Ph.D
Group : Networking

Network Coding for Quality of Service in WirelessMulti-hop Networks

Starts on 01/10/2009
Advisor : AL AGHA, Khaldoun

Funding : AM
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 15/11/2012, committee :

Research activities :

Abstract :
n this thesis we deal with the application of Network Coding to guarantee the Quality of Service (QoS) for wireless multi-hop networks. Since the medium is shared, wireless networks suffer from the negative interference impact on the bandwidth. It is thus interesting to propose a Network Coding based approach that takes into account this interference during the routing process. In this context, we first propose an algorithm minimizing the interference impact for unicast flows while respecting their required bandwidth. Then, we combine it with Network Coding to increase the number of admitted flows and with Topology Control to still improve the interference management. We show by simulation the benefit of combining the three fields: Network Coding, interference consideration and Topology Control.

We also deal with delay management for multicast flows and use the Generation-Based Network Coding (GBNC) that combines the packets per blocks. Most of the works on GBNC consider a fixed generation size. Because of the network state variations, the delay of decoding and recovering a block of packets can vary accordingly degrading the QoS. To solve this problem, we propose a network-and content-aware method that adjusts the generation size dynamically to respect a certain decoding delay. We also enhance it to overcome the issue of acknowledgement loss. We then propose to apply our approach in a Home Area Network for Live TV and video streaming. Our solution provides QoS and Quality of Experience for the end user with no additional equipment.

Finally, we focus on a more theoretical work in which we present a new Butterfly-based network for multi-source multi-destination flows. We characterize the source node buffer size using the queuing theory and show that it matches the simulation results.

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.