Collision Avoidance Energy Efficient Multi-Channel MAC Protocol for UnderWater Acoustic Sensor Networks
Bibliographic record
Abstract
Collisions in underwater acoustic networks can not be tolerated due to the fundamental differences between underwater acoustic propagation and terrestrial radio propagation. Thus, conceiving medium access protocols that avoid collision to the most possible extent is of paramount importance. In this paper, a multi-channel MAC protocol, MC-UWMAC, especially designed for underwater acoustic sensor networks, is proposed and evaluated. MC-UWMAC is an energy efficient MAC protocol that aims at achieving a collision free communication. MC-UWMAC operates on a single slotted control channel to avoid the missing receiver problem and multiple data channels to improve the network throughput. To guarantee to the most possible extent a collision free communication, MC-UWMAC uses two key newly designed procedures: i) a grid based slot assignment procedure on the common slotted control channel that approaches the 2-hop conflict free slot assignment and ii) a quorum based data channel allocation procedure. More precisely, according to MC-UWMAC, a sender uses its own dedicated slot on the common control channel for handshaking with an intended neighbor receiver. However, data transmission takes place in a unique data channel especially reserved for this pair of neighbor nodes. In fact, MC-UWMAC reserves for each pair of neighbor nodes a unique data channel that aims at being 2-hop conflict free. As such, the probability of collision is highly reduced and even completely mitigated in some scenarios. In addition, by using multiple channels, MC-UWMAC allows multiple data communications along with handshaking on the common control channel to take place at the same time and hence the network throughput as well as energy efficiency are improved. Simulation results show that MC-UWMAC can greatly improve the network performance especially in terms of energy consumption, throughput, and end-to-end 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".