Enhanced Gaussian Process-Based Localization Using a Low Power Wide Area Network
Bibliographic record
Abstract
With the recent advances of technology innovation in the Internet-of-Things (IoT) era, radio chips are able to transmit over long distances with extremely low energy consumption. While extending the range of communication links, the ability to provide large scale location-based services (LBS) solutions using native physical layer parameters from IoT networks will dramatically widen the availability of IoT applications. This letter proposes an enhanced Gaussian process-based localization solution for such a low power wide area network (LPWAN). It effectively deals with intermittent signals over a large area caused by low communication throughput, interference or packet collisions in LPWAN. Furthermore, a parametric model enhancement combining indoor and outdoor hypotheses and signal propagation statistics is proposed and evaluated. Field tests over a 37,500 square meter area have been conducted. Results show that the proposed method can provide LBS-quality positioning with a 2-D root mean square error of 20 to 30 meters, with an accuracy improvement of 29.8% outdoors and 40.6% indoors, respectively, compared to the traditional fingerprinting method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".