2023-06353 - PhD Position F/M Big Data and Machine Learning Methods for Direct-to-Satellite Internet of Things
Contract type : Fixed-term contract
Level of qualifications required : Graduate degree or equivalent
Fonction : PhD Position
Level of experience : From 3 to 5 yearsAbout the research centre or Inria department
The Inria research centre in Lyon is the 9th Inria research centre, formally created in January 2022. It brings together approximately 300 people in 16 research teams and research support services.
Its staff are distributed at this stage on 2 campuses: in Villeurbanne La Doua (Centre / INSA Lyon / UCBL) on the one hand, and Lyon Gerland (ENS de Lyon) on the other.
The Lyon centre is active in the fields of software, distributed and high- performance computing, embedded systems, quantum computing and privacy in the digital world, but also in digital health and computational biology.Context
The doctoral program will occur within the Inria Agora research group at the La Doua Campus in Lyon. The Space-Terrestrial Integrated Internet of Things (STEREO) ANR project (Projet de recherche collaborative- entreprise, or PRCE) (ANR-22-CE25-0014-01 managed by Inria) provides and funds the position. The candidate will collaborate with four group members: Dr. Hervé Rivano (director), Dr. Juan Fraire (encadrant), Dr. Oana Iova, and Prof. Fabrice Valois. Some remote work may be possible. The Ph.D. candidate will utilize pre-existing software tools, including simulators and optimizers provided by the Agora group. There is no requirement for regular travel associated with this position.Assignment
Context : The Ph.D. candidate will investigate the Direct-to-Satellite IoT (DtS-IoT) communication paradigm under the guidance of Dr. Hervé Rivano and the assistance of Dr. Juan Faire, Dr. Oana Iova, and Prof. Fabrice Valois. DtS-IoT offers a promising solution for providing data transfer services to IoT devices in remote areas where deploying terrestrial infrastructure is impractical or unfeasible. The project aims to establish a Space-Terrestrial Integrated Internet of Things (STEREO), enabling seamless connectivity for IoT devices to terrestrial gateways or directly to low-Earth orbit (LEO) satellites when no network infrastructure is available. Consequently, satellites can serve as passing-by IoT gateways, allowing devices to offload buffered data. The applications of DtS-IoT encompass various areas, including international asset tracking, cross-border environmental monitoring, and global data collection. These applications extend to remote regions that lack low-cost, power-efficient terrestrial IoT connectivity, such as poles, deserts, and oceans. The project aligns with the current trend in the space industry known as the "new space" paradigm, characterized by reduced costs and the emergence of new players exploring space technologies and business opportunities, such as nano-satellites. Despite the favorable context and compelling applications, DtS-IoT poses significant challenges due to transmission distances, dynamic channel conditions, and the resource limitations of ground-based devices.
Challenges : The primary challenge of DtS-IoT stems from its inherent characteristics. DtS-IoT aims to establish a direct device-to-gateway connection within a highly dynamic low-Earth orbit (LEO) environment without additional infrastructure. In such conditions, existing IoT medium access control (MAC) schemes must be supplemented with advanced informatics mechanisms to handle the potential simultaneous connection of millions of devices within the LEO coverage area. Furthermore, the challenges are amplified by limited energy availability and latency issues resulting from the extended channel range of approximately 600 kilometers. This poses difficulties in employing negotiation approaches reliant on extensive handshakes, underscoring the necessity of accurate predictions supported by machine learning techniques in DtS-IoT. DtS-IoT offers a unique opportunity to enhance machine learning predictions by leveraging the predictable nature of orbital mechanics, the tolerance for delays, and the learning derived from frequent revisits of satellites to service areas. These aspects necessitate novel Big Data approaches to effectively manage the numerous parameters required for modeling modern large-scale constellations, commonly called satellite mega-constellations. However, deploying DtS-IoT systems still needs to be improved. The private sector often drives it, resulting in challenges in accessing real-world data for research and development purposes. Overcoming these challenges requires concerted efforts to make DtS-IoT systems more widely available, reduce deployment size, and establish collaborations with private entities to facilitate access to relevant data.
Objective: This project aims to investigate methods and algorithms in the fields of Big Data and Machine Learning that can effectively address the challenges posed by DtS-IoT. As a sub-objective, the project will focus on developing suitable simulator tools capable of generating synthetic and realistic datasets for research purposes. This development will be based on the existing FLoRaSat tool, previously developed by the Agora team. Furthermore, the solutions derived from this research will be utilized as inspiration, mapped, and applied to the existing Low-Power Wide Area Network (LPWAN) technologies. These adapted solutions are being considered for various public and private DtS-IoT projects to provide connectivity in areas with limited terrestrial infrastructure.
The main research questions that will be addressed in this Ph.D. project are:
The main expected activities are:
Note: The prioritization and weight of the activities will be defined during the project.Skills
Technical: The candidate must be at least in his/her last year of master studies (or equivalent) in Computer Science or Telecommunications. Good mathematical background, performance evaluation, wireless networking, and practical skills with programming languages (e.g., C/C++, Python) are required.
Languages: Fluent English level. Knowledge of the French language is not mandatory
Soft: Active listening, empathetic communication, the ability to tolerate and accept appropriate differences, proactive and self-driven.Benefits package
Theme/Domain : Networks and Telecommunications System & Networks (BAP E)
Town/city : Villeurbanne
Methods : The project's methodology is at the crossroads of engineering, computer science, and data science disciplines, a necessary equilibrium to deal with the aforementioned DtS-IoT challenges.
Collaboration : The DtS-IoT topic is hot in the academic and industrial sectors. We foresee academic collaborations with IRIT / ENSEEIHT Toulouse researchers and foreign laboratories such as i2CAT in Barcelona and Universidad de Chile. Cooperation is ongoing with Semtech (the company that developed LoRa), and we are extending it to the DtS-IoT topic. Finally, several companies such as Lacuna, Kinesis, and Swarm are pursuing DtS-IoT activities and will be contacted as potential partners.About Inria
Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.Instruction to apply
Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.
Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities.
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