VLI: Variable-Length Identifier for Interconnecting Heterogeneous IoT Networks
Bibliographic record
Abstract
Long identifier brings low packet forwarding efficiency in Internet of Things (IoT), whereas short identifier may suffer from the exhaustion of identifier space. Compared with fixed-length identifiers (e.g., IPv4 and IPv6), flexible identifiers are expected for balancing the packet processing efficiency with the various IoT scales. However, it is challenging to make IoT support the flexible identifier-based forwarding. In this letter, we firstly proposed a novel variable-length identifier (VLI) solution for interconnecting IoT networks. In particular, a VLI datagram header is designed to effectively support a flexible identifier field. Following a basic VLI header, one (multiple) extension header(s) can be added if require. Each extension header includes a fixed-size identifier field. According to the combination of multiple identifier fields, the variable-length identifier can be easily achieved, resolved and supported by the IoT nodes in a flexible way. Experimental results show that VLI can decrease the processing delay effectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".