The emergence of connected electronic devices has paved the way for the concept of "smart city". In the paradigm of
smart cities, each electronic device becomes a miniature computing platform, equipped with multiple sensors and interacting with the
infrastructure through wireless connectivity. In the context of smart cities, streetlights are a particularly coveted resource, for
two reasons: (i) access to the power grid makes it possible to equip the lamp post with multiple sensors, without having to worry
about consumption problems; (ii) the regular deployment of lampposts makes it possible to have a regular and repetitive mesh of
the sensors.

This project will mainly study RF systems to locate, track and classify road users.
Each streetlight is equipped
with wireless signal sensors (for detecting transmitters) as well as a radar system, providing each an independent estimate of the
location of a user. The high density of streetlights provides access to numerous user location estimates, which can be combined
using data fusion algorithms to achieve accurate positioning.

In this project, we propose to: (i) design algorithms to locate,
track and classify users, making full use of the high density and multi-technology nature of
smart street lights; (ii) characterize
the performances of these algorithms by simulations; (iii) validate the algorithms experimentally.