Error analysis of localization systems for sensor networks
Bibliographic record
Abstract
The establishment of a localization system is an important task in wireless sensor networks. Due to the geographic correlation of the sensed data, location information is commonly used to name the gathered data, address nodes and regions, and also improve the performance of many geographic algorithms. Depending on the localization algorithm, different error behaviors (e.g., mean, probability distribution, and correlation) can be exhibited by the sensor network. The process of understanding and analysing this behavior is the first step toward a mathematical model of the localization error. Furthermore, this knowledge can also be used to propose improvements to these systems. In this work, we divide the localization systems into three components: distance estimation, position computation, and the localization algorithm. We show how each component can affect on the final error of the system. In this work, we concentrate on the third component: the localization algorithm. The error behaviors of three known localization algorithms are evaluated together in similar scenarios so the different behaviors of the localization error can be identified and analysed. The influence of these errors in geographic algorithms is also analysed, showing the importance of understanding the error behavior and the importance of geographic algorithms which consider the inaccuracy of position estimations.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".