Bibliographic record
Abstract
We propose and present a framework for enabling policy-based routing in mobile ad hoc networks (MANETs) by applying policy rules associated with the security and reliability (of connection) to peer-to-peer security associations (SA) that are established on (multi-link) routes. In this proposal, we leverage and integrate the concept of dispersity routing with the management and maintenance of an existing modular security architecture. We adopt the Ad hoc On-demand Multipath Distance Vector (AOMDV) routing protocol to achieve dispersity routing. We further expand the modular security architecture, containing the Trust-enhanced Routing Table (TRT) module to include a reliability metric so that a route, among multiple available routes to a destination, may be selected and tracked with policy-set parameters. Under our proposal, a secure route is one that would be mapped through authenticated (trusted) nodes with established SAs, whereas a reliable route is one that would have a high Mean Time Between Failures (MTBF). The combination of trust and reliability as parameters used with multiple routes renders a graded routing service - the capability of providing several potential routes to a destination in a MANET, each of which may be selected because its security and reliability metrics match those of the policy. We support this proposal with a proof of concept simulation and we discuss that secure and reliable policy-based routing in MANETs is a worthwhile area for further research and investment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".