Distributed and Adaptive Medium Access Control for Internet-of-Things-Enabled Mobile Networks
Bibliographic record
Abstract
In this paper, we propose a distributed and adaptive hybrid medium access control (DAH-MAC) scheme for a single-hop Internet of Things (IoT)-enabled mobile ad hoc network supporting voice and data services. A hybrid superframe structure is designed to accommodate packet transmissions from a varying number of mobile nodes generating either delay-sensitive voice traffic or best-effort data traffic. Within each superframe, voice nodes with packets to transmit access the channel in a contention-free period (CFP) using distributed time division multiple access, while data nodes contend for channel access in a contention period (CP) using truncated carrier sense multiple access with collision avoidance. In the CFP, by adaptively allocating time slots according to instantaneous voice traffic load, the MAC exploits voice traffic multiplexing to increase the voice capacity. In the CP, a throughput optimization framework is proposed for the DAH-MAC, which maximizes the aggregate data throughput by adjusting the optimal contention window size according to voice and data traffic load variations. Numerical results show that the proposed MAC scheme outperforms existing quality-of-service-aware MAC schemes for voice and data traffic in the presence of heterogeneous traffic load dynamics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".