Benefits of the IEEE 802.15.4's MAC layer acknowledgements in Ad-Hoc networks: An experimental analysis
Bibliographic record
Abstract
This paper highlights the benefits of IEEE 802.15.4's unslotted carrier sense multiple access collision avoidance (CSMA-CA) MAC layer acknowledgements (ACKs) in ad-hoc wireless networks. We performed different experimental studies to analyse the impact of enabling and disabling the ACKs on event detection ratio (EDR), available bandwidth estimator, and flow admission control algorithms. Comparison of the best performance of both, i.e., enabling and disabling the ACKs w.r.t. EDR demonstrates the following benefits of enabling the ACKs. 97% EDR by only transmitting a single message corresponding to each event, 52% higher EDR, 59% fewer total transmissions, and 78% lower event propagation delay to a sink node. The ACKs improves the effectiveness of the state-of-the-art flow admission control algorithms by up to 166%. Enabling the ACKs, and using no admission control algorithm is up to 40% more effective compared to using the admission control algorithms with the ACKs disabled. Similarly, estimating the residual data relaying capacity of the IEEE 802.15.4 communication link using an available bandwidth estimator is only useful when the ACKs are enabled.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".