A Hierarchical Approach to Handle Inter-domain Mobility in SDN-based Networks using Mobile IP
Bibliographic record
Abstract
Mobility management has become crucial in highly dynamic environments. As mobility traffic grows, handover rates between cells and different technologies increase. Handover has been studied extensively in order to enhance mobile users' perceived services. Mobile IP (MIP) is a standard protocol that handles handover efficiently in conventional networking. Recently, as Software Defined Networking (SDN) paradigm steps in, its impact on mobile networks has been studied, and it indeed benefits mobility management. To exploit the advantages of SDN and MIP, a merge of them is needed, which requires the modification of MIP. In this paper, we study a modified version of MIP protocol in a hierarchical architecture, taking into consideration metrics such as delay, packet loss, and throughput. We show that a hierarchical setting betters handling handover between heterogeneous networks. Our algorithm's performance shows improvement in decreasing total delay if the hierarchical architecture is applied compared to a distributed architecture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".