A Novel Asynchronous, Energy Efficient, Low Transmission Delay MAC Protocol for Wireless Sensor Networks
Bibliographic record
Abstract
Wireless sensor networks use battery-operated computing and sensing devices. It is often impractical or even impossible to charge/replace the exhausted batteries of the nodes. Thus, energy consumption is the main concern in a wireless sensor network. Moreover, a critical event detected by the sensor network should be delivered to the user as soon as possible. Thus, for sensor networks, both energy efficiency and transmission latency are important parameters. In this paper, we propose a novel asynchronous, duty cycled, energy efficient, and low transmission delay for wireless sensor networks, which addresses all the sources of energy waste to make the medium access more energy efficient, while keeping transmission delay low.The currently available asynchronous contention-based MAC protocols require that proper strategies for sender and receiver nodes are provided to rendezvous. However, the proposed protocol does not rely on any rendezvous between sender and receiver. For evaluating the novel MAC protocol, we have simulated this protocol and two very efficient and established MAC protocols, RTS/CTS IEEE 802.11 and S-MAC. The simulation results indicate that the proposed protocol has very good performance. In addition, as the results show, the novel protocol provides a very suitable balance between energy efficiency and transmission delay.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".