Analysis of optimal backhaul link selection in a novel maritime communication network
Bibliographic record
Abstract
Offshore fishing is serving as a major livelihood for millions of people around the world. OceanNet project aims at developing an effective, low-cost, long range communication system to provide internet connectivity at the sea. Wireless Backhaul network is formed by connecting Base Station (BS) in the shore to the Adaptive Backhaul Equipment (ABE) in the boats. The fishermen fishing in a particular fishing zone form a cluster. A mesh network is formed in the clusters to improve the connectivity. In this work, OceanNet Backhaul Link Selection (OBLS) algorithm is implemented in a hardware test-bed that models the OceanNet topology to assess the feasibility of using this in the off-shore boats. It also proposes and implements a controller as a static node in the Base Station network which analyzes the connectivity based on signal strength, noise floor and link quality and selects the best backhaul links by redirecting the route from Access Routers to the ABE having good Signal-to-noise (SNR) ratio to reach the Base Station. The throughput tests analyzed demonstrate that the packet delivery ratio is improved to a large extent after the application of the OBLS algorithm.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".