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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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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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U-GMo: Individual Clip Detection from a Graduation Ceremony Video
(2024-01-01)
Treesoonrat, Natee
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Kriengchaiyaprug, Nunnapat
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Upadhayawong, Thanakann
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Lohapongpan, Warinya
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Chawuthai, Rathachai
Graduation ceremonies are important occasions in life. A video in this event is usually very long due to a lot of graduates getting their degree. This study suggests a method for automatically cutting the entire ceremony video into customized segments that only include the most significant events for each particular graduate, named U-GMo (Your Great Moment). The system uses deep learning with computer vision techniques, such as YOLOv8 for posture detection, to identify graduates by observing their motions and posture during the degree ceremony. After that, the identified bits are taken out and assembled into brief video snippets for every graduate. The algorithm can detect and extract each graduate's crucial moments with high performance, according to an examination conducted on a dataset of graduation ceremonies. The personalized video clips provide a convenient way to preserve the meaningful highlights from these milestone events.
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Hierarchical KNN for Smartphone-Based 3D Indoor Positioning
(2024-01-01)
Adiyatma, Farid Yuli Martin
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Sunimit, Samita
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Chokporntaveesuk, Thanwa
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Lualum, Krittima
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Chaisang, Naphat
Fingerprint-based localization, or positioning technique, is well-known to achieve high accuracy in location estimation in indoor environments where the multipath fading effect is severe. However, the accuracy of location estimation depends on the choice of the pattern matching techniques that are developed in the on-line phase. This paper proposes a new algorithm called Hierarchical K-Nearest Neighbors (KNN) for the pattern matching phase to estimate the location of the target in 3-dimensional (3D) indoor environments. For practical usage and saving budget and time for implementation, the Wi-Fi-based indoor positioning system (IPS) is implemented, and the smartphone is used as the user device. In this work, an Android smartphone is used for the study case. The results demonstrate that Hierarchical KNN achieves the lowest mean distance error (MDE) of approximately 3.263 m, outperforming various fundamental machine learning approaches such as Random Forest and KNN classifiers, with MDE reductions of 8.19% and 11.52%, respectively.
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Radio Map Augmentation Using DBSCAN and KNN Regression for Improved Indoor Positioning
(2024-01-01)
Adiyatma, Farid Yuli Martin
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Cherntanomwong, Panarat
The proliferation of smartphones has driven an increased demand for indoor location-based services. Consequently, location fingerprinting with Received Signal Strength Indicators (RSSI) has become a popular method for indoor positioning, as it accurately determines a target location by effectively mitigating the multipath effect commonly encountered in indoor environments. However, the fingerprinting technique faces challenges in constructing a radio map, which is exceedingly time-consuming and labor-intensive, limiting its real-world application. To address this issue, synthetic RSSI data can be generated using small datasets collected from sparse reference points (RPs). This paper proposes a method for generating synthetic data using the KNN Regression approach. To improve the accuracy of synthetic data synthesis, we employed DBSCAN to partition the entire region into clusters. We evaluated our proposed method using a radio map collected from 18 Wi-Fi routers in a two-story university building, reducing the radio map by uniformly removing data from several RPs by 25%, 50%, and 75%. The method was then applied to enhance the incomplete datasets. The results indicate that the proposed method successfully reduced the average positioning error to 0.202 meters for the 75% reduced radio map, 0.048 meters for the 50% reduced radio map, and 0.116 meters for the 25% reduced radio map.
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Time Reduction for Collecting Fingerprint Data in Indoor Positioning Systems with Generated Synthetic Data by Ensemble Models and GANs
(2024-01-01)
Wongsekleo, Prab
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Nakpaen, Lapat
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Cherntanomwong, Panarat
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Pattiyanon, Charnon
Nowadays, the demand for IPS is growing due to the increasing need for accurate indoor location services in applications. The IPS fingerprint techniques are widely popular because they offer high accuracy. However, the process of collecting fingerprint data is labor-intensive and time-consuming. This study aims to alleviate the burden of data collection by generating synthetic data using Machine Learning (ML) and Generative Adversarial Networks (GANs). To create ML synthetic data, we used a dataset containing RSSI values and coordinates. Various regression models were trained using Randomized Search for hyperparameter tuning. The best models were then combined into an ensemble method using Voting Regressor. This ensemble model was used to predict RSSI values for new, synthetic coordinates generated around each reference point, forming the synthetic dataset. We combined synthetic data with actual data from the IPS fingerprint RSSI collecting from the mobile application to create three new datasets with varying ratios of actual to synthetic data from 90:10 to 10:90. These combined datasets were used to train models including Random Forest, Decision Tree, Linear Regression, Gradient Boosting, and K Nearest Neighbors. Our results indicate that models trained on combined datasets significantly reduce the mean distance error (MDE) compared to those trained solely on actual data. This improved performance, however, comes with trade-offs in terms of slightly increased training time, prediction time, and memory usage during both training and prediction phases.
