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    Hierarchical KNN for Smartphone-Based 3D Indoor Positioning
    (2024-01-01)
    Adiyatma, Farid Yuli Martin
    ;
    Sunimit, Samita
    ;
    Chokporntaveesuk, Thanwa
    ;
    Lualum, Krittima
    ;
    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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    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.
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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
    ;
    Suroso, Dwi Joko
    ;
    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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    An experimental study of wi-fi access service using drones in container yard
    (2020-10-13)
    Meesriyong, Krongpon
    ;
    Wongwirat, Olarn
    ;
    Namuduri, Kamesh
    Currently, there are some restrictions on employees to access the network in the area of the container yard. This is not only because the operating computers are installed at the positions that are quite remote from the working areas in the container yard but also there is no wireless access network, or Wi-Fi, provided. Installing a fixed tower to transmit a radio signal providing the Wi-Fi access service is also not applicable in the container yard since the layout of placing containers is often changed periodically. Furthermore, the containers are made by metal and often stacked over that causes blocking of the radio signal resulting in a dead zone occurred in several spots in the area. Therefore, the work in this paper presents an experimental study for assessing feasibility to provide the Wi-Fi access service by using drones in the container yard. A business analysis, site survey, and prototype design are performed in the study. Then, the received signal strength, coverage area, and support data rate are defined as parameters to be measured in accordance with the drone altitudes in the experiments. Finally, the results verification and analysis are conducted for affirming the feasibility to use drones for providing the Wi-Fi access service in the container yard in the future.
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    Mitigation technique for LTE-LAA and LTE-LWA coexistence
    (2020-07-01)
    A-Mapat, Narongchon
    ;
    Moungnoul, Phichet
    An implementation of Mobile network providers on 5G network are coexistence between LTE-LAA and LTE-LWA network. It was applied to achieve the objective of improve network performance and optimize resources. This paper is study the investigated LTE-LAA and LTE-LWA coexistence with Wi-Fi controlled Technique in different scenarios. The results show the distance between 50-600 meter and LTE-LWA was extremely affect by LTE-LAA. The controlled Wi-Fi AP technique is effective method to mitigate the network degradation problem.
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    Throughtput investigated of coexistence Wi-Fi and LTE-U
    (2019-11-01)
    Moungnoul, Phichet
    ;
    A-Mapat, Narongchon
    Implementation of the mobile network provider to improve the network performance and the resources optimization by implemented coexistence LTE-TDD, LTE-FDD network and Wi-Fi technology. The main interference is the adjacent cell interference (ACI) from coexisting network/technology, which affected to the network performance. This paper is investigated LTE/Wi-Fi technique in coexistence LTE-FDD/TDD. Results show the distance between 50-60 m and Wi-Fi access point should not over 12 given the best system. The throughput compared with normal case will describe in this paper.