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Author: search.filters.author.Cherntanomwong, PanaratSubject: search.filters.subject.indoor localization

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    Deep Generative Model-based RSSI Synthesis for Indoor Localization
    (2022-01-01)
    Suroso, Dwi Joko
    ;
    Cherntanomwong, Panarat
    ;
    Sooraksa, Pitikhate
    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.
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    Is Deep Diffusion Probabilistic Model Applicable for Fingerprint-based Indoor Localization?
    (2022-01-01)
    Suroso, Dwi Joko
    ;
    Sooraksa, Pitikhate
    ;
    Cherntanomwong, Panarat
    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?
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    Fingerprint Database Enhancement using Spatial Interpolation for IoT-based Indoor Localization
    (2022-01-01)
    Martin Adiyatma, Farid Yuli
    ;
    Joko Suroso, Dwi
    ;
    Cherntanomwong, Panarat
    The widespread adoption of the internet of things (IoT) drives indoor location-based service (ILBS) applications forward. The core parameter of ILBS is indoor localization. Generally, indoor localization is divided into two techniques, distance-based, i.e., triangulation, and distance-free, i.e., fingerprint technique. This paper discusses the fingerprint technique because of some advantages, i.e., higher accuracy performance compared to the distance-based technique. However, the fingerprint technique has drawbacks in offline database construction: extraordinarily time-consuming and labor-intensive, which hinders its application in the real world. Furthermore, the fingerprint database needs to be updated regularly in a dynamic environment. Therefore, we propose fingerprint database enhancement based on various spatial interpolations to tackle the issues of fingerprint database construction. We apply Inverse Distance Weighted (IDW), Quadratic Spline, Cubic Spline, and Ordinary Kriging Interpolation methods to generate the synthetic database. We have conducted a measurement campaign to obtain Received Signal Strength Indicator (RSSI) as the fingerprint-based localization parameter. From our results, the interpolation methods show that the generated synthetic RSSI can provide a lower prediction error. Our proposed methods can have similar accuracy performance compared to manual fingerprints using actual data. Moreover, the synthetic RSSI data has a 0 dBm error for the best prediction and not more than 6 dBm for the worst prediction. Thus, we conclude that our proposed methods can enhance the fingerprint database and have proven to increase localization performance.