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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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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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C-MEL: Consensus-Based Multiple Ensemble Learning for Indoor Device-Free Localization Through Fingerprinting
(2024-01-01)
Suroso, Dwi Joko
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Adiyatma, Farid Yuli Martin
The rise of location-aware services is enhancing the effectiveness of our daily tasks, especially within indoor environments where most activities take place. Wireless indoor localization systems are the predominant method for estimating locations indoors. These systems utilize two primary approaches: device-based and device-free. Device-based techniques are attracting considerable research attention due to their ability to offer highly accurate localization in most scenarios. Conversely, device-free techniques are increasingly popular because they can determine a target's location without the target carrying a device. This capability makes them suitable for certain applications such as elderly monitoring and intruder tracking. The most popular technique for both approaches is fingerprinting, which uses the uniqueness of spatial information to predict a target's location. This spatial information is stored in a fingerprint database, containing locations and their associated parameters. However, in device-free methods, the fingerprint technique encounters challenges in accurately recognizing the complexity of each parameter combination pattern, thus impacting the accuracy of the estimation. To overcome this issue, we introduce a novel indoor device-free localization (IDFL) pattern matching algorithm named Consensus-based Multiple Ensemble Learning (C-MEL). This algorithm incorporates consensus strategies, i.e., majority voting and average strategy, to integrate outputs from various ensemble learning algorithms, such as Random Forest, Gradient Boosting, XGBoost, and LightGBM. We validate our algorithm in an 18 m2 office space featuring stainless steel partitions, tables, chairs, and cabinets. Experimental results show that C-MEL using the average strategy (C-MEL-AV) enhances accuracy by up to 44.51%, 11.26%, and 37.85%, while C-MEL with majority voting (C-MEL-MV) improves by up to 40.56%, 4.95%, and 33.44% compared to Decision Tree, Gradient Boosting, and 1D CNN-BLSTM, respectively. Based on these results, C-MEL-AV emerges as a reliable approach for accurate IDFL based on the fingerprint technique, while C-MEL-MV remains a viable alternative for IDFL systems.
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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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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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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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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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Machine Learning-Based Multi-Room Indoor Localization Using Fingerprint Technique
(2023-01-01)
Adiyatma, Farid Yuli Martin
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Suroso, Dwi Joko
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Cherntanomwong, Panarat
Nowadays, developing Wi-Fi-based indoor localization systems has become an attractive research topic due to the growing need for pervasive location determination. The fingerprint technique offers higher positioning accuracy in indoor localization than the distance-based technique. Fingerprint-based techniques via machine learning have been proposed for many years to provide high-accuracy indoor localization services. These works attempt to establish the optimal correlation between the user fingerprint and a pre-defined set of grid points on a radio map. In this paper, a comparative analysis of selected machine learning algorithms is conducted within the context of online phase fingerprint techniques for localization, focusing on implementation in a multi-room case. The experiment involves measurements using a Wi-Fi module in a laboratory, an aisle, a lobby, and a typical classroom, resulting in a small-sized fingerprint database covering a total area of 573.71 m2. The results reveal that Naïve Bayes (NB) obtains the highest localization accuracy in the laboratory and classroom. Meanwhile, Support Vector Machine (SVM) outperforms other algorithms in the aisle, while K-Nearest Neighbor (KNN) delivers the best accuracy in the lobby. In summary, NB, KNN, and SVM are suitable pattern-matching algorithms for multi-room indoor localization and relatively small fingerprint databases.
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Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization
(2020-01-01)
Suroso, Dwi Joko
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Adiyatma, Farid Yuli Martin
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Cherntanomwong, Panarat
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Sooraksa, Pitikhate
Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden.

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