An Efficient and Robust Fingerprint-Based Localization Method for Multifloor Indoor Environment
Bibliographic record
Abstract
Fingerprint-based indoor localization is one of the most promising solutions for various Intelligent Internet of Things (IIoT) systems. However, recent studies show that the key design challenges of current fingerprint-based localization techniques come from the following three aspects: 1) temporal variation caused by various patterns of IIoT device operations and stochastic fluctuation of wireless signals, 2) spatial unevenness of collected RSSI samples due to complex multi-floor environments, and 3) high feature sparsity of collected RSSI samples in large areas. To address these challenges, we present a localization architecture for multi-floor indoor localization in multi-building environment and accordingly propose a fingerprint-based localization method (referred to as GrowNetLoc) based on Gradient Boosting Neural Network (GrowNet) and Long Short-Term Memory (LSTM) network. Regarding building/floor identification, the gradient ensemble model GrowNet is utilized for extracting the mapping relationship between uneven RSSI samples and building/floor indices. Regarding location estimation, LSTM network is adopted as one layer of base learner to extract temporal features of RSSI samples, and a gradient boosting strategy is further used for overcoming the sample sparsity issue and improving the location estimation performance. Extensive experiments are conducted on real datasets and the results demonstrate that GrowNetLoc has superior localization accuracy and robustness performance compared with the existing methods.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
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".