CCA-MAP and iCCA-MAP: stationary and mobile WSN localisation algorithms
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Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) are usually randomly deployed in a region of interest. As a result, algorithms that can determine the location of sensor nodes within a WSN are of great importance. In recent years, several localisation algorithms have been proposed for stationary WSNs. Due to the growing number of applications requiring mobility, algorithms for localising mobile WSNs have also gained much interest as of late. In this paper, we present our recent works on localisation algorithms for WSNs. An algorithm for mobile WSNs, which extends the stationary CCA-MAP algorithm, is presented. The algorithm, called iCCA-MAP, applies an iterative and efficient nonlinear data mapping technique in order to localise the position of a mobile node within a WSN. Simulations detailing the performance of iCCA-MAP are outlined and discussed. We also describe the implementation of the CCA-MAP localisation algorithm on a real WSN testbed. Most localisation algorithms that have been proposed only provide simulation results and have never been implemented on a real testbed. The results obtained show that the implementation results are consistent with the simulation results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it