Token-Based Adaptive MAC for a Two-Hop Internet-of-Things Enabled MANET
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Bibliographic record
Abstract
In this paper, a distributed token-based adaptive medium access control (TA-MAC) scheme is proposed for a two-hop Internet of Things (IoT)-enabled mobile ad hoc network. In the TA-MAC, nodes are partitioned into different one-hop node groups, and a time division multiple access (TDMA)-based superframe structure is proposed to allocate different TDMA time durations to different node groups to overcome the hidden terminal problem. A probabilistic token passing scheme is devised to distributedly allocate time slots to nodes in each group for packet transmissions, forming different token rings. The distributed time slot allocation is adaptive to variations of the number of nodes in each token ring due to node movement. To optimize the medium access control (MAC) design, performance analytical models are presented in closed-form functions of both MAC parameters and network traffic load. Then, an average end-to-end delay minimization framework is established to derive the optimal MAC parameters under a certain network load condition. Analytical and simulation results demonstrate that, by adapting the MAC parameters to the varying network condition, the TA-MAC achieves consistently minimal average end-to-end delay, bounded delay for local transmissions, and high aggregate throughput. Further, the performance comparison with other MAC schemes shows the scalability of the proposed MAC in an IoT-based two-hop environment with an increasing number of nodes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it