| KEYWORD |
Telecommunication Networks Group
Theses in external company
Mobile Edge Computing and augmented reality for the “augmented technician”
Thesis in external company 
Thesis abroad
keywords AUGMENTED REALITY, EDGE COMPUTING
Reference persons PAOLO GIACCONE
External reference persons Leonardo Linguaglossa, Telecom Paris, linguaglossa@telecom-paris.fr
Research Groups Telecommunication Networks Group
Thesis type EXPERIMENTAL - DEVELOPMENT
Description The recent Edge computing paradigm is increasingly becoming an effective alternative to existing cloud computing technologies.
Edge systems have been initially designed under the supervision of the ETSI working group on Mobile Edge Computing (MEC) with a focus on mobile users. However, we observe nowadays that the interest has shifted towards other ICT domains. In particular, Edge computing has been identified as a potential key factor to achieve the performance and reliability offered by 5G and next generation networks. In this internship, the candidate will study the state-of-the-art of Edge systems, and implement one or more solutions for distributed computation and resource access. The target use case is the “augmented technician”: in this scenario, an operator uses an augmented reality device to obtain detailed information about their surroundings. Furthermore, they can perform some actions in real time by connecting to a remote tool. The internship can be an introductory step to enter a Ph.D.
The intern student will have to deploy an edge computing prototype within a real 5G private network, hosted by Telecom Paris lab. In parallel, they will have to work on distributed orchestration systems (e.g., Kubernetes) and computation units (e.g., containers) in a specific environment hosted by EDF, the largest electricity provider in France and Europe. Finally, they will have to deploy an existing augmented reality application by EDF to be transposed towards a containerized or VM-based application.
See also stage_edf_2022_en.pdf
Required skills Excellent programming skills. Knowledge of the machine learning fundamentals.
Students with average grade higher than 27/30 are preferred.
Notes The thesis will be done in the company, in collaborations with EDF and Telecom Paris.
Deadline 08/02/2023
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