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Ph.D de

Ph.D
Group : Human-Centered Computing

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Starts on 01/10/2014
Advisor : CHAPUIS, Olivier

Funding : contrat doctoral du Ministère
Affiliation : Université Paris-Sud
Laboratory : LRI - HCC

Defended on 15/12/2017, committee :
Directeur de thèse :
- M. Olivier Chapuis, Chargé de Recherche, CNRS

Co-encadrante de thèse :
- Mme Anastasia Bezerianos, Maître de Conférence, Université Paris-Sud

Rapporteurs :
- M. Stéphane Conversy, Professeur, ENAC
- M. Raimund Dachselt, Professeur, Technische Universität Dresden

Examinateurs :
- M. Edward Lank, Professeur associé, University of Waterloo
- M. Jean-Daniel Fekete, Directeur de Recherche, Inria

Research activities :

Abstract :
In this thesis, I study the benefits of collaboration using an Ultra-High Resolution Interactive Wall Display (UHRWD). I focus on the specific collaborative context of control rooms. Visits of control rooms and interviews with operators show that different degrees of collaboration are required in function of the situation. I believe that a UHRIWD could be beneficial in situations when close collaboration is needed. I first show that wall display encourages close collaboration compared to multiple separate displays. Then I show that the interaction techniques can also influence the degree of collaboration, for instance, a technique with a large visual footprint also encourages a close collaboration. I apply this in the design of technique to visualize road traffic forecast on a wall display for road traffic control centres. Finally, I propose techniques to help the transition between the different setups of a control room: the workstations and the wall display.

Ph.D. dissertations & Faculty habilitations
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POWER-AWARE PROTOCOLS FOR WIRELESS SENSOR NETWORKS
In this thesis, we propose a formal energy model which allows an analytical study of energy consumption, for the first time in the context of population protocols. Population protocols model one special kind of sensor networks where anonymous and uniformly bounded memory sensors move unpredictably and communicate in pairs. To illustrate the power and the usefulness of the proposed energy model, we present formal analyses on time and energy, for the worst and the average cases, for accomplishing the fundamental task of data collection. Two power-aware population protocols, (deterministic) EB-TTFM and (randomized) lazy-TTF, are proposed and studied for two different fairness conditions, respectively. Moreover, to obtain the best parameters in lazy-TTF, we adopt optimization techniques and evaluate the resulting performance by experiments. Then, we continue the study on optimization for the poweraware data collection problem in wireless body area networks. A minmax multi-commodity netow formulation is proposed to optimally route data packets by minimizing the worst power consumption. Then, a variable neighborhood search approach is developed and the numerical results show its efficiency. At last, a stochastic optimization model, namely the chance constrained semidefinite programs, is considered for the realistic decision making problems with random parameters. A novel simulation-based algorithm is proposed with experiments on a real control theory problem. We show that our method allows a less conservative solution, than other approaches, within reasonable time.

USE OF INTRISIC MOTIVATIONS IN REINFORCEMENT LEARNING. APPLICATION TO SECURITY IN MULTI-AGENT SYSTEMS