Sensor network congestion avoidance and control primarily aims to reduce packet drops while maintaining fair bandwidth allocation to existing network flows. The design of a congestion control algorithm suited for all types of applications in sensor networks is a challenging task due to the application-specific nature of these networks. With numerous sensors transmitting data simultaneously to one or more base stations (also called sinks), sensor nodes located near the base station will most likely experience congestion and packet loss. In this thesis, we propose a novel distributed congestion avoidance algorithm which calculates the ratio of the number of downstream and upstream nodes. This ratio value (named Characteristic ratio) is used to take a routing decision and incorporate load balancing while also serving as a pointer to the congestion state of the network. Available queue sizes of the downstream nodes are used to detect incipient congestion. Queue characteristics of candidate downstream nodes are used collectively to implement both congestion avoidance and fairness by adjusting the node's forwarding rate and next hop destination. Such an approach helps to minimize packet drops, improve energy efficiency and load balancing. In cases of severe congestion, the source is signaled to reduce its sending rate and enable the network recovery process. This is essentially a transport layer algorithm and would work best with a multi-path routing protocol and almost any MAC layer standard. We present the design and implementation of the proposed protocol and compare it with the existing avoidance protocols like Global rate control and Lightweight buffering. Our simulation results show a higher packet delivery ratio with greater node buffer utilization for our protocol in comparison with the conventional mechanisms.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-4052 |
| Date | 01 January 2007 |
| Creators | Ahmad, Mohammad |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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