KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Cherntanomwong, PanaratSubject: search.filters.subject.fingerprinting

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Dynamic Ensemble Learning With Received Signal Strength Transformation for Robust Multi-Floor Wi-Fi Indoor Localization
    (2026-01-01)
    Yuli Martin Adiyatma, Farid
    ;
    Cherntanomwong, Panarat
    ;
    Joko Suroso, Dwi
    Received Signal Strength (RSS)-based Wi-Fi localization offers a cost-effective solution for multi-floor indoor location estimation. However, its accuracy is often degraded by signal fading, multipath propagation, and device heterogeneity, posing major challenges to reliable localization. Recent studies have increasingly employed deep neural networks due to their ability to extract meaningful patterns from RSS data; however, these models require substantial computational resources and extensive parameter tuning, which limits their adaptability across diverse dynamic environments. To address these limitations, we propose DELLoc-RT, a localization framework integrating Dynamic Ensemble Learning (DELLoc) with RSS Transformation (RT) for accurate, efficient, and adaptable multi-floor indoor localization. The RT module applies Sigmoid-scaled normalization and confidence weighting to convert RSS values into compact, learnable features. DELLoc employs multiple base learners optimized via the Tree-structured Parzen Estimator with a pruning strategy (TPE-PS) that accelerates convergence by focusing on promising configurations. Additionally, Iterative Ensemble Optimization with Stepwise Selection (IEO-SS) selects complementary learners to enhance overall performance. Experimental results demonstrate that DELLoc-RT achieves floor classification accuracies of 93.32%, 94.38%, and 94.02% on the UJIIndoorLoc, UTSIndoorLoc, and Tampere datasets, respectively, with mean Euclidean errors (MEE) of 10.63 m, 7.87 m, and 8.19 m. These results highlight the model’s strong adaptability across diverse datasets. Evaluation on a self-constructed dataset further confirms that DELLoc-RT delivers high accuracy and efficiency while substantially reducing the need for manual tuning, enabling rapid deployment in practical scenarios.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    An Efficient Radio Map Construction Method Using Region-Aware Adaptive Ensemble Regression for Indoor Localization
    (2025-01-01)
    Adiyatma, Farid Yuli Martin
    ;
    Cherntanomwong, Panarat
    The increasing adoption of indoor localization has increased demand for location-based services (LBS) to enhance daily efficiency. Fingerprinting is a widely used method due to its high accuracy and resilience to multipath fading, but it requires dense sampling and extensive data collection, making large-scale site surveys labor-intensive and often impractical. To alleviate these challenges, interpolation and regression-based methods have been explored for their scalability and reduced manual effort. However, these approaches often struggle to model the complex, multimodal nature of real-world Received Signal Strength Indicator (RSSI) distributions. To address this limitation, we propose a novel Region-Aware Adaptive Ensemble Regressor (RAER) for generating synthetic RSSI values. RAER adapts to the statistical characteristics of the training data to efficiently produce high-quality virtual RSSI maps with low computational cost. The model integrates two components: 1) a clustering algorithm that partitions the radio map based on fingerprint signal similarity, and 2) an adaptive ensemble regression framework that combines multiple base regressors using weighted averaging, where weights are inversely proportional to each regressor’s mean absolute error (MAE). By prioritizing regressors with lower localization error, RAER improves the realism of synthetic RSSI data and enhances radio map accuracy. Experimental evaluations conducted in a multi-story university building demonstrate that RAER can reconstruct radio maps, which reduces the need for site surveys by up to 25%. Furthermore, it improves localization accuracy by 5.06%, outperforming existing methods and offering a scalable and practical solution for fingerprint-based indoor positioning systems.