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    Regression-based Path Loss Model Correction to Construct Fingerprint Database for Indoor Localization
    (2023-03-24)
    Adiyatma, Farid Yuli Martin
    ;
    Suroso, Dwi Joko
    ;
    Cherntanomwong, Panarat
    The fingerprint-based indoor localization has been widely used due to its simple hardware setup and high positioning accuracy, especially using Received Signal Strength Indicator (RSSI). However, the fingerprint database has main drawbacks in database construction, requiring a lot of effort and time. This paper presents an approach for reducing the effort of manual fingerprint database construction for indoor localization using path loss model enhancement via simple regression, i.e., Linear and Polynomial Regression for RSSI-based fingerprint technique. We used the public dataset to evaluate our proposal, which was collected in a small room with low interference using three wireless technologies (Wi-Fi, ZigBee, and Bluetooth Low Energy). The K-nearest neighbors (KNN) is applied to locate the target. We compared the results from the original path loss model (O-PLM), the linear regression-path loss model (LR-PLM), and the polynomial regression path loss model (PR-PLM) with the actual RSSI values to validate our approach. The results showed that the Original Path Loss Model database and the Polynomial Regression Path Loss Model database improved the localization accuracy for Wi-Fi devices. The Linear Regression Path Loss Model can perform well in the ZigBee device case.
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    Wi-Fi Received Signal Strength-based Indoor Localization System Using K-Nearest Neighbors fingerprint integrated D∗algorithm
    (2022-01-01)
    Jarawan, Tanatthep
    ;
    Kamsing, Patcharin
    ;
    Torteeka, Peerapong
    ;
    Manuthasna, Shariff
    ;
    Hematulin, Warunyu
    The indoor localization system is essential since the Global Positioning System cannot give an accurate position indoors, especially when several floor plans are considered. WiFi received signal strength becomes an alternative indicator for indoor localization systems. The experiment proposed a localization system created by integrating and working between the K-Nearest Neighbors algorithm and the D∗algorithm. The result illustrates the optimal path from the start point to the target point by avoiding the obstacle performing exceptionally well. The K-Nearest Neighbors algorithm provide the result for localization with Root Mean Square Errors of displacement at 1.190 meters, 2.491 meters, and 1.363 meters of X-Axis Y-Axis, respectively. The proposed indoor localization system can have various applications considering different environmental factors in different applications, such as the size of unmanned aerial vehicles when applying indoor unmanned aerial vehicles.
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    Wi-Fi Received Signal Strength-based Indoor Localization System Using K-Nearest Neighbors fingerprint integrated D*algorithm
    (2021-02-07)
    Jarawan, Tanatthep
    ;
    Kamsing, Patcharin
    ;
    Tortceka, Peerapong
    ;
    Manuthasna, Shariff
    ;
    Hematulin, Warunyu
    The indoor localization system is essential since the Global Positioning System cannot give an accurate position indoors, especially when several floor plans are considered. Wi-Fi received signal strength becomes an alternative indicator for indoor localization systems. The experiment proposed a localization system created by integrating and working between the K-Nearest Neighbors algorithm and the D*algorithm. The result illustrates the optimal path from the start point to the target point by avoiding the obstacle performing exceptionally well. The K-Nearest Neighbors algorithm provide the result for localization with Root Mean Square Errors of displacement at 1.190 meters, 2.491 meters, and 1.363 meters of X-Axis Y-Axis, respectively. The proposed indoor localization system can have various applications considering different environmental factors in different applications, such as the size of unmanned aerial vehicles when applying indoor unmanned aerial vehicles.