iCCA-MAP: A New Mobile Node Localization Algorithm
Bibliographic record
Abstract
Accurately determining the location of mobile wireless sensor nodes in real-time is essential for many purposes. This paper proposes a new and efficient algorithm for localization of mobile node(s) within a WSN. The proposed algorithm, called iterative CCA-MAP (iCCA-MAP), is based on the CCA-MAP algorithm which applies an efficient nonlinear data mapping technique. The latter has been shown to perform extremely well for localizing stationary nodes in WSNs. Simulation results show that the localization error results for both CCA-MAP and iCCA-MAP are similar. However, the computational time required for obtaining location results using the iterative CCA-MAP scheme is far smaller than that of the original CCA-MAP. The advantage of the proposed algorithm is that it can provide the mobile node's location information at near real-time, allowing for it to be applied at a much higher frequency in order to provide up-to-date estimations of the mobile node's position, which can result in a lower localization error.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".