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Keynote Lectures

Available soon.
M. Verleysen, Machine Learning Group, Université Catholique de Louvain, Belgium

Indoor Localization - Solved, Finally?
Kay Römer, Graz University of Technology, Austria

 

Available soon.

M. Verleysen
Machine Learning Group, Université Catholique de Louvain
Belgium
 

Brief Bio
Available soon


Abstract
Available soon.



 

 

Indoor Localization - Solved, Finally?

Kay Römer
Graz University of Technology
Austria
 

Brief Bio
Kay Römer is professor at and director of the Institute for Technical Informatics and head of the Field of Expertise "Information, Communication & Computing" at TU Graz. He obtained his doctorate in computer science from ETH Zurich in 2005 with a thesis on wireless sensor networks. Kay Römer is an internationally recognized expert on networked embedded systems, with research focus on wireless networking, fundamental services, operating systems, programming models, dependability, testbeds, and deployment methodology. He has co-chaired the program committees of leading conferences in the field such as SenSys or IPSN, he is also chairing the steering committee of the EWSN conference series. He is coordinator of the TU Graz Research Center "Dependable Internet of Things" and leads the research area "Cognitive Products" in the research center Pro2Future - Products and Production of the Future.


Abstract
Accurate and reliable indoor localization of smart objects is a key services in many applications domains of the Internet of Things such as smart homes, smart factories, or smart healthcare. While the related problem of outdoor localization has been (mostly) solved by global navigation satellite systems (GNSS) such as GPS, GLONASS, or GALILEIO - indoor localization has been an active research topic over several decades without finding an ultimate solution that is as mature as GNSS.

A very promising technology in this regard are Ultra-Wide-Band (UWB) radio transceivers. While UWB has a long research history, only recently low-power and low-cost UWB transceivers, for example ones produced by DecaWave, have appeared on the market and are being included in the latest smartphone generation, such that UWB will likely become a ubiquitous technology.

Due to the ultra-wide bandwidth, UWB radios transmit very short pulses that allow accurate measurement of time-of-flight even in multi-path environments, which in turn allows for distance measurements with an accuracy of a few centimeters. While UWB radios thereby provide a very promising technology foundation, the indoor localization problem isn't automatically solved by the availability of UWB.

Instead, serveral important research questions have to be addressed to arrive at a global indoor localization system. Firstly, there is the challenge to minimize the infrastructure required to cover large indoor areas. With GPS some tens of satellites are sufficient to cover the globe, but due to the limited communication range of UWB, this is not possible with indoor localization. Secondly, how can we support localization of an arbitrarily large number of densely deployed tags with high update rates? Traditional UWB-based distance measurement requires pair-wise sequential measurements between each tag and each reference station, which does not scale. Thridly, how can robust and accurate localization be achieved in typical indoor enviroments with many obstacles, where line-of-sight between reference and tag is obstructured or even blocked? In this keynote, we present present latest results we obtained in the "Dependable Internet of Things" research center at TU Graz towards addressing these challenges.



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