Efficient On-Demand Routing for Mobile Ad Hoc Wireless Access Networks
Bibliographic record
Abstract
In this paper, we consider a mobile ad hoc wireless access network in which mobile nodes can access the Internet via one or more stationary gateway nodes. Mobile nodes outside the transmission range of the gateway can continue to communicate with the gateway via their neighboring nodes over multihop paths. On-demand routing schemes are appealing because of their low routing overhead in bandwidth restricted mobile ad hoc networks, however, their routing control overhead increases exponentially with node density in a given geographic area. To control the overhead of on-demand routing without sacrificing performance, we present a novel extension of the ad hoc on-demand distance vector (AODV) routing protocol, called LB-AODV, which incorporates the concept of load-balancing (LB). Simulation results show that as traffic increases, our proposed LB-AODV routing protocol has a significantly higher packet delivery fraction, a lower end-to-end delay and a reduced routing overhead when compared with both AODV and gossip-based routing protocols.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".