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.
Highlights
Human daily life is becoming highly integrated with the Internet of Things (IoT), as the Internet attracts much attention with respect to the outlook of future life and rapidly increasing communication networks
In deep neural network (DNN), we focused on classification- and regression-based indoor localization schemes using real data collected from three buildings
We conduct simulations in both classification and regression schemes to analyze the performance of the proposed algorithm
Summary
Human daily life is becoming highly integrated with the Internet of Things (IoT), as the Internet attracts much attention with respect to the outlook of future life and rapidly increasing communication networks. Range-based localization uses the measured distance or angle to estimate the location Under these schemes, received signal strength (RSS), time-of-arrival (ToA), time-difference-of-arrival (TDoA), or angle-of-arrival (AoA) are common approaches. In [11], authors used an ANN algorithm for indoor localization using ToA and AoA as data sources This type of approach has operational complexity and is difficult to accurately localize because of lacks of LoS. In DNN, we focused on classification- and regression-based indoor localization schemes using real data collected from three buildings In this particular work, we used the multilayer perceptron (MLP). We practice the parallel implementation of classification and regression schemes in Graphical Processing Unit (GPU), which helps to improve performance rates in Wi-Fi environments This helps to provide both bounded and specific positioning of Wi-Fi users in hierarchical and complex environments at the same instance.
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