@article{MAKHILLRJAS201813910121,
    title = {Indoor Wireless LAN Fingerprints Parameterisation and
Classification in Academic Environments},
    journal = {Research Journal of Applied Sciences},
    volume = {13},
    number = {9},
    pages = {499-512},
    year = {2018},
    issn = {1815-932x},
    doi = {rjasci.2018.499.512},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2018.499.512},
    author = {Moses E.},
    keywords = {Fingerprint,indoor localisation,pattern classification,principal component analysis,received
signal strength,machine learning},
    abstract = {In this study, we explore Machine Learning (ML) techniques to indoor Wireless Local Area Network
(WLAN) Fingerprints (FPs) parameterisation and classification in academic environments. First, relevant indoor
location (received signal strength indication and site specific) features were abstracted from the proposed area
of study (University of Uyo, Nigeria) in a previous research to serve as fingerprints to the current research.
Second, an unsupervised principal component analysis methodology was employed to produce Principal
Component Dominant Features (PCDFs) for the first three principal components (components with eigenvalues
of at least unity). These components revealed the degree of variances exhibited by the selected FPs. Third,
using three ML classifiers (Support Vector Machine: SVM, k-Nearest Neighbour: k-NN, decision tree and
Adaptive Neuro-Fuzzy Inference System: ANFIS) a classification of the PCDFs was performed. Results obtained
showed that decision tree and linear SVM classifiers were excellent at predicting large datasets an important
precursor to accommodating scalability in WLAN environments and areas with localisation challenges such
as difficult terrains, heavy interference and spatial or uneven distribution of wireless infrastructure as these
classifiers maintained high classification accuracies of above 90%. For small datasets, ANFIS gave good
classification accuracy when compared with other classifiers.}
    }