Bibliographic record
Abstract
Many devices and machines used in diverse applications require ubiquitous connectivity to the Internet through cellular network. These devices have different requirements in terms of their location, data rates, mobility, energy consumption, latency, complexity, power output level, spectrum, and security. These criteria impose specific requirements on the network infrastructure. While some Internet of Things (IoT) enabling technologies exist today that may be able to address the wide area coverage requirement of the IoT devices, they fall short as compared to the 3rd Generation Partnership Project (3GPP) technology in terms of coverage, scalability, interoperability, Quality of Service (QoS), and security. 3GPP Release 13 introduced two categories of IoT technologies called LTE-M and narrow band IoT (NB-IoT). In LTE release, 14, and 15, the enhancements of LTE IoT continued to provide cellular IoT connectivity to more IoT devices and in more diverse applications. In this article, we provide an overview of the evolution from Releases 13 to 15 (a rich technology roadmap toward 5G), and for multiple different use cases discuss the technology requirements that need to be met for each specific application.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".