Positioning of Wireless Sensor Network under Emergency Communication Environment
Bibliographic record
Abstract
With the development of microelectromechanical system (MEMS), embedded system, and wireless communication, it is now feasible to implement and deploy wireless sensor network (WSN) in emergency communication environment. However, the positioning accuracy of WSN nodes needs to be further improved. To solve the problem, this paper improves the initial value calculation method of multi-hop positioning algorithms, which are suitable for emergency communication environment, and puts forward a WSN node positioning algorithm that narrows the initial values of Kalman filter. By narrowing the initial value range of Kalman filter, the specially deployed sensors could accurately derive its position from the known positions of anchor nodes. To prevent error accumulation in the network, distributed computing was performed to solve the global nonlinear optimization problem, and calculate the position of the nodes. Simulation results show that the proposed algorithm can improve the WSN positioning accuracy under emergency communication environment, while greatly saving computing and communication costs. The research further improves the practicability and efficiency of multi-hop positioning algorithms in emergency communication environment.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".