Diagnosing mobile ad-hoc networks
Bibliographic record
Abstract
In emergency/rescue applications mobile ad-hoc networks (MANETs) play an important role as a self-organizable and rapidly deployable infrastructure. Consequently, reliable break MANETs are necessary for this type of applications. One of the key problem we are considering in this paper is the identification of faulty mobile hosts in MANETs. Current distributed diagnosis protocols assume either that the network topology is fixed or impose some restrictions on the mobility of the hosts. In this paper, we first develop an adaptive distributed self-diagnosis protocol, called Adaptive-DSDP, that identifies all faulty mobiles in a diagnosable fixed-topology MANET. Then, we introduce a second self-diagnosis protocol, called Mobile-DSDP, using a comparison-based diagnostic model devised especially for mobile environments. In the comparison approach, each mobile host transmits a test task to its neighbors and the outcomes are compared. The identification of faulty mobiles is based on the matching and mismatching results among the mobiles. The evaluation of the communication and time complexities of Mobile-DSDP shows that efficient self-diagnosis protocols based on the comparison diagnosis model can be designed.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".