Bibliographic record
Abstract
Energy consumption is an important metric to consider in designing routing protocols for mobile ad hoc networks (MANET). In this article, we present some results from our work on integrating energy-efficiency aspects into a standard MANET routing protocol, optimised link state routing. We explore a range of protocol modifications and show that such changes can indeed increase the protocol performance in ideal scenarios (i.e. nodes having instantaneous and accurate knowledge of other nodes' residual energy levels) by as much as 30%. We then investigate the impact of nodes having only inaccurate/imprecise knowledge of the residual energy levels of other nodes, learning this information through protocol messages. The article shows that the achievable protocol performance is negatively affected by the imprecise information available. The loss in performance can be as high as 10%, emphasising the need to collect more precise routing-related information.
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.
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.026 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".