http://dl.gi.de/handle/20.500.12116/18976 2026-07-22T22:10:54Z http://dl.gi.de/handle/20.500.12116/19000 Identifying good connections in an unstructured peer-to-peer network: the case of Gnutella Babin, Gilbert; Larocque, Denis; Dine, Abdessamad Eichler, Gerald; Küpper, Axel; Schau, Volkmar; Fouchal, Hacène; Unger, Herwig; Eichler, Gerald; Küpper, Axel; Schau, Volkmar; Fouchal, Hacène; Unger, Herwig Past studies have shown the unstability of unstructured peer-to-peer networks and in particular Gnutella. Because of this unstability, queries in these networks are inefficient. Furthermore, to keep the network stable, the protocols used require huge bandwidth usage. One approach proposed by Gnutella conceptors is to create two classes of servents: leaf nodes and ultrapeers. The core of the network is preserved by the ultrapeers, while the leaf nodes remain at the periphery. Others suggest to use random walks rather than flooding. We also see suggestions to build communities to reduce traffic. In this paper, we investigate the possibility to use a different, yet complementary approach, where a node would only keep connections that it deems “good”. Based on statistics collected on the Gnutella network, we show that it is possible to rapidly determine whether a connection is good or not. The detection of good connections uses two criteria: the duration of connection establishment time and the number of messages received through the connection. 2011-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/19002 On using a distributed approach for help in medical diagnosis with wireless sensor networks Dessart, Nathalie; Fouchal, Hacène; Hunel, Philippe; Vidot, Nicolas Eichler, Gerald; Küpper, Axel; Schau, Volkmar; Fouchal, Hacène; Unger, Herwig; Eichler, Gerald; Küpper, Axel; Schau, Volkmar; Fouchal, Hacène; Unger, Herwig In this paper, we focus on providing new tools to help doctors in their diagnosis. We study the feasibility of using a distributed approach over Wireless Sensor Networks (WSN). We define a Wireless Body Area Network (WBAN) as a set of wireless sensors which equipped a patient. Each sensor senses a health parameter, for example, temperature or heart pulse. Our aim is to understand how a distributed approach can be a fair alternative to the common centralized paradigm. We study a distributed approach based on the token paradigm. Then, we compare this approach to the centralized one, throw simulations and experimentations over real sensors. 2011-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/19001 Challenges in designing technology for reducing the need for synchronous communication in the perioperative environment Wienhofen, Leendert W. M.; Toussaint, Pieter J. Eichler, Gerald; Küpper, Axel; Schau, Volkmar; Fouchal, Hacène; Unger, Herwig; Eichler, Gerald; Küpper, Axel; Schau, Volkmar; Fouchal, Hacène; Unger, Herwig This paper describes health informatics as a field of research and some of the challenges that are faced when designing a complex event processing middleware gathering input from multiple legacy sources in a hospital environment. The goal of the system is to reduce the need for synchronous communication between medical stakeholders by creating a situational awareness and thereby cause fewer interruptions. 2011-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/18999 Predicting social networks in weblogs Jähnichen, Patrick Eichler, Gerald; Küpper, Axel; Schau, Volkmar; Fouchal, Hacène; Unger, Herwig; Eichler, Gerald; Küpper, Axel; Schau, Volkmar; Fouchal, Hacène; Unger, Herwig Weblogs and other platforms used to organize a social life online have achieved an enormous success over the last few years. Opposed to applications directly designed for building up and visualizing social networks, weblogs are comprised of mostly unstructured text data, that comes with some meta data, such as the author of the text, its publication date or the URL it is available under. In this paper, we propose a way, how these networks may be inferred not by the available meta data, but by pure natural language analysis of the text content, allowing inference of these networks without any meta data at hand. We discuss results of first experiments and outline possible enhancements as well as other ways to improve prediction of social networks based solely on content analysis. 2011-01-01T00:00:00Z