Medium access control techniques in M2M communication: survey and critical review
Bibliographic record
Abstract
Abstract Machine‐to‐machine (M2M) communication has received increasing attention in recent year. An M2M network exhibits some salient features such as large number of machines/devices, low data rates, delay tolerant/sensitive, small packets, energy constrained and low or no mobility. A large number of M2M terminals may exist in a small area with many trying to simultaneously and randomly access for channel resources, which will result in overload and access problem. This increased signalling overhead and diverse requirements of machine‐type communication (MTC) devices call for the development of flexible and efficient scheduling and random access techniques. In an M2M scenario, where the network is operating at high offered load with a large number of contending transmitters, distributed random access techniques are more appropriate than centralised scheduling techniques because of less control messages and better channel utilisation. There is a need for comparison of various medium access methods that can be used in the development of an efficient hybrid M2M and human to human network. In this article, we review and compare various scheduling and random access techniques in cellular networks, particularly in Long‐Term Evolution. We also discuss how successful they are to fulfill the unique requirements of M2M communication and networking. Resource management in M2M networks with a large number of MTC devices is also discussed from the access point of view. Energy efficiency, being one of the main challenges of quality‐of‐service‐constrained M2M communication, is also discussed. Minimisation of the energy consumption is tightly bound to channel access and hence considered in the comparison of various medium access control protocols. Finally, some potential research directions related to access control and resource allocation are presented for future work. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".