Machine-to-machine (M2M) communications
Bibliographic record
Abstract
Wireless connectivity is rapidly expanding beyond traditional mobile devices used by humans. In the near future, many wireless devices (e.g., sensors and actuators) will be connected in the framework of the Internet-of-Things (IoT) [363]. In cellular networks, hundreds or thousands of devices can exist in one cell. Therefore, the concept of machine-to-machine (M2M) communications has been introduced to handle the transmission of a number of devices in the network. M2M communication, also known as machine-type communications (MTC), refers to mobile nodes communicating over a network without (or with minimal) human intervention. M2M communication enables ubiquitous connectivity among autonomous devices and/or Internet connectivity of MTC devices (i.e., communications between an MTC device and an M2M server or between two MTC devices). M2M communication is different from human-to-human (H2H) communication, which mainly involves voice calls, messaging, and web browsing. The goal of M2M communications is to increase the level of system automation by allowing the devices and systems to exchange and share data. Therefore, the protocol and data format are the major issues in M2M communications owing to the need to ensure seamless data and control flows.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".