Bibliographic record
Abstract
Unlike communication networks which are traditionally assumed to be connected, Opportunistic networks (OppNets) are a type of ad hoc networks where there is no guarantee of end-to-end path for data routing due to node mobility, volatile links, and frequent disconnections. In such networks, data transmission among the nodes relies on nodes' cooperation and is achieved in a store-carry-and-forward fashion. As such, the design of routing protocols for OppNets is challenging since it relies on opportunistic connections that may arise among the nodes. To address this challenge, this thesis proposes three novel geocast routing protocols for OppNets, namely: (1) an Energy-Efficient Check-and-Spray Geocast (EECSG) routing protocol, (2) a Fuzzy-based Check-and-Spray Geocast (FCSG) protocol and an Energy-Efficient version of it (EFCSG), and (3) a Reinforcement Learning-based Fuzzy Geocast Routing Protocol (RLFGRP). Using the Opportunistic Networks (ONE) simulator along with the INFOCOM 2006 real mobility traces and synthetic mobility models, the proposed routing protocols are evaluated and compared against some benchmark schemes, in terms of predefined performance metrics, showing their superiority.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".