A fuzzy-based check-and-spray geocast routing protocol for opportunistic networks
Bibliographic record
Abstract
Unlike communication networks which are traditionally assumed to be connected, Opportunistic networks (OppNets) are a type of wireless ad hoc networks with no guarantee of end-to-end path for data routing, which is due to node mobility, volatile links, and frequent disconnections. As such, data transmission among the nodes relies on their cooperation and this is realized in a store-and-carry fashion. To this end, several opportunistic routing techniques have been proposed in the literature, some of which using geocasting, a technique that consists of scheduling the message to a specific region toward its destination. This paper proposes a Fuzzy-based Check-and-Spray Geocast (FCSG) routing protocol for OppNets, in which a Check-and-Spray mechanism is used to control the message flooding within the destination cast and a fuzzy controller is used for selecting the suitable relay nodes to carry the message toward the destination, with the aim to improve the delivery ratio. Using simulations, the proposed FCSG protocol is shown to outperform the F-GSAF, GeoEpidemic and EECSG routing protocols in terms of overhead ratio, average latency, and delivery ratio, under varying number of nodes, buffer size, and Time-to-Live.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".