A seamless handover scheme for vehicles across heterogeneous networks
Bibliographic record
Abstract
Next generation heterogeneous networks require an effective handover scheme, optimised specially to support network mobility. The paper proposes an architecture to support seamless mobility of mobile networks across heterogeneous networks. We consider the vehicular scenario where a vehicle is provided with mobility through different internet service providers (ISPs) and propose to use multiple mobile routers-based handover scheme in vehicles. The multiple mobile routers architecture is extended to include multiple home agents (HAs) where each HA belongs to different administrative domains. We carry out comparative performance evaluation of the handover performance of the proposed scheme in regard to UDP and TCP throughput, service disruption time, and signalling overhead. Moreover, we analyse the impact of vehicle speed and overlapping distance of access routers on packet loss during handover period. The simulation results show that the proposed scheme provides a mobile network with seamless mobility across heterogeneous networks. The overlapped reception of packets from different access routers significantly minimises packet losses during handover even without reducing handover latency.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".