A Two-Phase Algorithm for Locating Sensors in Irregular Areas
Bibliographic record
Abstract
In wireless sensor networks, location-aware applications require an accurate and robust sensor localization algorithm. Among them, most of the multihop-based localization algorithms approximate the shortest path distances to the Euclidean distances. This approximation is valid only if the sensors are uniformly and densely deployed in a convex area where the shortest paths are close to straight lines. However, in a real- world setting, the convexity assumption may not always be valid. Non-convex deployment areas, such as C-shaped or S-shaped topologies, can corrupt the localization results severely due to erroneous distance estimations distorted by the non-convex topology. In this paper, we formulate the localization problem in irregular areas as a constrained least-penalty problem. We then propose a two-phase algorithm to eliminate the impact of irregularities. In the first phase, the estimated position is confined in the intersection area of the communication range constraints. In the second phase, the distorted measurements are eliminated by using a robust position estimator. Simulation results show that the two-phase algorithm outperforms some of the existing multihop localization algorithms in terms of a lower average localization error in both C-shaped and S-shaped topologies. The effects of anchor density, range error and communication range on localization performances are studied as well.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".