KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Subject: search.filters.subject.fingerprint technique

Search Results

Now showing 1 - 6 of 6
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Enhancing Wi-Fi-based Fingerprint Technique for Indoor Positioning System
    (2024-01-01)
    Nimnaul, Tanapol
    ;
    Bureetes, Natchapong
    ;
    Siriwat, Siwat
    ;
    Wongwirat, Olarn
    This paper addresses the enhancement of the Wi-Fi-based fingerprint technique for an indoor positioning system applied in an experimental area. The conventional Wi-Fi-based fingerprint technique utilizes a k-nearest neighbor (k-NN) algorithm for position estimation. The k-NN algorithm is a simple and intuitive classification algorithm based on distance metric, i.e., Euclidean distance (ED), but often demonstrates limited accuracy. To mitigate this constraint and enhance positioning precision, advanced machine learning algorithms in artificial neural networks (ANNs) have been introduced. Although ANN algorithms are considered highly reliable, they are complex and resource-intensive algorithms, resulting in less suitable for a small-scale area that requires simple indoor positioning applications. In contrast, the random forest (RF) algorithm offers comparable positioning accuracy while being more computationally efficient, making it a favorable choice for such scenarios. The work in this paper enhances the accuracy of the Wi-Fi-based fingerprint technique for indoor positioning systems by adopting the RF algorithm over the k-NN alternative for position estimation accuracy. The number of received signal strength (RSS) data selected from appropriate access points (APs) in the area chosen by a feature selection method is a pivotal factor influencing accuracy improvements. The experimental results express the direct correlation between increased RSS data and accuracy improvement for both algorithms. Significantly, the application of the feature selection method using the information gain ratio augments the positioning accuracy specifically for the RF algorithm.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Robustness of 3D indoor localization based on fingerprint technique in wireless sensor networks
    (2013-09-02)
    Chuenurajit, Thanapong
    ;
    Phimmasean, Sisongkham
    ;
    Cherntanomwong, Panarat
    The simple approaches of an indoor localization have been continuously developed and extensively published. In order to achieve a challenge of the effective indoor localization, the expected localization system should be a high efficiency such as high accuracy, simple, robustness and effective system. For the indoor localization, the researches have been supported by the advancement of the sensor node technology. One application of this technology is wireless sensor networks-based indoor localization. The simple approaches of 2-dimensional (2D) indoor localization have been widely proposed. Since a realistic system includes complicated terrain and different environment, 3-dimensional (3D) consideration is more suitable to be applied in the real life. This paper proposes an approach of 3D indoor localization based on ZigBee standard. Fingerprint technique-based received signal strength indicator (RSSI) is employed. Due to fluctuating signals in indoor environment, robustness of fingerprint technique will be proposed in order to solve the propagation mechanisms. The k-Nearest Neighbor using Euclidean distance is utilized as the pattern matching algorithm. For the case study, 8 reference nodes and 1 target node are stationary placed on a bookshelf in clean and human body's effect environments. The expected errors of estimated target locations should not be more than 36 cm (each level height). From the results, we can acquire an acceptable accuracy. It shows that our system can be applied in the real application. © 2013 IEEE.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Location fingerprint technique using Fuzzy C-Means clustering algorithm for indoor localization
    (2011-12-01)
    Suroso, Dwi Joko
    ;
    Cherntanomwong, Panarat
    ;
    Sooraksa, Pitikhate
    ;
    Takada, Jun Ichi
    The recent researches in localization technique have been supported by the emerging of wireless sensor network (WSN) technology. The issues of power and time consumption have become the main research topics in WSN-based localization technique. ZigBee as IEEE 802.15.4 is commonly used as supporting device because of its advantages for low-power, small and smart sensor nodes. This paper proposes the new technique in radio frequency (RF) fingerprint technique-based localization using Fuzzy C-Means (FCM) clustering algorithm. This technique provides an efficient localization system that gives benefit in the time-efficient and low power consumption. In this paper, received signal strength indicator (RSSI) is used as the fingerprint information which indicates the location of sensor nodes. The different amount of the reference nodes is applied. The effectiveness of this method is verified by an indoor experiment. The estimated location results from different sets of reference nodes are compared. The time consumption in experiment is compared with those using the common fingerprint technique. © 2011 IEEE.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Fingerprint-based technique for indoor localization in wireless sensor networks using Fuzzy C-Means clustering algorithm
    (2011-12-01)
    Suroso, Dwi Joko
    ;
    Cherntanomwong, Panarat
    ;
    Sooraksa, Pitikhate
    ;
    Takada, Jun Ichi
    ZigBee as IEEE 802.15.4 standard has been using for main research topic in the wireless sensor network (WSN) applications. The new method of radio frequency (RF) fingerprint-based technique for indoor localization is proposed. The received signal strength indicator (RSSI) is used as database values which correspond to the location of the sensor nodes. Fuzzy C-Means (FCM) clustering algorithm is applied as the experiment data cluster method. FCM algorithm is deployed to cluster the obtained feature vectors into several classes corresponding to the different amount of RSSI values. The results show that FCM can cluster the target node in a group of the fingerprint database. The location of target node is arranged in various forms to validate the accuracy of the clustering technique. Euclidean distance is used as the parameter to compare the similarity between fingerprint database and the target location. The results show that the new method is simple and effective method to reduce the complexity and to support the low power and to reduce the time using in the fingerprint-based localization technique. © 2011 IEEE.