@article{MAKHILLJEAS2017121614735,
    title = {Enhancing Wi-Fi based Indoor Positioning using Fingerprinting Methods by
Implementing Neural Networks Algorithm in Real Environment},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {16},
    pages = {4144-4149},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.4144.4149},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.4144.4149},
    author = {Samaneh,Salwani,NurulIman and},
    keywords = {Indoor positioning,fingerprinting,Wi-Fi,neural network,global positioning system,implemented},
    abstract = {Global positioning systems have difficulties in finding positions inside buildings, since indoor
positioning needs additional indoor infrastructures deployment. In this research, indoor positioning by using
Wi-Fi access point is investigated as the main usage of Location Based Service (LBS) applications. We
employed fingerprinting method to increase the accuracy of positioning. The study has been done in real
environment in Universiti Teknologi Malaysia (UTM). Two models were designed by using Neural Network
algorithm for indoor positioning. The fingerprinting dataset contains received signal strength from different
numbers of existing Wi-Fi access points in the real environment. Accuracy rate and mean square error were
calculated for the algorithm. Evaluations of models have been done by conducting experiments to compare both
models. Analysis suggests that Neural Network method which achieved 71% of accuracy with number of
neurons = 11 is the most precise model for indoor positioning in this project. In future, more features can be
applied to this model in order to increase the accuracy. This approach has the potential to be implemented as
a real mobile application for indoor environment.}
    }