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, 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, 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, Distance-based indoor localization system utilizing general path loss model and RSSI(2020-11-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Regression-based Path Loss Model Correction to Construct Fingerprint Database for Indoor Localization(2023-03-24) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, TLB and WC-TLB-MM: The Improved Min-Max Algorithms for Multi Targets Indoor Localization(2023-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization(2020-01-01); 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Implementation of Wi-Fi-based indoor positioning system: Challenges and future possibilities(2023-11-20); 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Is Deep Diffusion Probabilistic Model Applicable for Fingerprint-based Indoor Localization?(2022-01-01); The latest deep learning (DL) phenomenon is the Denoising Diffusion Model (DDM). DDM is in a class of latent variable models of the deep generative model (DGM) along with the big name of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Moreover, in a recent finding, DDM beats GANs in image synthesis. This paper presents the prospective applicability discussion of DDM for indoor localization research as previous models, e.g., GANs and VAEs, which are successfully implemented. Here, we focus more on how DDM can synthesize localization parameters with the help of fingerprinting technique's database enhancement. The fingerprint technique needs a preconstructed database which has the main drawbacks of its cost, time inefficient, and high complexity. We found valuable works of literature on this specific topic for GANs and VAEs. However, there are few DDM applications for discrete data types, and as the authors' concern, there is no attempt to apply them to indoor localization yet. DDM implementation is to generate continuous data domains, e.g., image, text, and audio data. A radio map or fingerprint database is essentially needed for fingerprint-based indoor localization. Learning this database pattern helps increase the system's performance. Obtaining a high-density and quality database is expensive and challenging to implement. Then, it raises a question, is DDM applicable for synthesizing this database and alleviating this problem? - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Indoor Device-free Localization Using Received Signal Strength Indicator and Illuminance Sensor for Random-forest-based Fingerprint Technique(2021-01-01); Indoor device-free localization (IDFL) offers more flexibility than conventional indoor localization (device-based) systems, as the targets or objects need not be equipped with any device to be located. In the process of IDFL, the target is passive, enabling applications such as monitoring of elderly people, security systems to detect intruders, and indoor navigation. Despite having more flexibility than device-based systems, IDFL is still inferior in terms of localization performance. The most commonly used technique for IDFL is the fingerprint technique, which uses the uniqueness of spatial information to predict the target's location. The spatial information is a fingerprint database containing information on locations and their corresponding parameters. The most specific parameter for the fingerprint database is the received signal strength indicator (RSSI). RSSI can be obtained directly from many low-cost devices, i.e., Wi-Fi-based devices, without the need to install additional hardware. The fingerprint technique is a two-phase process: The database is constructed in the offline phase, and a matching process to compare the target's current parameter with those in the database is performed in the online phase. We propose fingerprint-technique-based IDFL using RSSI and illumination from an illuminance sensor as the additional parameters of the fingerprint database. Both parameters are recorded by considering two scenarios: An empty room and a person standing in the fingerprint grids. The constructed database is the person-filled room subtracted from the empty room database. We use random forest, one of the machine learning (ML) algorithms, as the pattern-matching algorithm. We evaluate its performance by comparison with two other ML algorithms: K-nearest neighbor (k-NN) and neural networks (NN). The results show that k-NN has better accuracy than the random forest for learning and testing in terms of the root mean square error (RMSE). On the other hand, the random forest has better accuracy than NN and better precision than either k-NN or NN for learning and testing in terms of the standard deviation (STD). The results show the possibility of improving the IDFL performance by adding more parameters to the fingerprint database and using an ML-based pattern-matching algorithm.