3D Real-Time Routing Protocol With Tunable Parameters for Wireless Sensor Networks
Bibliographic record
Abstract
A novel 3D real-time geographical routing protocol (3DRTGP) for wireless sensor networks is presented in this paper. 3DRTGP controls the number of forwarding nodes in the network by limiting forwarding to a unique packet forwarding region (PFR). PFR selection is based on the network density around each of the forwarding nodes, which reduces the number of redundant packet transmissions, collisions, and congestion. This enables 3DRTGP to meet the real-time requirements of a time sensitive application. In order to meet the packet delivery delay deadline, a forwarding node uses its own delay parameters, such as queuing and processing delays, and the expected number of hops to the destination to make a forwarding decision. 3DRTGP does not require an explicit exchange of neighboring information, such as location information. 3DRTGP is evaluated through extensive simulations under various network densities and traffic load conditions, which provides network tuning parameters to meet the real-time requirements of applications. 3DRTGP heuristically solves the void node problem (VNP) in 3-D deployments. It is demonstrated that 3DRTGP resolves VNP given that there is no network partitioning. 3DRTGP significantly outperforms similar 3-D geographical routing protocols in terms of end-to-end delay and miss ratio.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".