A P2P Approach to Routing in Hierarchical MANETs
Bibliographic record
Abstract
We present an effective routing solution for the backbone of hierarchical MANETs. Our solution leverages the storage and retrieval mechanisms of a Distributed Hash Table (DHT) common to many (structured) P2P overlays. The DHT provides routing information in a decentralized fashion, while supporting different forms of node and network mobility. We split a flat network into clusters, each having a gateway who participates in a DHT overlay. These gateways interconnect the clusters in a backbone network. Two routing approaches for the backbone are explored: flooding and a new solution exploiting the storage and retrieval capabilities of a P2P overlay based on a DHT. We implement both approaches in a network simulator and thoroughly evaluate the performance of the proposed scheme using a range of static and mobile scenarios. We also compare our solution against flooding. The simulation results show that our solution, even in the presence of mobility, achieved well above 90% success rates and maintained very low and constant round trip times, unlike the flooding approach. In fact, the performance of the proposed inter-cluster routing solution, in many cases, is comparable to the performance of the intra-cluster routing case. The advantage of our proposed approach compared to flooding increases as the number of clusters increases, demonstrating the superior scalability of our proposed approach.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".