Leveraging Service Discovery in MANETs with Mobile Directories
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
This paper advances a novel approach that facilitates the location of services and/or digital assets advertised by directories in a Mobile Ad hoc Network. The proposed Service Directory Placement Protocol (SDPP) improves scalability and reduces packet traffic overhead by advancing a multi-directory extension of an earlier approach that relied on the migration of a single directory through the network. This investigation demonstrates that modelling the directory replication problem as a Semi-Markov Decision Problem solved by means of a Reinforcement Learning technique known as Q-learning improves the performance of SDPP. Computer simulations validate the feasibility of the proposed scheme that enables packet overhead reductions between 15 and 75 %, whereas the directory location success rate improves by up to 11% when compared with pure broadcast flooding and other existing approaches in wireless networks where hosts move at walking speeds.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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