Machine-Learning Indoor Localization with Access Point Selection and Signal Strength Reconstruction

Cheng, Yen-Kai; Chou, Hsin-Jui; Chang, Ronald Y.

doi:10.1109/vtcspring.2016.7504333

Cited by 35 publications

(33 citation statements)

References 16 publications

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“…Authors of [4] used 1.8 m × 1.8 m grids to collect required data. In [5], authors used 2.5 m × 2.5 m grids. In [6], 1 m × 1 m grids were used for data collection, and accurate results were found.…”

Section: Experimental Data Acquisitionmentioning

confidence: 99%

“…In indoor environments; Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), and Galileo are not practical because they lack line of sight (LoS) between the satellites and the receivers, which is easily affected by the physical layout of equipment and is sensitive to occlusion [3][4][5]. Therefore, indoor localization becomes common in indoor environments to offer convenient services.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, indoor localization becomes common in indoor environments to offer convenient services. According to [2,5], one of the enabling key factors for the future IoT archetype is applying accurate indoor localization. As pointed out in [6], indoor localization can be applied for robotics, ambient assisted living, health applications, location-based services (LBS), and to address challenges in 5G networks.…”

Section: Introductionmentioning

confidence: 99%

“…To alleviate localization problems of fingerprinting approaches, machine-learning methods were proposed [1,5,8,11,12]. In [1], a radial basis function network was used to estimate location, and a particle filter was used to track the user's path in an indoor environment.…”

Section: Introductionmentioning

confidence: 99%

“…RFID and IR were used as the source of data from sensors, which creates a high cost as it requires additional hardware and requires offensive calibration processes. In [5], support vector regression (SVR) and an artificial neural network (ANN) were used for indoor localization. The data was generated by propagation model in 2 GHz band.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

et al. 2018

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Featured Application: This work can be applied to track mobile users, manage indoor navigations, provide alarms in secured areas, such as unacceptable hospital areas, military systems and mass rapid transit (MRT) inside enclosed areas. In general, this work is applicable to inside enclosed areas where the specific location is mandatory.Abstract: In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.

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Section: Experimental Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%