Capacity Region of ALOHA Protocol for Heterogeneous IoT Networks
Bibliographic record
Abstract
In an Internet of Things (IoT) network, heterogeneous users with different priorities and service requirements will co-exist. This makes scheduling access to the shared communication medium a major challenge. To tackle this challenge, we consider the application of irregular repetition slotted ALOHA (IRSA), one of the best-performing random access protocols for homogeneous networks, for a heterogeneous multiclass IoT network. To this end, centralized and distributed implementations of the IRSA for multiclass IoT networks is proposed. Then, we focus on finding the network performance boundaries by studying the set of feasible throughput values for each class achieved via IRSA, called the capacity region. In addition to identifying the capacity region, the average and maximum delay of the users' packet delivery for both centralized and distributed IRSA are investigated. Our throughput and delay analysis reveals that the performance of distributed IRSA achieves that for the centralized implementation as the number of users increases. Further, we use our capacity region analysis to find the optimal IRSA strategy that maximizes the weighted sum-throughput of the network.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".