Localization in time and space for wireless sensor networks: A Mobile Beacon approach
Bibliographic record
Abstract
Localization in time and space can be defined as the problem of solving both synchronization and positioning problems at the same time. This is a key problem for wireless sensor networks that need to determine the timing and location information of detected phenomena, especially for tracking applications. In this paper, we discuss the relationship between these two problems and propose the Mobilis (Mobile Beacon for Localization and Synchronization) algorithm, a new time-space localization algorithm for wireless sensor networks. The main aspect of the Mobilis algorithm is the use of a mobile beacon for both localization and synchronization. A mobile beacon is a node that is aware of its time and position (e.g. equipped with a GPS receiver) and that has the ability to move around the sensor field. In our algorithm, the synchronization component uses the packets required by the positioning component to improve its performance. Similarly, the positioning component benefits from the communication required by the synchronization component to decrease errors. We also present an extensive set of experiments and simulations to evaluate the performance of our algorithm.
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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".