Range-Based Localization in Wireless Networks Using the DBSCAN Clustering Algorithm
Bibliographic record
Abstract
Node localization has many applications in wireless networks. For example, it can be used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorithms use location metrics such as ToA, TDoA, RSS, and/or AoA to estimate the distance between nodes. Range free algorithms are based on proximity sensing. Range-based algorithms are more accurate but also more computationally complex. However, in applications such as target tracking, localization accuracy is important. In this paper, we propose a new range-based algorithm which is based on decision tree classification and the density based spatial clustering of applications with noise (DB SCAN) algorithm, which are well known in data mining. The Euclidean distance between intersection points is used as a distance metric, and the DBSCAN algorithm is applied to a subset of intersection points based on this metric. Different performance measures are used to compare our localization algorithm with linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS SVD). The proposed algorithm is shown to perform better than the LLS and WLS-SVD algorithms even when the anchor geometric distribution about an unlocalized node is poor.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".