An adaptive learning fuzzy logic system for indoor localisation using Wi-Fi in Ambient Intelligent Environments

Abstract

One of the important requirements for Ambient Intelligent Environments (AIEs) is the ability to localise the whereabouts of the user in the AIE to address her/his needs. The outdoor localisation means (like GPS systems) cannot be used in indoor environments. The majority of non intrusive and non camera based indoor localisation systems require the installation of extra hardware such as ultra sound emitters/antennas, RFID antennas, etc. In this paper, we will propose a novel fuzzy logic based indoor localisation system which is based on the WiFi signals which are free to receive and they are available in abundance in the majority of domestic spaces. The proposed system receives WiFi signals from a big number of existing WiFi Access Points (up to 170 Access Points) with no prior knowledge of the access points locations and the environment. The proposed system is able to adapt online incrementally in a lifelong learning mode to deal with the uncertainties and changing conditions facing unknown indoor structures with a few days of calibration at zero-cost deployment with high accuracy. The proposed system was tested in simulated and real environments where the system has given high accuracy (that outperformed the existing techniques) to detect the user in the given AIE and the system was able also to adapt its behaviour to changes in the AIE or the WiFi signals.

A Survey of Knowledge Extraction Classification using Different Techniques

“…It can generate highly interpretable knowledge bases with a good accuracy as compared to that achieved by other methods. [10] In 2012 Teresa Garcia-Valverde, Alberto Garcia-Sola, Antonio Gomez-Skarmeta and Juan A. Botia, Hani Hagras, James Dooley and Victor Callaghan [11] invented a protocol in which system receives WiFi signals from a big number of existing WiFi Access Points with no prior knowledge of the access points locations and the environment. This scheme is able to adapt online incrementally in a lifelong learning mode to deal with the uncertainties and changing conditions and in simulated and real environments this system has given high accuracy to detect the user in the given AIE.…”

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

“…Further, Le and Nguyen 25 proposed scalable WiFi‐based localization approaches which handle various mobile devices in the deployment phase. Garcia et al 26 used fuzzy concepts and online incremental learning models to tackle the uncertainties and varying conditions.…”

A new fuzzy location indicator for Interval Type-2 indoor Fuzzy Localization System

“…Depends on the used localization methods [3]: based on Time of Arrival (ToA), Angle of Arrival (AoA), Radio Signal Strenght (RSS) or Time Difference of Arrival (TDoA), localization techniques merged from classical and probabilistic approaches [7] to Evolutionary computation (EC) using Neural networks (NN) [8], Support Vector Machines (SVM) [9], Fuzzy logic (FL) [10,11,12] and hybrid approaches.…”

“…Besides, the system is only adaptable to linear trajectories and not for the various kinds of fuzzy observer trajectories. Garcia-Valverde in [10] and [16] works to build an adaptive rule-based model to the system. Using T1 in RSS classification, the used technique is able to automatically learn offline and online to adapt in order to deal with the environmental changes.…”