An Integrated Stimulation and Punishment Mechanism for Thwarting Packet Dropping Attack in Multihop Wireless Networks
Bibliographic record
Abstract
In multihop wireless networks, the rational packet droppers may not relay the others' packets because packet relay consumes their resources without benefits, and the irrational packet droppers intentionally drop packets to disrupt the packet transmission process, which may make multihop communication fail. Cooperation stimulation mechanisms can motivate the rational packet droppers to relay packets, but they cannot identify the irrational packet droppers. In this paper, we develop a novel mechanism that can thwart the rational and irrational packet dropping attacks by adopting stimulation and punishment strategies (TRIPO). TRIPO uses micropayment to stimulate the rational packet droppers to relay the others' packets and enforce fairness and uses reputation system (RS) to identify and evict the irrational packet droppers. We propose a novel monitoring technique to measure the nodes' frequency of dropping packets based on processing the payment receipts instead of using the medium overhearing technique. The receipts can be processed to extract financial information to reward the cooperative nodes that relay packets, as well as contextual information, such as broken links, to build up the RS. Extensive analytical and simulation results demonstrate that TRIPO can secure the payment and precisely identify the irrational packet droppers with almost no false-positive nodes, which can improve the network performance in terms of packet delivery ratio.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".