Proximity Aware Routing in Ad Hoc Networks
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it