Networking of Women in Computing

"Networking of Women in Computing“ mit Prof. Dr. Anna Förster (Universität Bremen)

S3|20 KOM-Lounge / Terrasse (vor Raum 111), Rundeturmstr. 10, 64283 Darmstadt

Titel:

Organic Data Dissemination in Opportunistic Networks

Abstract:

Current opportunistic networking concepts mainly focus on destination-oriented applications, where the receiver is well-known. However, many real applications, such as logistical operations in disaster situations or smart city announcements, do not have dedicated receivers. Instead, all nodes are potential receivers and users of the data. In this talk, I will present our latest results from implementing a novel concept of opportunistic data dissemination, where network flooding is restricted organically by the non-permanent interests and needs of the users. The approach is highly scalable and offers very good performance in realistic scenarios, where the “value” of individual data items is not known a-priori.

Bio:

Anna Förster is currently a Professor at the Universität Bremen, Germany leading the Sustainable Communication Networks group. She received her PhD in Computer Science from the University of Lugano in Switzerland in 2009 and her Master’s in Computer Science and Aerospace Engineering from the Freie Universität Berlin, Germany, in 2004. After her MSc graduation she spent a year as a junior consultant at the Business Technology Office of McKinsey & Company, before dedicating herself completely to research. Before joining the Universität Bremen in early 2015, she spent several years as researcher at the University of Applied Sciences of Southern Switzerland. Her research goal is to enable a sustainable future for computer networking. She is interested both in the usage of computer networking techniques for enabling sustainability and vice versa: enabling more sustainable and flexible computer networks. She is mainly concentrating on wireless networks, cyber physical systems and networks, internet of things, opportunistic networks and computing, massively distributed clouds, participatory sensing, wireless underground sensor networks, smart grids, etc. Furthermore, she is exploring the usage, further development, and adaption of machine learning techniques in these areas.