Accurate Range-Free Localization in Multi-Hop Wireless Sensor Networks
Bibliographic record
Abstract
To localize the wireless sensor networks nodes, only the hop-based information (i.e., hops' number, average hop size, and so on) has been, so far, exploited by range-free techniques, with poor-accuracy, however. In this paper, we show that localization accuracy may greatly benefit from joint exploitation, at no cost, of the information already provided by the forwarding nodes (i.e., relays) between each anchor (i.e., position aware) and sensor nodes pair. As such, we develop a novel range-free localization algorithm, derive its average location estimation error (LEE) in closed-form, and compare it in LEE performance with the best representative algorithms in the literature. We show that the proposed algorithm outperforms them in accuracy. In contrast to the latter, we further prove that it is able to achieve an LEE average and variance of about 0 when the number of sensors is large enough, thereby achieving an unprecedented accuracy performance among range-free techniques.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".