PASOR: A Packet Salvaging Model for Opportunistic Routing Protocols
Bibliographic record
Abstract
Opportunistic Routing (OR) protocols are known as a promising research area with the aim of delivering data packets to their destination more reliably. Security of such protocols, however, is still an open, and challenging research problem. In this paper, we propose an enhancement on OR protocols which benefits from an appropriate candidate coordination mechanism, and assists in salvaging data packets that are dropped by malicious nodes. More precisely, an analytical approach is proposed using Discrete-Time Markov Chain (DTMC) to model a packet salvaging mechanism in an OR-based wireless network, while malicious nodes attempt in degrading the network performance. The proposed model demonstrates how back-up candidates in the candidate set can help in salvaging dropped packets by supervising the behavior of other peers in the candidate set. Different network parameters including packet delivery, drop, salvage, and direct-delivery ratio are then extracted from the introduced model. Furthermore, the model is applied on a well-known OR protocol and is evaluated using both analytical approach, and network simulations. Evaluation results represent that the introduced model can dramatically boost the performance of a wireless network by delivering greater number of packets to their destination compared to a baseline protocol.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".