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@squ.edu.om, hadj@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

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 network’s 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. 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. Conclusions

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.