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Technology with situational awareness needs a lot of information of the environment to execute the correct task at the correct moment. Location of the user is typical information to achieve the goal. This work proposes a mobile application that enables the indoor location of smartphones using the potential infrastructure given by Wireless Local Area Networks. This infrastructure goes beyond GPS (Global Position System) where signal is weak or is not available for indoors. This application uses an alternative and unconventional method to indoor location using Wi-Fi RSSI fingerprinting as well as an estimation based on Type-2 fuzzy inference systems provided by the developed framework JT2FIS. Wi-Fi Fingerprinting creates a radio map of a given area based on the RSSI data from several access points (APs) and generates a set of RSSI data for a given zone location. Consequently Data Mining is required for clustering the obtained set of data and generating the structure of a Type-2 Mamdani or Takagi-Sugeno Fuzzy Inference System; thus new RSSI values are introduced to the Type-2 Fuzzy Inference System to obtain an estimation of the user zone location.

Hybrid-Intelligent Mobile Indoor Location Using Wi-Fi Signals - Location Method Using Data Mining Algorithms and Type-2 Fuzzy Logic Systems