2019-01382 - Post-Doctoral Research Visit F/M Post-Doctorant F/H Heterogeneous Data collection and processing for edge-based vehicular networks

Contract type : Public service fixed-term contract

Renewable contract : Oui

Level of qualifications required : PhD or equivalent

Fonction : Post-Doctoral Research Visit

About the research centre or Inria department

The Inria Lille - Nord Europe Research Centre was founded in 2008 and employs a staff of 360, including 300 scientists working in sixteen research teams. Recognised for its outstanding contribution to the socio-economic development of the Nord - Pas-de-Calais Region, the Inria Lille - Nord Europe Research Centre undertakes research in the field of computer science in collaboration with a range of academic, institutional and industrial partners.

The strategy of the Centre is to develop an internationally renowned centre of excellence with a significant impact on the City of Lille and its surrounding area. It works to achieve this by pursuing a range of ambitious research projects in such fields of computer science as the intelligence of data and adaptive software systems. Building on the synergies between research and industry, Inria is a major contributor to skills and technology transfer in the field of computer science.


The INRIA FUN research group investigates solutions to enhance programmability, adaptability and reachability of FUN (Future Ubiquitous Networks ) composed of RFID, wireless sensor and robot networks. Limited resources, high mobility and high security level evolving in distrusted environments characterize the objects that compose FUN. They communicate in a wireless way. To be operational and efficient, such networks have to follow some self-organizing rules. Indeed, components of FUN have to be able in a distributed and energy-efficient way to discover the network, self-deploy, communicate, self-structure in spite of their hardware constraints while adapting the environment in which adapting the environment in which they evolve. For additional information on the FUN research group, please see http://team.inria.fr/fun/


Our project will focus on vehicular networks based on the exploitation of enhanced
edge devices able to “treat“ data originated from different and heterogeneous
sources and through heterogeneous communication technologies. Keeping in mind
the main requirements of Vehicular Networks applications, such as low-latency,
energy-efficiency and real-time constraints, we will propose context-aware and
adaptive mechanisms in order to implement smarter edge devices that are reactive to
new situations. In the above-mentioned mechanisms, we will revise the role of Road
Side Unit (RSU) that will be charged with new types of tasks (i.e. those related to the
role of edge device).
The three main goals of HEAVE are as follow: 1) The treatment of different
heterogeneous data by trying to homogenize them through a cross-platform approach
[1][2]; 2) Reducing the sending of redundant data by applying advanced algorithms
for correlated data (e.g. Bayesian approaches [3]); 3) Design and implementation of
adaptive and context-aware mechanisms for new types of vehicular services and
applications assisting the vehicle user by defining Quality of Services (QoS) metrics
for prioritizing some specific applications.
The achievement of these three objectives will converge on the design of an
advanced and versatile platform respecting a hierarchical architecture with
vehicles/users as nodes, RSU as enhanced edge devices and cloud.
To the best of our knowledge, HEAVE platform is the first vehicular solution based on
a smart edge device integrating heterogeneous data sources treatment and
communication protocols to reduce the energy consumption by ensuring at the same
time the definition of QoS data flows.
[1] Perchat J., Desertot M., Lecomte S. (2014). COMMON framework : A hybrid
approach to integrate cross-platform components in mobile application. Journal of
Computer Science, Volume 10, Issue 11, pp. 2164-2180. [IF=0.69]
[2]Popovici D., Desertot M., Lecomte S., Delot T. (2013). A framework for mobile and
context-aware applications applied to vehicular social networks. Journal of Social
Network Analysis and Mining, Springer, Volume 3, Issue 3, pp 329–340.
[3] Cristanel Razafimandimby, Valeria Loscri, Anna Maria Vegni, Alessandro Neri. A
Bayesian and Smart Gateway Based Communication For Noisy IoT Scenario . ICNC
2017 - International Conference on Computing, Networking and Communications, Jan
2017, Silicon Valley, United States.

Main activities

  • Design of an adaptive communication paradigm for edge based vehicular
  • Realize a survey of similar existing techniques
  • Implementation of the designed solution


- Knowledge in wireless networks and/or edge/fog architecture
- Skills in Simulation tools and development
- English speaking
- Autonomy
- Open minded
- Team working
- Capacity to write English reports and papers

Benefits package

  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours)
  • Access to vocational training
  • Social security coverage


Net monthly salary (after taxes) : 2132.97€