Post-doctoral Research Visit F/m Mobility-aware

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat **:CDD

**Niveau de diplôme exigé **:Thèse ou équivalent

**Fonction **:Post-Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris since 2021.

The centre has 39 project teams, 27 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris. Its activities occupy over 600 scientists and research and innovation support staff, including 54 different nationalities.

**Contexte et atouts du poste**:
**Mission confiée**:
A direct consequence of hosting resources in a distributed way at the Edge is their exposure and sensitivity to the heterogeneity, massiveness, and uncertainty in mobility and demands of smart devices, leading to non-optimal edge usage in the long run. We aim to deal with such impacting factors in device behaviors by bringing (i) perceptive and aware mobility/demand quasi-in-time anticipation, (ii) uncertainty handling, and (iii) self-adaptation to device-edge resource management.

The **second goal** will be the design of perceptive mobility-aware offloading policies and adaptive allocation strategy according to the quasi-in-time anticipation of spatial-temporal resource demands. The quasi-in-time anticipation will limit service interruptions due to networking uncertainties or overload. The **third goal** concerns the evaluation of the designed framework, policies, and strategies. Besides, the benefits and tradeoffs of decisions that are taken based on quasi-in-time spatiotemporal anticipation of demands will also be analyzed (e.g., energy or resource loss/gain).

**References**

[1] D. Do Couto Teixeira, A. Carneiro Viana, J. Marques Almeida, and M.S. Alvim, ”The Impact of7Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility”, ACM Trans-8actions on Spatial Algorithms and Systems, vol. 7, no. 4, June 2021.

[2] D. Do Couto Teixeira, A. Carneiro Viana, J. Marques Almeida. On estimating the predictability of10human mobility: the role of routine. EPJ Data Science, 2021, 10 (1).

[3] L. Amichi, A. C. Viana, M. Crovella, A. A. F Loureiro. Revealing an inherently limiting factor in human12mobility prediction. IEEE Transactions on Emerging Topics in Computing, 2023. In press.

[4] L. Amichi, A. C. Viana, M. Crovella, A. A. F Loureiro. Understanding individuals’ proclivity for novelty14seeking. ACM SIGSPATIAL 2020.

[5] Pedro Cruz, Nadjib Achir, Aline Carneiro Viana. On the Edge of the Deployment: A Survey on16Multi-Access Edge Computing. ACM Computing Surveys, 55 (5). 2022.

[6] H. Mazouzi, N. Achir, and K. Boussetta. DM2-ECOP: an efficient computation offloading policy for18multi-user multi-cloudlet mobile edge computing environment. ACM Transaction on Internet Tech-19nology, 20219, 19(2).

[7] H. Mazouzi, K. Boussetta, and N. Achir. Maximizing mobile energy saving through tasks optimal21offloading placement in two-tier cloud: A theoretical and an experimental study. Elsevier Computer22Communication, 144:132-148, 201

**Principales activités**:

- Literature and code review of the team's previous works and design of the framework for quasi-in-time anticipation of spatial-temporal resource demands
- Evaluate designed solutions, mainly through emulation tools or experimental platforms, following the recommendations of ETSI MEC architecture to mimic realistic MEC infrastructures.
- Quantify benefits and tradeoffs of quasi-in-time spatiotemporal anticipation on energy, resource usability, network management, etc.
- Analyse performance gains obtained when perceptive-to-needs policies and adaptive allocation strategies are implemented.

**Compétences**:

- A Ph.D. degree in wireless networks, mobile networks, or data-related topics.
- a solid understanding of networking principles, protocols, and architectures is essential.
- ability to write and debug (student) code in Python is an important requirement.
- proficiency in programming languages commonly used in AI and networking research.
- Experience with relevant libraries and frameworks is also valuable.
- Ability to design and implement algorithms for solving complex problems.
- Familiarity with optimization techniques.
- Excellent written and verbal communication skills for presenting research findings, writing academic papers, and collaborating with peers.
- The ability to work effectively as part of a research team, collaborate with colleagues from diverse backgrounds, and contribute positively to group dynamics
- A good personal and project management skills are required to function in this multi-disciplinary multi-team project.

**Avantages**:

- Subsidized meals
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of