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17 mai 2018

Radio Localisation for IoT-LPWAN infrastructures

Catégorie : Doctorant

Context : With the emergence of new Internet of Things (IoT) networks, a massive deployment of sensor nodes connected at low speed and with very low power consumption can be seen in a very short time horizon. These nodes will communicate with various radio standards it is difficult to say at this time hich one will get the lead (LoRa, SigFox, NB-IoT, LTE-M, 5G-IoT,...). These countless sensors will very quickly produce data that can probably be exchanged automatically (M2M). In this context, node location information is a key feature for both source and consumer data authentication. Localization and positioning in IoT infrastructures and networks is therefore a major technical challenge, but difficult because of the small bands used by IoT nodes that directly impact location accuracy (200m, typically 1km away). This issue of IoT localisation is particularly important in the field of vehicles, for logistical monitoring or for future modes of electric urban mobility.

Objectives: The goal of the thesis is to improve the current positioning accuracy in focusing on the insight the knowledge and the propagation environment can bring into this problem. A testbed will be developped in the thesis to apply the new algorithms on real data.


  • Bibliographical study and state of the art on localization techniques for the IoT-LPWAN
  • Exploitation of the knowledge of the propagation environment with learning techniques
  • Construction and use of an experimental platform and proof of concept
  • Study of the specificities of mobility (Exploitation of observed radio variabilities)
  • Study of the issue of interference and its impact on performances

Doctoral School https://ed-mathstic.u-bretagneloire.fr/


Education : MS or equivalent Background : Signal Processing, Radio propagation.
Knowledge and skills in computer science and machine learning is welcome


To apply please send your CV, motivation letters and reference letters (optional) to:

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(c) GdR 720 ISIS - CNRS - 2011-2018.