Authors: Andreas Dittrich Daniel Solis Herrera Pablo Coto Miroslaw Malek 20th IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2014, Singapore, November 18-21, 2014 Download: accepted version, final published version

Service Discovery (SD) is an integral part of service networks. Before a service can be used, it needs to be discovered successfully. Thus, a comprehensive service dependability analysis needs to consider the dependability of the SD process. As a time-critical operation, an important property of SD is responsiveness: the probability of successful discovery within a deadline, even in the presence of faults. This is especially true for dynamic networks with complex fault behavior such as wireless networks. We present results of a comprehensive responsiveness evaluation of decentralized SD, specifically active SD using the Zeroconf protocol. The ExCovery experiment framework has been employed in the Distributed Embedded System (DES) wireless testbed at Freie Universität Berlin. We present and discuss the experiment results and show how SD responsiveness is affected by the position and number of requesters and providers as well as the load in the network. Results clearly demonstrate that in all but the most favorable conditions, the configurations of current SD protocols struggle to achieve a high responsiveness. We further discuss results reflecting the long-term behavior of the testbed and how its varying reliability impacts SD responsiveness.

	Authors: Jelena Milosevic Andreas Dittrich Alberto Ferrante Miroslaw Malek Camilo Rojas Quiros Rubén Braojos Giovanni Ansaloni David Atienza 41st Computing in Cardiology Conference, CinC 2014, Cambridge, MA, USA, September 7-10, 2014 Download: author version, final published version

We present a methodology for identifying patients who have experienced Paroxysmal Atrial Fibrillation (PAF) among a given subjects population. Our work is intended as an initial step towards the design of an unobtrusive system for concurrent detection and monitoring of chronic cardiac conditions.

Our methodology comprises two stages: off-line training and on-line analysis. During training the most significant features are selected using machine-learning methods, without relying on a manual selection based on previous knowledge. Analysis is based on two phases: feature extraction and detection of PAF patients. Light-weight algorithms are employed in the feature extraction phase, allowing the on-line implementation of this step on wearable and resource-constrained sensor nodes. The detection phase employs techniques borrowed from the field of failure prediction. While these algorithms have found extensive applications in diverse scenarios, their application to automated cardiac analysis has not been sufficiently investigated.

Obtained results, in terms of performance, are comparable to similar efforts in the field. Nonetheless, the proposed method employs computationally simpler and more efficient algorithms, which are compatible with the computational constraints of state-of-the-art body sensor nodes.