A performance evaluation of a pre-emptive on-demand distance vector routing protocol for mobile ad hoc networks: Research Articles
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Bibliographic record
Abstract
Mobile ad hoc networks are useful for providing communication support where no fixed infrastructure exists or the deployment of a fixed infrastructure is not economically profitable and movement of communicating parties is allowed. Therefore, it is not possible to establish a priori and fixed paths for message delivery through the network. Because of their importance, routing and packets dropped problems, mainly due to the path breaking, are among the most studied problem in mobile and wireles ad hoc networks. Multi-path protocols can be useful for the purpose of balancing congestion and decreasing the delay, by routing packets along different paths. However, they may allow only source-based load balancing decisions. In this paper, we present a pre-emptive ad hoc on-demand distance vector routing protocol for mobile and wireless ad hoc networks. We present the algorithm, discuss its implementation and report on the performance results of simulation of several workload models on ns-2. Our results indicate that a scheme based on scheduling a path-discovery routine before the current in-use link breaks is feasible and that such a mechanism can increase the number of packets delivered and decrease the average delay per packet. It also improves the throughput (packet delivered ratio) and balances the traffic between different source–destination pairs. Copyright © 2004 John Wiley & Sons, Ltd.
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Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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