Comparative Study on Range Free Localization Algorithms
Bibliographic record
Abstract
Nowadays, determining the exact position of sensor nodes in WSNs is an important factor in many applications. As the need of the localization accuracy varies between the applications, many localization techniques are used in different applications. Hence, node localization becomes one of the fundamental challenges in WSNs. Localization is categorized in two groups: range free and range based. In the range free techniques, localization is related between nodes and topological information of sensor nodes. On the other hand, in range based techniques, it is required to calculate distance between nodes. The scope of this paper is on range free localization. We survey different range free localization techniques and discuss some localization-based applications where the location of these sensor nodes is vital and sensitive. On the second part of the paper, we describe five algorithms namely: Centroid, Amorphous, APIT, DV-Hop and DV-HopMax algorithms. We simulate these algorithms using MATLAB based on different setups. Moreover, we make a comparative study between these localization algorithms based on different performance metrics showing their pros and cons.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".