Smart transport layer based mobility for horizontal and vertical handoffs
Bibliographic record
Abstract
Mobility management remains an important task to be investigated while integrating homogeneous and heterogeneous wireless networks. Traditionally, IP layer is widely used to implement roaming solutions including Mobile IP , HMIP , FMIP , FHMIP , etc. With the standardization of the Stream Control Transmission Protocol, which offers new interesting features such as multihoming and multistreaming, experiencing mobility at the transport level becomes more attractive. Indeed, this layer is endowed with various connectivity facilities and flow control features that render the transport layer more appropriate to support seamless roaming. To take benefit from these new facilities, several SCTPbased mobility schemes have been proposed. Nevertheless, none can claim to be the ultimate solution since they suffer from drawbacks such as unnecessary handoff delays and signaling loads. Moreover, the throughput measured immediately after a handoff is affected quite considerably by spurious retransmissions due to packet loss and failed Selective Acknowledgment messages (SACKs). In this paper, we propose a smart Hierarchical Transport layer Mobility scheme ( sHTM ) which deals with homogeneous and heterogeneous handovers, reduces packet loss, handoff latencies and improves throughputs. sHTM exploits the dynamic address reconfiguration feature of SCTP and introduces a new mobility unit to effect more efficient handoff procedures. Simulation results reveal that sHTM guarantees lower handoff latency and good throughput during the handoff period compared to existing mSCTP-based solutions.)
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".