A simulation framework for ad-hoc wireless networks
Bibliographic record
Abstract
Protocols, currently used for ad-hoc wireless networks, are designed and tested for networks, which are different in characteristics than ad-hoc networks. Hence, the challenge is to improve current protocols or design new protocols to meet the demands for such new type of networks. Although the research challenge covers all layers of ad-hoc network stack, the current research focus is on network and data link layer mainly to obtain optimum routing algorithm and overcome the problem of applying IEEE802.11 to multihop, ad-hoc, networks. There are two common approaches to analyze the proposed solutions; analytical approach and simulation approach which is most commonly used. In this paper, a simulation framework is developed as a tool for the analysis of new protocols and algorithms. This framework is designed to be, reusable in the sense that it can be integrated with new protocols/current protocols for evaluation purposes. The results from the framework include the network throughput and the packet delay for end-to-end links. The output is given as runtime visual presentation, which may give some hints about the problem type. Moreover, the output presentation may be customized according to user objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".