Extended Mobile IPv6 Route Optimization for Mobile Networks in Local and Global Mobility Domain
Bibliographic record
Abstract
Network mobility (NEMO) route optimization support is strongly demanded in next generation networks; without route optimization the mobile network (e.g., a vehicle) tunnels all traffic to its Home Agent (HA). The mobility may cause the HA to be geographically distant from the mobile network, and the tunneling causes increased delay and overhead in the network. It becomes peculiar in the event of nesting of mobile networks due to pinball routing, for example, a Personal Area Network (PAN) inside a vehicle. The authors propose an Extended Mobile IPv6 route optimization (EMIP) scheme to enhance the performance of nested mobile networks in local and global mobility domain. The EMIP scheme is based on MIPv6 route optimization and the root Mobile Router (MR) performs all the route optimization tasks on behalf of all active Mobile Network Nodes (MNNs). Thus, the network movement remains transparent to sub MRs and MNNs and modifies only MRs and MNNs leaving other entities untouched and is more efficient than the Network Mobility Basic Support protocol (NEMO BS). The authors carried out an extensive simulation study to evaluate the performance of EMIP.
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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 | 0.001 |
| 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".