Towards self-learning radio-based localization systems

Author

Alyafawi, Islam

Author_Institution

Inst. of Inf. & Appl. Math., Univ. of Bern, Bern, Switzerland

fYear

2012

fDate

19-23 March 2012

Firstpage

556

Lastpage

557

Abstract

Location-awareness indoors will be an inseparable feature of mobile services/applications in future wireless networks. Its current ubiquitous availability is still obstructed by technological challenges and privacy issues. We propose an innovative approach towards the concept of indoor positioning with main goal to develop a system that is self-learning and able to adapt to various radio propagation environments. The approach combines estimation of propagation conditions, subsequent appropriate channel modelling and optimisation feedback to the used positioning algorithm. Main advantages of the proposal are decreased system set-up effort, automatic re-calibration and increased precision.

Keywords

feedback; indoor communication; mobility management (mobile radio); optimisation; radio networks; radiowave propagation; telecommunication security; ubiquitous computing; channel modelling; location-awareness; mobile services; optimisation feedback; privacy issues; radio propagation; self-learning radio-based localization systems; ubiquitous availability; wireless networks; Accuracy; Adaptation models; Adaptive systems; Artificial neural networks; Databases; Radar tracking; Shadow mapping; indoor applications; localization techniques; propagation models;

fLanguage

English

Publisher

ieee

Conference_Titel

Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012 IEEE International Conference on

Conference_Location

Lugano

Print_ISBN

978-1-4673-0905-9

Electronic_ISBN

978-1-4673-0906-6

Type

conf

DOI

10.1109/PerComW.2012.6197572

Filename

6197572