A Novel Hybrid MAC Protocol for Sustainable Delay-Tolerant Wireless Sensor Networks
Bibliographic record
Abstract
Efficient MAC protocols are fundamental to conserve energy and enable sustainable delay-tolerant sensor networks (DTSNs). They can be explored to reduce energy consumption, deal with relaxed latency requirements, support mobility and address diverse traffic loads. In this paper, we theoretically analyze the performance of reservation-based and contention-based MAC protocols in DTSNs regarding throughput and energy consumption, respectively. According to the derived theoretical results, we propose a TRaffic-adaptive energy-efficient MAC protocol (TREEM) to achieve better data transmissions as well as energy efficiency, in order to satisfy DTSN requirements. More precisely, our protocol can dynamically switch its working mode between contention and reservation to adapt to the varying data traffic. In addition, to further improve the energy efficiency of DTSN, our algorithm can also calculate the more suitable duty/active period length. The simulation results of TREEM demonstrate better performance in terms of energy efficiency and traffic adaptability than the schedule-based MAC protocol TDMA, the contention-based protocol CSMA, and the traffic-adaptive protocol TRAMA under mobile DTSN environments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".