Mitigating the effect of jamming signals in wireless <i>ad hoc</i> and sensor networks
Bibliographic record
Abstract
In an infrastructure-less wireless ad hoc or sensor network, communication may be achieved between mobile nodes without a central entity (base station) using a half-duplex Slotted ALOHA protocol. The probability of success and the throughput per mobile node can be reduced significantly, if the network is attacked by jamming signals. Mitigating the effect of jamming signals using multi-packet transmission (MPT) and/or multi-packet reception (MPR) capabilities of each mobile node is studied. The effect of the probability of success reduction due to jamming signals can be mitigated by using the MPT and the MPR capabilities of each mobile node. Similarly, reduced throughput can be increased using the same techniques. The maximum throughput per mobile node can be obtained by the proper adjustment of the transmitting probability of each mobile node and the receiving probability of each mobile node. A lower mitigation of the maximum throughput reduction can be obtained by using only the MPT, if the jamming signal rate is very low. On the other hand, only the MPR capability can provide a lower mitigation of the maximum throughput reduction. The effect of jamming signals on the maximum throughput can be mitigated successfully at all traffic load conditions, if the MPT and the MPR capabilities work together.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".