IoT/M2M from research to standards: the next steps (Part I) [Guest Editorial]
Bibliographic record
Abstract
As the pace of IoT deployments accelerate, IoT standards are undergoing major evolutions, sometimes revolutions. For instance, cellular networks standards are now adding techniques to improve network performance to address traffic patterns generated by an increasing number of IoT devices. Ongoing discussions around 5G requirements may become game changing for M2M communications because the standard will be designed, from the ground-up, for massive scale IoT deployments. This is a radical shift compared to the “quick-fixes” 3GPP and 3GPP2 have been adding to 2G/3G and 4G standards so far. Another example of this radical shift is related to IoT service platforms (such as the platform standardized by oneM2M) and IoT applications. Semantic interoperability is now emerging as a major trend that allows data exchange between applications, an increased level of interoperability, analytics, and reasoning. With ontologies engineering, researchers will soon overcome the limitations of static data models and bridge the gap between the currently deployed vertical silos. Other areas that will see intense standardization activity are IoT security and low power wide area connectivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".