THREE DIMENSIONAL REAL-TIME GEOGRAPHICAL ROUTING PROTOCOLS FOR WIRELESS SENSOR NETWORKS
Bibliographic record
Abstract
One of the most important concerns in the operation of Wireless Sensor Network(WSN) is the real-time data delivery. This dissertation addresses the problem of real-time data delivery and void node problem in three dimensional WSN, which has a signicant impact on the network performance. In order to provide an accurate route calculation for reliable data delivery the third coordinate of the location sensor nodes is considered in this dissertation. Additionally, two dierent heuristic solutions for void node problem in three dimensional space have been provided to elevate the eect of long route and spares regions on assurance of real-time data delivery. In order to provide a wide applicable soft real-time routing protocol two decentralized geographical routings are proposed: Three Dimensional Real-Time Geographical Routing Protocol (3DRTGP) and Energy-Aware Real-Time Routing Protocol for Wireless Sensor Networks (EART). 3DRTGP and EART are designed to t with WSNs that are deployed in 3D space. Both protocols benet from utilizing the third coordinate of nodes' locations to achieve less packet end to end (E2E) delay and packet miss ratio.In 3DRTGP, void node problem in 3D space was solved based on adaptive packet forwarding (PFR) region. 3D-VNP solution solely was done locally and without any messaging overhead. In EART, 3D-VNP was solved based on an adaptive spherical forwarding wedge (SFW).
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".