RF channel modelling and multi‐hop routing for wireless sensor networks located on oil rigs
Bibliographic record
Abstract
This study presents a combined analytical and empirical model for predicting the signal loss effects of metallic drilling rig structures on radio‐frequency electromagnetic waves used by wireless sensor networks (WSNs) installed on a drilling rig. The model is based on the combination of free space path loss and the excess loss caused by the metallic structure separately. The authors combine both losses to predict the overall loss of signal strength. The model has been validated against field data collected from multiple drilling rigs. Further modification of the model to include the effects of different signal frequencies is under way. They present simulation results from OMNeT++ based on their model, to establish the packet loss and energy consumption expected for a real WSN. They also present a novel dynamic multi‐hop routing protocol, which improves network performance by removing the constraints of single‐hop forwarding. The algorithm directs packets to their destination via a selected node within a routing ‘cluster’. By combining their channel model and routing protocol, they are able to achieve 100% packet success, while setting transmit power levels appropriately to achieve the longest possible network lifetime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".