Mitigating Malicious and Unintentional Packet Drops in Mobile Ad Hoc Networks
Bibliographic record
Abstract
Mobile Ad Hoc Networks' adaptability, flexibility, and autonomous characteristics make them suitable for critical applications like healthcare, military, and disaster recovery with cost-effective and time-effective deployment.However, these characteristics make them vulnerable to packet operation at the network layer, as packets get dropped due to malicious activities and resource constraints, i.e., unintentional packet drops.Packet drops directly negatively impact network performance regarding throughput, delay, and wastage of resources.Existing solutions either focus on mitigating malicious or unintentional packet drops, but fail to address both simultaneously.The work mitigates unintentional packet drops by dynamic multi-metric routing that adapts to the dynamic conditions of the network by computing the route by Current Residual Energy (CR) and Residual Buffer Space Metric (RBM).Moreover, work mitigates malicious packet drops by authenticated key agreements and cryptographic decisions.Performance results indicate that the proposed work significantly improves packet delivery and energy efficiency and reduces overhead compared to existing mechanisms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".