EM channel characteristics and their impact on MAC layer performance in underwater surveillance networks
Bibliographic record
Abstract
Abstract Wireless communications and multihop networking based on electromagnetic (EM) radios have been considered as an alternative to acoustic communications in seawater because in typical applications for networked underwater sensing, EM waves are much less susceptible to multipath distortion and environmental noise. In this paper, we discuss the characteristics of EM channels in seawater and derive a novel EM signal propagation model. Based on the propagation model, we implement in the QualNet network simulator an EM underwater surveillance network for studying the impact of these unique characteristics of underwater EM channels on the media access control (MAC) layer performance. Both a single‐hop network model and a multihop network model are simulated. Simulation results show that the carrier sense multiple access without or with acknowledgement (CSMAWithoutACK or CSMAWithACK, respectively) has advantages over ALOHA and multiple access with collision avoidance in terms of packet average delay, packet delivery ratio, and MAC scheme overhead. In the multihop network model, the use of CSMAWithoutACK significantly reduces the packet average delay and the MAC scheme overhead, and both CSMAWithoutACK and CSMAWithACK achieve more than 90%packet delivery ratio. Therefore, CSMAWithoutACK (with no handshaking via request‐to‐send and clear‐to‐send control packets) is the most appropriate MAC protocol to be used in multihop EM‐based underwater surveillance networks. © Her Majesty the Queen in Right of Canada 2014. Reproduced with the permission of the Minister of Industry Canada
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".