About the research centre or Inria department

The Inria research centre in Lyon is the 9th Inria research centre, formally created in January 2022. Itbrings 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 andcomputational biology.

Context

The doctoral position at Inria's AGORA research group, located at the La Doua Campus in Lyon, offers aunique opportunity to collaborate with esteemed experts such as Prof. Hervé Rivano, Prof. Razvan Stanica, and Dr. Juan Fraire. The appointee will use advanced software and simulators, enhancing theirexpertise in communication systems, wireless sensor networks, and urban network planning. This role isenriched by AGORA's strong international and academic-industrial collaborations. It offers the chance todelve into the smart city and satellite domains, exploring technologies pivotal to wireless sensornetworks and massive machine-to-machine communications. This position is a gateway to cutting-edge research and professional growth in IoT and networking.

Assignment

Overview

This research delves into the dynamic contact networks within bike-to-infrastructure and Low-Earth Orbit (LEO) satellite systems.

In this context, a contact is a time-bound interaction allowing data exchange between two nodes,characterized by parameters like duration, nodes involved, signal attributes, and reliability metrics. Acontact set encapsulates these interactions over time, forming a network topology that can be either aforward-looking contact plan or a historical contact trace. Central to this study is the hypothesis that AIcan effectively translate historical contact traces into predictive contact plans. The research willleverage AI to analyze temporal patterns in contact data, aiming for accurate predictions of future network interactions.

This project will focus on two use cases: urban bike-to-infrastructure networks with complex, ever-changing data streams and LEO satellite networks characterized by their vast, evolving mega-constellations.

Use Cases

LEO Satellite Networks: Emerging Low Earth Orbit (LEO) Satellite Networks are set to revolutionize EarthObservation and broadband communication systems. Initially conceptualized in the early 1990s, thesenetworks have gained renewed attention with the advent of Mega-Constellations. The constantly shifting satellite positions result in a continuously evolving network topology, traditionally representedas a sequence of 'snapshots' – stable network configurations over short periods, each comprising a set ofcontacts. Comprising numerous satellites interconnected through Inter-Satellite Links (ISLs), thesenetworks facilitate low-latency, high-capacity communication vital for broadband services and EOmissions. The dynamic motion of satellites in LEO presents unique challenges in maintaining effectivecommunication links, as the ISLs represent temporal contacts with finite lifetimes due to continuoussatellite movement. The challenge lies in efficiently predicting and managing these dynamic topologiesto ensure uninterrupted and reliable data exchange, a vital component for the success of autonomous,heterogeneous satellite networks.

Bike-to-Infrastructure Connectivity: With the rapid urbanization since the mid-20th century, cities havewitnessed a significant increase in challenges like traffic congestion, pollution, and unhealthy lifestyles.Biking has emerged as a sustainable alternative, leading to a worldwide trend in urban bikingdevelopment. Bike-sharing systems, in particular, have grown tremendously, with thousands of bikes inover a thousand cities globally. This shift towards intelligent transport systems (ITS) has given rise toinnovative approaches like the "Internet of Bikes" (IoB-DTN). IoB-DTN is a Delay Tolerant Network (DTN)protocol tailored for Internet of Things (IoT) applications in urban bike-sharing systems. It emphasizes data aggregation, leveraging the mobility of bikes to collect and relay data efficiently. The protocolexplores spatial, temporal, and spatiotemporal data aggregation strategies, aiming to optimize networkthroughput and reduce energy consumption in data transmission. In this context, the project will focuson advanced contact modeling to understand and predict the interactions between bikes and urbaninfrastructure to enhance urban mobility experiences.

Objectives

General Objective: To enhance predictive analysis in dynamic contact networks through AI-drivenmethodologies, focusing on two distinct use cases: urban bike-to-infrastructure networks and LEOsatellite systems. The aim is to develop and validate AI models that can effectively predict future network interactions by analyzing historical contact data and optimizing network efficiency and reliability.

