Bibliographic record
Abstract
Most of the existing routing protocols for ad hoc networks are designed to scale in networks of a few hundred nodes. They rely on state concerning all links of the network or links on the route between a source and a destination. This may result in poor scaling properties in larger mobile networks or when node mobility is high. Using location information to guide the routing process is one of the most often proposed means to achieve scalability in large mobile networks. However, location-based routing is difficult when there are holes in the network topology. We propose a novel position-based routing protocol called Proximity Aware Routing for Ad-hoc networks (PARA) to address these issues. PARA selects the next hop of a packet based on 2-hops neighborhood information. We introduce the concept of "proximity discovery". The knowledge of a node’s 2-hops neighborhood enables the protocol to anticipate concave nodes and helps reduce the risks that the routing protocol will reach a concave node in the network. Our simulation results show that PARA’s performance is better in sparse networks with little congestion. Moreover, PARA significantly outperforms GPSR for delivery ratio, transmission delay and path length. Our results also indicate that PARA delivers more packets than AODV under the same conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".