A reliable quality of service aware fault tolerant gateway discovery protocol for vehicular networks
Bibliographic record
Abstract
Abstract A great interest in vehicular ad‐hoc networks has been noticed by the research community. General goals of vehicular networks are to enhance safety on the road and to ensure the convenience of passengers by continuously providing them, in real time, with information and entertainment options such as routes to destinations, traffic conditions, facilities' information, and multimedia/Internet access. Indeed, time efficient systems that have high connectivity and low bandwidth usage are most needed to cope with realistic traffic mobility conditions. One foundation of such a system is the design of an efficient gateway discovery protocol that guarantees robust connectivity between vehicles, while assuring Internet access. Little work has been performed on how to concurrently integrate load balancing, quality of service (QoS), and fault tolerant mechanisms into these protocols. In this paper, we propose a reliable QoS‐aware and location aided gateway discovery protocol for vehicular networks by the name of fault tolerant location‐based gateway advertisement and discovery. One of the features of this protocol is its ability to tolerate gateway routers and/or road vehicle failure. Moreover, this protocol takes into consideration the aspects of the QoS requirements specified by the gateway requesters; furthermore, the protocol insures load balancing on the gateways as well as on the routes between gateways and gateway clients. We discuss its implementation and report on its performance in contrast with similar protocols through extensive simulation experiments using the ns‐2 simulator. Copyright © 2013 John Wiley & Sons, Ltd.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".