Analysis of priority arbitration in low‐rate CSMA/CA‐based differentiated access with throughput optimization
Bibliographic record
Abstract
Summary A class of applications, such as home energy management and control and utility data acquisition, is emerging in recent times where smart meters, sensors, and appliances are networked together for intelligent management and coordination. Such applications rely on low data rate communication of monitoring and control information at large scale. For the underlying networking infrastructure to facilitate communication of the real‐time and intermittent packet traffic expected, random access‐based protocols are regarded as suitable medium access control solutions. A key challenge in this regard is that the random access protocols are prone to throughput degradation when the number of contending nodes grows, as expected with the infrastructures involved. Besides, provision for certain degree of criticality/priority is needed for some of the packets compared with the rest. With this background, this paper analytically determines the criterion for throughput‐optimal operations in a network based on low‐ratecarrier sense multiple accessprotocol. In addition, ways to provide priority‐wise access differentiation at arbitrary proportions without a negative impact on the achievable throughput is incorporated within abinary exponential backoff‐basedcollision avoidance scheme. Discrete‐event simulations are performed to validate the accuracy of the approximations made in analysis.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".