Bibliographic record
Abstract
Looking at the progress of mobile-IP in the recent years, there's a sense that IP (for QoS support, IPv6 more specifically) is going to be involved more and more in wireless applications. The current IETF standard for mobility is the mobile IP (RFC 3344 for IPv4 and RFC 3775 for IPv6) both work by changing the IP address when changing the subnet. Both mobile IPv4 and IPv6 suffer from longer handover delays mainly due to AAA (authentication, authorization and accounting) signalling and IP address configuration. There are numerous proposals out there, which try to either optimize mobile IP or use different mechanisms for a certain domain. What proposed here is the deployment of existing technologies binding to a new approach in handling QoS and smooth and technology independent handoffs, thanks to the MPLS mechanism and to the adaptive characteristics of mobile ad-hoc networks (MANETs). This paper discusses a mechanism for a fast handoff in mobile-IPv6 architecture. Fast handoff in mobile-IP is used for facilitating applications such as video-conferencing, Internet telephony, and other applications that require minimal delays and packet drops. In our proposal, multipath routing approach facilitates the communication between M-MANET entities "mobile node (MN), correspondent nodes (CN), and home agent (HA)". These entities are all IPv6-ad-hoc-MPLS-ready elements and this is an MPLS mobile-IPv6 ad-hoc network (M-MANET) topology, therefore QoS will be maintained through the usage of this integration. With simulation results we discuss the overall functionality
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".