A performance evaluation of a pre‐emptive on‐demand distance vector routing protocol for mobile ad hoc networks
Bibliographic record
Abstract
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 establisha prioriand 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".