Cherntanomwong, Panarat

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panarat.ch@kmitl.ac.th

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An Efficient Radio Map Construction Method Using Region-Aware Adaptive Ensemble Regression for Indoor Localization
(2025-01-01)
Adiyatma, Farid Yuli Martin
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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.
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TLB and WC-TLB-MM: The Improved Min-Max Algorithms for Multi Targets Indoor Localization
(2023-01-01)
Adiyatma, Farid Yuli Martin
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Suroso, Dwi Joko
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Cherntanomwong, Panarat
Internet of Things (IoT)-based Indoor localization is the most commonly used system to determine target locations indoors. It applies to various purposes, e.g., indoor navigation, asset tracking in warehouse management, and tracking people in hospitals. Distance-based techniques using the Received Signal Strength Indicator (RSSI), e.g., Min-Max, are widely applied because they can be directly implemented without prerequisite work such as site surveys. However, a challenging indoor environment with high numbers of interiors and people can obstruct signal propagation. This obstruction can reduce the accuracy of translating RSSI to distance using the path loss model, which will degrade the localization accuracy. In this paper, we introduce two improved Min-Max (MM) algorithms, i.e., Three Layer Bounding Box Min-Max (TLB-MM) and Weighted Centroid TLB-MM (WC-TLB-MM), to alleviate the issue and achieve higher localization accuracy. The novelty of the proposed TLB-MM is incorporating RSSI error functions to generate three-layer bounding boxes: the inner, middle, and outer in the Min-Max algorithm. Meanwhile, WC-TLB-MM enhanced the TLB-MM algorithm by integrating the Weighted Centroid Localization Algorithm (WCLA) in the calculation process. We validate our proposal by conducting various experiments using Wi-Fi at 2.4 GHz deployed in a laboratory room of 10.17 m ×9.12 m. Experimental results demonstrate that TLB-MM improved the accuracy performance to 55.78% and 30.86%, while WC-TLB-MM gave 40.93% and 7.65% compared to Min-Max and WCLA, respectively. From these results, our proposed methods are proven simple yet applicable to RSSI-based indoor localization systems.
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Dynamic Ensemble Learning With Received Signal Strength Transformation for Robust Multi-Floor Wi-Fi Indoor Localization
(2026-01-01)
Yuli Martin Adiyatma, Farid
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Cherntanomwong, Panarat
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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.
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Fingerprint-based Indoor Localization via Deep Learning
(2023-03-24)
Suroso, Dwi Joko
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Cherntanomwong, Panarat
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Sooraksa, Pitikhate
Deep learning (DL) application is proven helpful in a vast research field. One recent trend is to employ DL in radio frequency (RF)-based indoor localization. The fingerprint technique is the most used indoor localization technique known for its accuracy and performance. However, the fingerprint technique pays a high cost and effort in offline database construction, while its performance solely depends on the database density. Moreover, to apply deep learning, we also need a large dataset for it to learn efficiently. We propose to implement a DL-based fingerprint technique to tackle both problems of dataset scarcity and localization performance. We propose the DL's discriminative model, i.e., multilayer perceptron (MLP), for classification tasks. For the fingerprint database augmentation, we employed the generative model, i.e., Generative adversarial networks (GANs). We considered using a received signal strength indicator (RSSI) from a measurement campaign based on Wi-Fi devices for the database. The total area of interest is 25 m<sup>2</sup> inside the typical classroom environment, and we consider the 25 fingerprint locations as labels. We have a dataset of 1,250 rows x 8 columns (from 8 reference points). From the results, by using only 50% of actual data combined with the 125 synthetic data, we can improve the accuracy by more than 200% compared to only using 50% of actual data and show a 60% improvement in the loss. The combination of 100% actual data and 125 synthetic data gives the best accuracy and loss performance of 0.76 and 0.85, respectively. It gives an improvement of 144% in accuracy and 200% loss performance. By implementing deep learning for fingerprint techniques for data augmentation and classification, we can achieve good performance and reduce the workload of fingerprint database construction.

Cherntanomwong, Panarat

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