Specific Objectives:

• Data Collection and Processing: Gather extensive contact trace data from urban bike test benchesand satellite orbital datasets. Process and prepare the data for analysis, ensuring it is suitable for AI modeling.
• Development of Predictive Models: Utilize time series analysis and forecasting methods to predictfuture contacts in both use cases. Implement Graph Neural Networks (GNNs) to model the complex relationships between network nodes.
• Optimization and Routing: Apply Reinforcement Learning (RL) techniques to optimize networkrouting and decision-making processes. Develop clustering and community detection models tocategorize bikes or satellites based on behavioral patterns.
• AI Algorithm Design and Implementation: Create and refine AI algorithms, focusing on predictiveanalytics and feature recognition specific to each use case. Integrate different ML techniques,including RNNs, LSTMs, GCNs, and RL methods, to address the unique challenges of each network.
• Model Validation and Testing: Test the developed AI models for bike-to-infrastructure and satellitenetworks within realistic scenarios. Evaluate the performance of the models against key metricssuch as accuracy, efficiency, and reliability.
• Scholarly Dissemination: Publish research findings in premier academic conferences and journals.Share insights and methodologies developed during the project with the broader AI and networkresearch communities.
• Application and Impact Assessment: Assess the practical applications of the research in enhancing urban mobility and satellite communication systems. Evaluate the potential societal andenvironmental impacts of improved network management and efficiency.

Methodology

The research plan considers a range of Machine Learning (ML) solutions to address the challenges ofpredictive analysis in dynamic contact networks, specifically for bike-to-infrastructure and LEO satellitenetworks. Below is A list of potential ML approaches and how they could be applied to the project.

• Time Series Analysis and Forecasting
• Models: Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, andGated Recurrent Units (GRUs).
• Application: These models are well-suited for predicting future contacts based on temporalpatterns in historical data. They can effectively handle sequential data, making them idealfor time-dependent contact trace analysis.
• Graph Neural Networks (GNNs):
• Models: Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs).
• Application: GNNs can model the complex relationships and interdependencies between different nodes in a network, which is crucial for understanding the dynamics of satellitenetworks and urban bike infrastructures.
• Reinforcement Learning (RL):
• Models: Deep Q-Networks (DQN), Policy Gradient methods, Actor-Critic models.
• Application: RL can optimize decision-making processes, such as routing in bike-to-infrastructure networks and satellite network operations, by learning the best actions to take in various states of the network.
• Clustering and Community Detection:
• Models: K-means, Hierarchical Clustering, DBSCAN, Louvain method for communitydetection.
• Application: These methods can segment bikes or satellites into groups based on similarcharacteristics or behaviors, aiding in the management and optimization of the networks.

References

1. Ruiz-De-Azua, Joan A., et al. "Assessment of satellite contacts using predictive algorithms forautonomous satellite networks." IEEE Access 8 (2020): 100732-100748.

2. Fontanesi, G., et al. "Artificial Intelligence for Satellite Communication and Non-TerrestrialNetworks: A Survey." arXiv preprint arXiv:2304.13008 (2023).

3. Müller, Kevin. "Building Contact Graphs for Large-scale Constellations." Bachelor's thesis, SaarlandUniversity, 2023.

4. Magnana L. et al. “Implicit GPS-based bicycle route choice model using clustering methods and anLSTM network” PLoS ONE, 2022

5. Delaine F. et al. “Rendez-vous Based Drift Diagnosis Algorithm For Sensor Networks Towards In SituCalibration” IEEE Transactions on Automation Science and Engineering, 2022

6. Zguira Y., et al. “Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection”Sensors, 2018

Main activities

1. Data Collection: Acquiring extensive contact traces through connected bike test benches andsatellite orbital data.

2. Predictive Modeling: Generating large-scale contact plans, utilizing AI to process and predictnetwork dynamics.

3. Data Structuring: Creating an optimized data structure for contact sets that will feed into the AImodels.

4. AI Development: Designing innovative AI algorithms for both domains, focusing on predictiveanalytics and feature recognition.

5. Model Validation: Testing the AI models within realistic network scenarios for bike-to-infrastructure and satellite communications.

6. Scholarly Contribution: Publishing findings in leading conferences and journals, contributing novelinsights to AI in dynamic contact networks.

Skills

We encourage applications from researchers with a Computer Science or Computer Engineering profile.Practical proficiency with programming languages (C/C++ and Python) is desirable. A solid understandingof mathematics, informatics, and mobile wireless networking is also preferred. Applicants must havefluency in English; proficiency in French is not a prerequisite but would be advantageous. We are seekingcandidates who are empathetic, proactive, and self-motivated.

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)+ possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of workinghours)
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational trainingS
• ocial security coverage

Remuneration

1st and 2nd year: 2100 euros gross salary /month

3rd year: 2190 euros gross salary / month