EnOR: Energy balancing routing protocol for underwater sensor networks
Bibliographic record
Abstract
Opportunistic routing (OR) has emerged as a promising paradigm to the design of routing protocols for underwater sensor networks (UWSNs). However, despite of its advantages, it introduces a critical problem that has been neglected until now: the immutable transmission priority level of the next-hop forwarding nodes. This characteristic can lead to an overuse of a unique node (or a few of them), quickly depleting its battery, creating network partitions, shortening the network lifetime and, consequently, degrading the application's performance. In this paper, we shed light on the need for mechanisms for rotating the forwarding priority level between candidate nodes. We propose a baseline new lightweight energy-aware opportunistic routing (EnOR) protocol, leading to a balanced energy consumption and prolonged UWSN network lifetime. EnOR rotates the transmission priority level of the forwarding candidate nodes by considering the remaining energy, link reliability and packet advancement of them. Simulation results reveal that EnOR effectively extends the network lifetime as compared with other underwater sensor network opportunistic routing protocols.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".