the device actually in use. Due to such premises, traditional software design methods need to be extended, and new issues and requirements need to be addressed for supporting context-aware access to services and applications. In this paper we propose a model-driven approach towards adaptive, context

Abstract not found

In this article, we present a performance engineering enhanced modelling methodology for designing embedded devices and describe the experiences we have gained in applying this methodology during the design of a DVB-H enabled handheld device. The methodol-ogy uses UML 2.0 to model the system

ABSTRACT Internet-of-Things (IoT) devices, especially sensors are producing large quantities of data that can be used for gathering knowledge. In this field, machine learning technologies are increasingly used to build versatile data-driven models. In this paper, we present a novel architecture

the above in the general case, existing approaches for IoT application development only cover limited subsets of above mentioned challenges. In this paper, we propose a multi-stage model-driven approach for IoT application development based on a precise definition of the role to be played by each

Ubiquitous Computing has enabled users the easy access to services and applications from anywhere, at anytime, and using any device. In this scenario, software development presents a number of challenges, among them the need for adapting applications to different access devices. Each device has a

Digitising statistics handling through

-by-step fashion and then used to predict the system perfor-mance under various workload and configuration scenar-ios. After the case study, we present a set of generic per-formance modeling patterns that can be used as build-ing blocks when modeling message-oriented event-driven systems. The results demonstrate

First, I would like to thank to Dimka Karastoyanova and Mariano Cilia for all the fruitful discussions, their guidance and support through the course of this project. Thanks also to Prof. Alejandro Buchmann who sponsored my stay at Technische Universität Darmstadt during the spring of 2004. The special thanks go to my advisor, Mariano Cilia, who has made all this possible. Finally, I’d like to thank to my family for all these years of support.

This thesis presents CraneTracker, a multi-modal sensing and communication system for monitoring migratory species at the continental level. By exploiting the robust and extensive cellular infrastructure across the continent, traditional mobile wireless sensor networks can be extended to enable