth
• keep smooth the data throughput transfer profile

6
Transmission Rate
7

• Utilization of Fuzzy Logic

Fuzzy logic Controller
8

• Membership Functions of the Two Inputs

9

• De-Fuzzification

10

Effect of packet loss rate p and dp/dt on fuzzy
logic transmission rate
11

Effect of packet loss rate p and dp/dt on
transmission rate
12

Sending Rate approaching Actual Rate
13
Congestion Control for WLAN

• The AIMD is not entirely suitable for wireless ad
  hoc networks
• This is very evident in cases where networks
  links are broken as a result of node mobility and
  it takes time to perform route reconfiguration in
  which cases data loss is assured
• The data packets could be lost or hindered, where
  , in TCP the sender can mistake this event as
  congestion.

14
Congestion Control for WLAN

• A need for mechanism to distinguish packet drops
  due broken links and route configuration
• Have a quick start and high sending rate once
  the new route is identify instead of additive
  increase
• Smoothly decrease the rate to the
  actual/appropriate sending rate using Fuzzy logic
  or other mechanism.

15
FL Congestion Control for WLAN
16
Conclusions

• TCP congestion controller can be developed to
  offer to smooth the data throughput profile and
  to optimize the network transfer bandwidth
  including WLAN.
• This can be achieved using a fuzzy logic
  compensator, which identifies the transfer rate
  steps depending on the packet drop rate and the
  round trip time of the network at a given point
  of time.
• In addition, due to dynamic network constraints,
  the proposed method allows proximity tracking of
  both the bandwidth and data throughput profile.
  This has been ensured by embedding within the
  controller a damping function to avoid abrupt
  changes of the transmission rate.

17
Conclusions

• This approach allows reducing unnecessary
  over-transmitted packets that will be lost in the
  network and at the same time reduces the abrupt
  Changes.
• Because of its non-analytical nature, the
  proposed approach appears as a good candidate to
  complement the available congestion controllers
  for unicast as well as for multicast systems in
  Wired and WLAN.