Efficient Routing in Mobile Ad-Hoc Social Networks
Bibliographic record
Abstract
Efficient routing in large-scale P2P applications over mobile networks is a challenge. Such networks are formed by clusters of nodes, representing a community of interest, joining with other clusters, establishing a two-tiered hierarchical network. An example application in such a network would be P2PSIP to enable P2P Voice-over-Internet Protocol (VoIP) or other media services. Mobile Ad-hoc Networks (MANETs) are a logical choice to model such a network as a MANET is also inherently a P2P network where each node moves from one place to another independently, joins and leaves the network as it wishes. In this paper, we present our design to enable efficient routing in such a two-tiered network. Each cluster is represented by a single MANET, connected through a backbone. Nodes can join and leave MANETs, and whole clusters can join and leave the network or split and merge, so our design needs to address both node and network mobility. Unlike previous solutions, our approach is based on the use of a distributed hash table (DHT) to support routing in the network backbone, while individual MANETs may employ any MANET routing protocol of their choice, such as OLSR or ADOV. Efficiently providing the functionality of a DHT in a highly mobile network such as a MANET is challenging. We review some of the issues that arise in this context and identify potential candidate protocols. Through extensive simulations, using OMNeT++, we show that one of the protocols in particular, OneHopOverlay4MANET, is a promising choice to support backbone routing in our architecture. Compared to alternative choices, it maintains good performance at low overhead costs even under highly dynamic network topologies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".