Indoor Positioning Systems Based on Li-Fi Technology Using RSS-Triangulation With Assisted by DNN
Authors: Kawther Dawood Salman, Ekhlas Kadhum Hamza
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Due to the rapid growth in mobile data traffic, academia and industry are considering Li-Fi (Light Fidelity) as an alternative to Wi-Fi (Wireless Fidelity). Li-Fi is a high-speed, two-way wireless network system that has several advantages over traditional radio frequency systems. To fully exploit the potential of Li-Fi systems, users must be provided with a seamless connection, which must be achieved by carefully studying user mobility. Nowadays, most wireless Li-Fi network applications focus their design on solving some unspecific user-location problems that may fully achieve some preferred results in the domain they serve. For example, locating an unknown user in an indoor environment is considered a kind of solution for maintaining the QoS (Quality of Service) and delivering the signal to the user without distraction. Where the main research problem lies in the signal interference, loss, and scattering in the Li-Fi network, it is a big problem because it reduces the network performance and coverage. Therefore, to solve this problem, this research investigates the locating of the mobile user within the Li-Fi equipped room to obtain the best possible coverage of the user's service. The aim of the research was divided into two stages. In the first stage, the method used in our work was presented by showing the triangulation method in simulating user location data (sensor simulation), the first stage was not affected by noise, as it serves as proof of the correctness of our work and depends in the second stage when it is under the influence the noise. In the second stage, the method of RSS - triangulation (Receiving Signal Strength -Triangulation) was adopted to simulate the user’s location data (a simulation of the sensor) and under the influence of noise and implemented on a deep learning algorithm to determine the accuracy of the user’s location and how to deal with noise. In the simulation results for the data in the first stage, our work was validated using the triangulation method in data simulation. The average error for the X-Axis was 2.17344 〖× 10〗^(-14) cm; Y-Axis was 6.44762 〖× 10〗^(-14) cm and Z-Axis 4.65690 〖× 10〗^(-11) cm the results obtained from the triangulation simulation were close to the real results. In the second stage, when adopting the RSS-Triangulation method under effect the noise, the average error was achieved on the X-axis 2.05612 〖×10〗^(-3 )cm, Y-Axis was 4.56249〖 ×10〗^(-3 )cm, and Z-Axis 5.10474 〖×10〗^(-3 )cm where The DNN (Deep Neural Network) showed how to deal with noise, the highest error was obtained on the X-axis 2.51535 cm, the Y-axis 2.25903 cm, and the Z-axis 4.22898 cm for an indoor environment of 5m x 5m x 3m.