October 04, 2011, ESB room 2003

Consensus algorithms are a popular choice for computing averages and other aggregate functions in ad-hoc wireless sensor networks. However, existing work mostly addresses the case where the measurements lie in a Euclidean space. In the first part of the talk we will present Riemannian consensus, a natural extension of consensus algorithms to Riemannian manifolds. We will discuss its convergence properties and their dependence on various factors, such as network connectivity, geometric configuration of the measurements and curvature of the manifold. In the second part of the talk we will focus on the problem of distributed 3-D camera network localization, and show how ideas and analysis techniques from Riemannian consensus can be extended and applied to this problem.

Roberto Tron received his BSc degree in 2004 and MSc degree (highest honors) in 2007 in Telecommunication Engineering from the Politecnico di Torino in Italy. He also received a Diplome d'Engenieur from the Eurecom Institute and a DEA degree from the Universit\'e de Nice Sophia-Antipolis in 2006. He is currently a PhD student in the Department of Electrical and Computer Engineering at the Johns Hopkins University. His research interests include motion segmentation and distributed algorithms on camera sensor networks.