Load-aware on-demand routing (LAOR) protocol for mobile ad hoc networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most current routing protocols for mobile ad hoc networks consider the shortest path with minimum hop counts at the optimal route. However, the minimum end-to-end delay from source to destination may not always be achieved through this shortest path. In this paper, we propose an efficient delay-based load-aware on-demand routing (D-LAOR) protocol, which determines the optimal path based on the estimated total path delay and the hop count. We demonstrate the effectiveness of D-LAOR by integrating it with the ad hoc on-demand distance vector (AODV) routing protocol. Simulation results obtained suing the ns-2 network simulation platform, show that D-LAOR scheme increases packet delivery fraction and decreases end-to-end delay by more than 10% in a moderate network scenario when compared with the original AODV ad the other LAOR protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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