PPBR: Privacy-Aware Position-Based Routing in Mobile Ad Hoc Networks
Bibliographic record
Abstract
Position-based routing for Mobile Ad-Hoc Networks is a promising approach to reduce route overhead by using the location information of each node. However, in most of the previously reported position-based routing protocols, a node has to periodically broadcast its current position coordinates and identifiers to its one-hop neighbors. Such information could be easily eavesdropped on by an adversary if it is not protected, and consequently, location privacy would be violated. In this paper, we propose a novel privacy-aware position-based routing protocol (PPBR), in which a node takes dynamic pseudo identifiers instead of its real identity in advertising its position. Furthermore, PPBR provides end-to-end anonymity to any intermediate nodes along the route. The theoretical analysis shows that the probability of tracking a node under PPBR through traffic analysis would be very small. We compare the performance of PPBR with that by GPSR and AODV through extensive simulation, which demonstrates the effectiveness and efficiency of the proposed scheme. We also show that frequent update of the pseudo identifier of each node yields an insignificant impact on routing performance and overhead.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".