A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building.
Soumya Prakash Rana, Javier Prieto, Maitreyee Dey, Sandra Dudley, Juan Manuel Corchado
Author Information
Soumya Prakash Rana: Division of Electrical and Electronic Engineering, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK. ranas9@lsbu.ac.uk.
Javier Prieto: BISITE Research Group, University of Salamanca, Edificio I+D+I, C/ Espejo s/n, 37007 Salamanca, Spain. javierp@usal.es. ORCID
Maitreyee Dey: Division of Electrical and Electronic Engineering, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK. deym@lsbu.ac.uk.
Sandra Dudley: Division of Electrical and Electronic Engineering, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK. dudleyms@lsbu.ac.uk.
Juan Manuel Corchado: BISITE Research Group, University of Salamanca, Edificio I+D+I, C/ Espejo s/n, 37007 Salamanca, Spain. corchado@usal.es. ORCID
Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.
