PhD AI-Powered Reliable and Available Wireless Mesh Networks for the Factory of the Future F/M

15/07/2022 – 16/07/2022 all-day

Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : Orange Labs / ICube
Durée : 36M
Contact :
Date limite de publication : 2022-07-15

Contexte :
You will participate to experiment-based research, developing prototypes to assess the performance of your ideas in realistic environments, with concrete scientific productions. You will have the opportunity to run experiments on large-scale testbeds (with hundreds of devices). A participation to the IETF is also expected, with concrete propositions and possibilities to push ideas to standards, through the novel RAW working group.

You will be involved in an exciting environment, with several key French academic and industrial players in the Internet of Things. In particular, you will be an active participant of the future ANR CONNECT project, expected to bootstrap in 2022.

You will also be integrated in the Network research group at ICube, where several researchers have a strong experience in Internet of Things, and Internet in general. The group hosts also one part of the large-scale FIT IoT-Lab platform and you will benefit from the strong skills in experimental research and reproducibility of the group.

Orange Innovation brings together the research and innovation activities and expertise of the Group’s entities and countries. We work every day to ensure that Orange is recognized as an innovative operator by its customers and we create value for the Group and the Brand in each of our projects. With 740 researchers, thousands of marketers, developers, designers and data analysts, it is the expertise of our 6,000 employees that fuels this ambition every day.

Orange Innovation anticipates technological breakthroughs and supports the Group’s countries and entities in making the best technological choices to meet the needs of our consumer and business customers.

Within Innovation, you will join a research team in the department « Machine To Machine, Internet of Things and Smart Cities” specialized in IoT connectivity technologies. The team has about fifteen engineers and researchers and also hosts doctoral and post-doctoral students working on various cutting edge topics such as 6G physical layer design, Artificial Intelligence and communication protocols for the IoT like 802.15.4 TSCH.

Sujet :
Your role is to carry out a thesis work on “AI at the service of Reliable and Available Wireless Mesh Networks for the Factory of the Future”.

The industry is amid an in-depth transformation with the pervasive integration of sensors and actuators in the manufacturing process. So-called Industry 4.0 involves the agile combination of reliable process monitoring, data analysis and timely operational adaptation of production lines and Industrial Internet of Things (IIoT) networks, such as 5G-URLLC and IEEE 802.15.4 networks, are critical enablers to this transformation.
The later IIoT networks operate on license-free frequency bands and allow for low-power and low-cost device implementations. However, achieving latency and delivery requirements of Industry 4.0 use-cases with state-of-the-art IEEE 802.15.4 networks is still an open challenge, largely due to interference and harsh radio propagation environments.

Novel enablers at the physical layer – such as IEEE 802.15.4g radio waveforms and modulations – or at the MAC layer, i.e. IEEE 802.15.4e TSCH, are stepping stones to bridge the gap between IIoT networks capabilities on unlicensed spectrum and Industry 4.0 requirements. The new radio waveforms and modulation offer a wide range of range and bit-rate vs link budget operating points, allowing the adaptation of data-rate to link quality, while Time Slotted Channel Hopping (TSCH) mechanisms and the IETF 6TOP protocol lay the basis for a centralized orchestration of the network, enabling time-sensitive, high-availability uses-cases.

In this context, the main objective of this thesis is to define a complete toolbox allowing to orchestrate the radio communications in a wireless mesh network through a combination of centralized and distributed decision making based on Reinforcement Learning (RL) algorithms, in order to meet the reliability and latency requirements for the FoF applications.
In order to achieve this goal, you will study RL-based resource allocation and scheduling algorithms and their application to wireless mesh networks. Specifically, DQN (Deep Q Learning) algorithms for centralized long-term resource allocation, and MAB (Multi-Armed Bandit) algorithms for connectivity restoration in case of connectivity topology change, and for continuous optimization to accommodate possible variations.
The main challenges to be addressed are the modeling of endogenous/exogenous interference in a mesh network, the establishment of a constrained schedule (half-duplex radios, delay, energy consumption, etc.) and the restoration of the connectivity under constraints (respect of deadlines and delivery rate).

The main expected achievements are the design algorithms allowing the calculation of communication schedules in multi-hop networks and the establishment of backup routes in case of transmission failure according to the calculated schedule, and their integration in a PCE (Path Computation Element) network controller and demonstrator.

Profil du candidat :
You have a Master’s degree in Computer Science or Data Science.

You are creative and innovative, have good interpersonal skills and a high motivation for research. Curiosity, critical thinking, open-mindedness, autonomy, and ability to organize one’s work according to the objectives to be reached are qualities particularly appreciated for research work. Dynamism, proactiveness and communication skills are also qualities that would be appreciated. You want to transform your ideas in concrete prototypes, and to play with large-scale experiments.

Formation et compétences requises :
It is required to have some experience and in-depth knowledge of wireless networks, and to be familiar with reinforcement learning techniques. Skills in low-power radio technologies would be a plus.

You have good programming skills (C, Python) and a previous experience in embedded development, preferably on a board including a radio circuit.

Excellent level of English is mandatory. Conversational French is also desirable.

Adresse d’emploi :
Orange Labs Meylan, with frequent visits at ICube, Strasbourg