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Ph.D de

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
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