On Assessing the Impact of Jamming Strategies on the Behavior of DYMO-Based MANETs
Bibliographic record
Abstract
The wide-scale deployment of smart phones has begun to provide a pragmatic deployment environment for at-scale MANETs, (i.e., for providing non-cellular based mobile device services). The services enabled via these MANETs will, of course, be subject to cyber-attacks, some of the simplest of which are wireless jamming attacks. Through simulation studies, this work assesses the impact that such jamming attacks have on standard network-level MANET features, (i.e., packet delivery ratio (PDR), hops traveled, delay, etc.), using DYMO as the exemplar MANET routing protocol. More particularly, it is shown that jamming causes more complex effects to the MANET's behavior than generally has been reported. For example jamming can cause: a) it to take considerably longer for start-up transients to decay, and b) a larger percentage of the experiments in which statistical steady-states are never reached, (i.e., the start-up transients are never observed to decay). These results are important as they highlight that jamming can have significant impact's on MANET operations past just causing network disconnections. These issues, in turn, imply that designing jamming resistant or resilient MANETs may be significantly more challenging than prior work would tend to suggest.
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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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 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".