Ph.D
Group :

Clock synchronization and localization for wireless sensor network

Starts on 01/10/2015
Advisor : LAMBERT, Alain

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

Defended on 12/11/2018, committee :

Research activities :

Abstract :
Wireless sensor networks (WSNs) play an important role in applications such as environmental monitoring, source tracking, health care, etc. In WSN, sensors have the ability to perform data acquisition, distributed computing and information fusion. To perform such tasks, information about their localization in some reference frame, and the availability of well-synchronized clocks is very important.

WSNs have been widely studied in the past years, and the scientific literature reports many outcomes that make them applicable to various fields such as environmental monitoring, health care, and the internet of things. For some others, research still needs to find solutions to some of the challenges posed by the limitations of sensors, such as battery limitation, dynamicity, and low computing clock rate. With the aim of contributing to the research on WSN, this thesis proposes new algorithms for both clock synchronization and localization. The proposed synchronization algorithm is able to tolerate a low connectivity dynamic WSN and results in a much smaller clock skew than its previous work.

Localization of sensors has been widely studied. Nevertheless, the characterization of estimation uncertainty is usually overlooked. Asymptotic techniques are usually employed, assuming that the noise is Gaussian and that many measurements are available. These hypotheses are seldom satisfied. This thesis applies the Leave-out Sign-dominant Correlated Regions (LSCR) algorithm to localization problem from received signal strength measurements. With LSCR, one evaluates the accurate estimates of confidence regions with prescribed confidence levels. From this confidence region, an estimate of the sensor location may also be obtained. In this thesis, several localization approaches are implemented and compared. The results show under mild assumptions, LSCR obtains competitive localization performance compared to other methods.

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.