Roberta Calegari

CTG: a connectivity trace generator for testing the performance of opportunistic mobile systems

The testing of the performance of opportunistic communication protocols and applications is usually done through simulation as i) deployments are expensive and should be left to the final stage of the development process, and ii) the number of varying parameters in thesesystems is so high that it would be very hard to conduct thorough testing of all the functionality within a single deployment. Therefore, protocols and applications are often plugged into mobility simulators to test their performance; however, until recently, most of the testing has been conducted with random mobility models which do not mirror reality. Furthermore, despite disconnections playing a veryprominent role in the performance of any opportunistic mobile system, most models do not really account for it. A different approach to testing is the use of real traces of movement collected in specific domains as test cases. These cases, however, do not allow for flexible performance testing, as they are specific for a given scenario withfixed connectivity properties. In this paper we propose the Connectivity Trace Generator (CTG), a tool for the automatic generation of connectivity traces, which takes as input real mobility traces and is able to output a set of traces with similar connectivity properties, which can be used as test cases. This allows developers to investigate the impact of the variation of connectivity patterns, number of hosts, and other parameters on the protocol or application under investigation. We use a real case study (the Dartmouth campus connectivity traces) to show how CTG allows protocol developers to play with some connectivity and density parameters so to best conduct performance testing of different aspects of protocols and applications.

Logic Programming as a Service (LPaaS): Intelligence for the IoT

The widespread diffusion of low-cost computing devices, such as Arduino boards and Raspberry Pi, along with improvements of Cloud computing platforms, are paving the way towards a whole new set of opportunities for Internet of Things (IoT) applications and services. Varying degrees of intelligence are often required for supporting adaptation and self-management—yet, they should be provided in a light-weight, easy to use and customise, highly-interoperable way. Accordingly, in this paper we explore the idea of Logic Programming as a Service (LPaaS) as a novel and promising re-interpretation of distributed logic programming in the IoT era. After introducing the reference context and motivating scenarios of LPaaS as a key enabling technology for intelligent IoT, we define the LPaaS general system architecture. Then, we present a prototype implementation built on top of the tuProlog system, which provides the required interoperability and customisation. We showcase the LPaaS potential through a case study designed as a simplification of the motivating scenarios.

The Butlers Framework for Socio-Technical Smart Spaces

Smart Spaces outline an intriguing application scenario where people are immersed in time and space in an augmented virtual environment, which suitably exploits ubiquitous computing technologies, space and time awareness, and pervasive intelligence. A number of technical, social, pragmatic challenges, arising from several perspectives and domains, need to be dealt with.

Context Reasoning and Prediction in Smart Environments: The Home Manager Case

In Smart Environments computing systems are ubiquitous, intelligence pervades the space, and people’s situatedness in time and space is exploited to provide a contextualised, adaptive user experience.