Cherntanomwong, Panarat
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Cherntanomwong, Panarat
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panarat.ch@kmitl.ac.th
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Item type:Publication, Deep Generative Model-based RSSI Synthesis for Indoor Localization(2022-01-01); Indoor localization via deep learning (DL) is attracting researchers' attention. DL is mainly used for fingerprinting-based indoor localization as it generally employs a vast offline database to ensure its reliability. However, the long effort and high cost of constructing this database are the disadvantages of this technique. This paper implements variational autoencoders (VAE), one of the popular deep generative models, to alleviate the drawbacks of offline database issues. Our proposal works using the received signal strength indicator (RSSI); unfortunately, it is known for its fluctuation and instability. Thus, instead of using RSSI directly as a localization parameter, we learn its distribution via VAE to generate the synthetic RSSI values. We utilized the RSSI from an actual measurement campaign. The VAE implementation results show that we can obtain the RSSI synthesis by exploring the latent distribution learned from the input distribution. Thus, the offline database density grids can be enhanced. We validated the results by varying epochs to map the learned latent distribution. However, we still have relatively low accuracy in the synthetic RSSI values, especially when applying a small number of epochs, i.e., 10 and 100. When we applied epoch number 1000, the error was relatively low (-3dBm average error) in the sampled position. Our preliminary assumption is that the dataset is small for VAE learning, and probably the 3-by-3 RSSI-to-image size assumption could still be inadequate. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Radio Map Augmentation Using DBSCAN and KNN Regression for Improved Indoor Positioning(2024-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 3D range-based indoor localization by using only two beacons(2023-05-22); 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fingerprint Database Enhancement using Spatial Interpolation for IoT-based Indoor Localization(2022-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, U-GMo: Individual Clip Detection from a Graduation Ceremony Video(2024-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Synthesis of a Small Fingerprint Database through a Deep Generative Model for Indoor Localisation(2023-01-01); In deep learning (DL), the deep generative model is helpful for data augmentation objectives to tackle the lack of datasets that have a significant impact on learning performance. Data augmentation or synthesis is expected to solve the issue in a small/sparse database. The problem of databasing also exists in the fingerprint-based indoor localisation system. The dense offline fingerprint database must be constructed with the accuracy requirement. However, this will affect the high cost, massive laborious work, and increase the complexity of the system. Therefore, this paper proposes to address these issues by generating synthetic data via a deep generative model. The generative adversarial network (GAN) is selected to generate the synthetic fingerprint database for indoor localisation. Our database consideration consists of power-based parameters, i.e., the received signal strength indicator (RSSI) from Wi-Fi devices obtained from the actual measurement campaign. Some of the literature mainly discusses how GAN works in a vast and complex dataset. Here, we consider applying GAN in a relatively small dataset and for a simple setup. Our results show that by only using the 20 % fraction of actual RSSI data combined with the synthetic RSSI, the accuracy validation performance is slightly higher than when using all actual data usage. Moreover, in only 60 % of actual data usage and in combination with 625 samples of synthetic data, the accuracy performance is improved to 0.73 (1.37 times higher than the use of all actual data, 0.53). Thus, this result proves that the challenges of offline fingerprint databases can be alleviated by data synthesis through GAN by using only a small dataset. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Building RSSI-based Indoor Positioning Fingerprint Maps using Android-based Coordination(2024-01-01); Pattiyanon, CharnonIndoor 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Time Reduction for Collecting Fingerprint Data in Indoor Positioning Systems with Generated Synthetic Data by Ensemble Models and GANs(2024-01-01); Pattiyanon, CharnonNowadays, 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Efficient Radio Map Construction Method Using Region-Aware Adaptive Ensemble Regression for Indoor Localization(2025-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Distance-based indoor localization system utilizing general path loss model and RSSI(2020-11-01)