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-Saclay
Laboratory : LRI

Defended on 15/12/2017, committee :

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.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.