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Building RSSI-based Indoor Positioning Fingerprint Maps using Android-based Coordination
(2024-01-01)
Nakpaen, Lapat
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Wongsekleo, Prab
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Cherntanomwong, Panarat
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Pattiyanon, Charnon
Indoor positioning systems (IPS) have emerged as a critical technology for location-based applications. Developing IPS system is challenging since technologies for outdoor positioning seem to be limited in indoor environment. Fingerprinting is a technique to build an offline map and compare the current location with it. While fingerprinting remains a popular technique for indoor positioning, its reliance on extensive manual data collection is a significant challenge. These data points can be the Received Signal Strength Indicator (RSSI) of the Wi-Fi signal or signals from the triangulation of Bluetooth/cellular beacons. However, the conventional grid-based fingerprint technique is facing challenges when the target area is being large. This research proposes an automated approach to gathering Wi-Fi RSSI data for building indoor positioning maps using the Android-based triangulated coordination. Our method demonstrates a substantial reduction in data collection time (79%) compared to traditional grid-based techniques. The resulting dataset effectively supports machine learning models for indoor positioning, achieving a Mean Distance Error (MDE) of less than 2 meters different.
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Implementation of Wi-Fi-based indoor positioning system: Challenges and future possibilities
(2023-11-20)
Suroso, Dwi Joko
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Adiyatma, Farid Yuli Martin
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Cherntanomwong, Panarat
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Sooraksa, Pitikhate
Radiofrequency (RF)-based technologies are utilized as the core of indoor positioning system (IPS), i.e., Wi-Fi, Bluetooth, radio frequency identification (RFID), ultrawideband (UWB), and ZigBee standard. Wi-Fi is the most promising technology because of its wide availability on any smart or personal device and comes at a relatively low cost. Considering Wi-Fi technology as the core of IPS, there are several challenges related to positioning performance in its research and implementation. Nowadays, most real applications and implementations of Wi-Fi-based indoor positioning use the received power signal, the received signal strength indicator (RSSI). The position and time accuracy can determine the IPS system's performance. In the actual implementation, we must consider the specific position at that specific time (real-time system). From some references on IPS systems, most research is likely divided into two types; the algorithm development is somehow not a real-time implementation and proposal to implement in an actual application. This paper aims to study the real implementation of Wi-Fi-based IPS development, its recent advances, methods, performance requirements, challenges, and limitations, and, finally, offer the possibility for the prospective IPS research topics. Some future possibilities include the signal optimization algorithm, the new method with artificial intelligence, machine, or deep learning for Wi-Fi-based device-free IPS.
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3D range-based indoor localization by using only two beacons
(2023-05-22)
Suroso, Dwi Joko
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Krisnawan, Aditya Bagus
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Sooraksa, Pitikhate
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Cherntanomwong, Panarat
Indoor localization has been active research in the past decade. The lack of a general model and the demand for high accuracy push the research forward. Some researchers have proposed unique variations and combinations to answer the challenges from the technologies to techniques. Some publications considered the low-cost equipment to the simple algorithm or model to the advanced, robust, and sophisticated system. Almost all of them use the two basics technique; range-based and range-free techniques. Each technique has advantages and disadvantages, including how the technique can efficiently handle the indoor multipath propagation effects. Most of the papers published are also considered the same room or single room indoor environment as the indoor localization system measurement campaign. This paper proposes a three-dimensional (3D) indoor localization for multi-story buildings using simple technology and technique and using only two beacons. We utilized the received signal strength indicator (RSSI) from ESP8266 based on the Wireless-Fidelity (Wi-Fi) standard as the localization parameter. We employ the range-based technique of min-max and least-square. We also compare both techniques to observe which one is suitable for the multi-story building implementation. We also emphasized the challenge of using only two beacons as our contribution. Our system performance results show that the mean error accuracy for min-max is 1.93m, while the least-square yields the mean error of 5.48m. The minimum error of min-max and least-square are 0.33m and 0.77m, respectively. The maximum error of least-square can reach more than 10m, while the min-max gives the maximum error of 4.38m. The results prove that RSSI-based min-max can be applied in a particular condition using only two beacons in the multi-story building.
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Regression-based Path Loss Model Correction to Construct Fingerprint Database for Indoor Localization
(2023-03-24)
Adiyatma, Farid Yuli Martin
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Suroso, Dwi Joko
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Cherntanomwong, Panarat
The fingerprint-based indoor localization has been widely used due to its simple hardware setup and high positioning accuracy, especially using Received Signal Strength Indicator (RSSI). However, the fingerprint database has main drawbacks in database construction, requiring a lot of effort and time. This paper presents an approach for reducing the effort of manual fingerprint database construction for indoor localization using path loss model enhancement via simple regression, i.e., Linear and Polynomial Regression for RSSI-based fingerprint technique. We used the public dataset to evaluate our proposal, which was collected in a small room with low interference using three wireless technologies (Wi-Fi, ZigBee, and Bluetooth Low Energy). The K-nearest neighbors (KNN) is applied to locate the target. We compared the results from the original path loss model (O-PLM), the linear regression-path loss model (LR-PLM), and the polynomial regression path loss model (PR-PLM) with the actual RSSI values to validate our approach. The results showed that the Original Path Loss Model database and the Polynomial Regression Path Loss Model database improved the localization accuracy for Wi-Fi devices. The Linear Regression Path Loss Model can perform well in the ZigBee device case.

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