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Ph.D de

Ph.D
Group : Parallel Systems

Power-Aware Protocols for Wireless Sensor Networks

Starts on 01/09/2012
Advisor : BEAUQUIER, Joffroy
[Joffroy Beauquier and Janna Burman]

Funding : Bourse pour étudiant étranger
Affiliation : Université Paris-Sud
Laboratory : LRI

Defended on 15/12/2017, committee :
M. Luís E. T. RODRIGUES Professeur (Rapporteur)
Université de Lisbonne
M. Alexandre CAMINADA Professeur (Rapporteur)
Université de Technologie de Belfort-Montbéliard
M. Abdel LISSER Professeur (Examinateur)
Université Paris Sud, Saclay
Mme. Janny LEUNG Professeur (Examinatrice)
Université chinoise de Hong Kong
M. Joffroy BEAUQUIER Professeur (Directeur de thèse)
Université Paris Sud, Saclay
Mme. Janna BURMAN Maître de conférence (Co-encadrante)
Université Paris Sud, Saclay
M. Thomas NOWAK Maître de conférence (Invité)
Université Paris Sud, Saclay

Research activities :

Abstract :
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.

Ph.D. dissertations & Faculty habilitations
DECODING THE PLATFORM SOCIETY: ORGANIZATIONS, MARKETS AND NETWORKS IN THE DIGITAL ECONOMY
The original manuscript conceptualizes the recent rise of digital platforms along three main dimensions: their nature of coordination devices fueled by data, the ensuing transformations of labor, and the accompanying promises of societal innovation. The overall ambition is to unpack the coordination role of the platform and where it stands in the horizon of the classical firm – market duality. It is also to precisely understand how it uses data to do so, where it drives labor, and how it accommodates socially innovative projects. I extend this analysis to show continuity between today’s society dominated by platforms and the “organizational society”, claiming that platforms are organized structures that distribute resources, produce asymmetries of wealth and power, and push social innovation to the periphery of the system. I discuss the policy implications of these tendencies and propose avenues for follow-up research.

DISTRIBUTED COMPUTING WITH LIMITED RESOURCES


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