Congestion Avoidance Routing Protocol for Ad-Hoc Networks
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Abstract
The objective of this research is to bring the consideration of congestion into the design of ad hoc routing protocols. The main thrust is to avoid congestion by minimizing contentions for channel access. The intermediate delay (IMD) is proposed to replace the hop count as a routing metric. IMD estimates the delay introduced by the intermediate nodes along the route using the sum of delays from each node. It characterizes the impacts of channel contention, traffic load, and the length of a route. The self-adjusting congestion avoidance (SAGA) routing protocol is designed to use IMD as the routing metric. SAGA reduces congestion at intermediate nodes. It selects routes that bypass the hot spot where contention is intense. The packet transmission procedure of the distributed coordination function (DCF) in the IEEE 802.11 standard is analyzed and used as a study case for evaluation and experimentation. An estimate of the transmission delay is derived based on local information available at a node. The estimation takes the impact of active traffic in the neighborhood into account without exchanging messages with neighbors.The performance of SAGA is evaluated and compared with that of ad hoc on-demand distance vector (AODV), dynamic source routing (DSR), and destination-sequenced distance-vector (DSDV) protocols using simulation. TCP and two types of UDP traffic are considered: constant bit rate traffic and traffic exhibiting long range dependency. SAGA can sustain heavier traffic load and offers higher peak throughput than AODV and DSR. The overhead of SAGA can be as low as 10% as that of AODV and 12% as that of DSR. The average end-to-end delay of SAGA is the lowest among the protocols. It is shown that considerations of congestion and intermediate delay instead of the hop count can enhance routing performance significantly.
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How to Cite
Yi Lu, & Bharat Bhargava. (2010). Congestion Avoidance Routing Protocol for Ad-Hoc Networks. International Journal of Next-Generation Computing, 1(1), 124–145. https://doi.org/10.47164/ijngc.v1i1.10
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