Mobility management in hybrid<i>ad‐hoc</i>networks and the Internet environment
Bibliographic record
Abstract
Abstract The integration of mobilead‐hocnetworks (MANETs) with the Internet provides flexible and multi‐hop communication capability in hybrid wired and wireless networks. Existing architectures for integrating these networks use Mobile IP andad‐hocrouting protocols with fixed gateways. In this paper, we propose a mobility management scheme based on a mobile gateways (MGs) architecture. We designed a buffering mechanism for micro‐ and macro‐mobility management in hybrid networks. The performance of mobility management with optimized handover (MM‐OH), optimized handover with prediction (MM‐OHP) and forced handover (MM‐FH) are evaluated using simulation. Our simulation results show that a buffering mechanism coupled with the hybrid gateway discovery results in a higher packet delivery ratio for MM‐OH and MM‐OHP compared with the MM‐FH scheme. The MM‐OHP scheme has a lower number of handovers compared with the MM‐OH scheme. Moreover, the simulation experiments reveal that the speed of the MG has a relatively higher impact on performance than the speed of mobile nodes. Copyright © 2007 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".