Fuzzy decision trees embedded with evolutionary fuzzy clustering for locating users using wireless signal strength in an indoor environment. Issue 8 (16th May 2021)

• Record Type: Journal Article
• Authors: Narayanan, Swathi Jamjala; Baby, Cyril Joe; Perumal, Boominathan; Bhatt, Rajen B.; Cheng, Xiaochun; Ghalib, Muhammad Rukunuddin; Shankar, Achyut
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 8(2021)
• Page Start: 4280
• Page End: 4297
• Publication Date: 2021-05-16
• Subjects: ant colony optimization -- artificial bee colony -- fuzzy decision tree -- particle swarm optimization -- user localization -- wireless signal strength; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22459
• Languages: English
• ISSNs: 0884-8173

Abstract

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.