Machine-to-machine (M2M) communications
Bibliographic record
Abstract
Introduction 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. D2D communication can be considered as a type of M2M communication when the D2D user equipments UEs are in close proximity and have small amounts of data to transmit among themselves (e.g., in application scenarios relating to the control of appliances in the home). In this chapter, we provide an overview of M2M communications in Section 11.2. Specifically, we focus on MTC in Long Term Evolution (LTE) and LTE-Advanced (LTE-A). Section 11.3 presents the mechanisms to support MTC, i.e., a random-access (RA) procedure and random-access-channel (RACH)-overload control mechanisms. Section 11.4 introduces a performance-modeling technique based on queueing theory to analyze the performance of the RA mechanism for M2M communications. Finally, Section 11.5 gives a summary of the chapter and lists some important research directions. Machine-to-machine (M2M) communications M2M communication, which is undergoing the process of standardization by the Third Generation Partnership Project (3GPP), can support a wide range of applications (e.g., secured access and surveillance, metering and smart grid, and Internet-of-Things).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".