Fuzzy decision trees embedded with evolutionary fuzzy clustering for locating users using wireless signal strength in an indoor environment

Abstract

Location estimation is one of the critical requirement for developing smart environment products. Due to huge utilization and accessibility of WiFi infrastructure facility in indoor environments, researchers widely studied this technology to locate users accurately to provide several services instantly. In this research work, a hybrid algorithm namely fuzzy decision tree (FDT) with evolutionary fuzzy clustering methods is adopted for optimal user localization in a closed environment. Here we consider the wireless signal strengths received from the smart phones as predictors and the location of the user as the classification label. The required data for the current research is collected from the physical facility available at an office location in USA. The classification results obtained are promising enough to show that the evolutionary clustering approaches provide good fuzzy clusters for FDT induction with better accuracy.

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

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New energy grid connection power control method based on predictive tuning performance and embedded system

“…In the design of system software, it is necessary to use programming language for programming, and the system program includes the acquisition of power grid signals, data processing, and output of corresponding sine wave data (Narayanan et al, 2021). After the system is started, it is initialized and then checked to see if the power grid is normal.…”