Predicting failures and acting proactively have a potential to improve availability as a correct prediction and a successful mitigation may bring a reward resulting in decrease of downtime and availability improvement. But, conversely, each incorrect prediction may introduce additional downtime (penalty). Therefore, depending on the quality of prediction and the system parameters, predictive fault-tolerance methods may improve or may degrade availability in comparison to the reactive ones. We first derive taxonomies of fault-tolerant techniques and policies to differentiate between reactive and proactive policies that are further classified as systematic and predictive. To evaluate whether a predictive policy improves availability or not, we derive an analytical model for availability quantification. We use Markov chains to extend steady-state availability equation to include: precision and recall, penalty and reward, mitigation success probability and potential failure rate increase due to the prediction load. We also derive an A-measure to optimize failure prediction for increasing availability. In our conclusion, precision and recall have comparable impact on availability as changing MTTF and MTTR. To validate the model we also simulate and analyze availability of a virtualized server with exponential distribution of failure and repair rates.

With Smart Grids efficiency of the electric power delivery service can be significantly increased by using advanced ICT infrastructure. The looming danger of merging two critical infrastructures, ICT and electric power, is that dependability may be compromised due to interdependencies and different approaches to dependability in the two communities. This calls for a unified approach to dependability which may be achieved by viewing Smart Grids as cyber-physical systems. We analyze and compare definitions and figures of merit used in ICT and electric power systems' communities to provide the basis for dependability analysis of Smart Grids. We propose a taxonomy of faults for Smart Grids by examining a large set of previous power system outages. Our work relies on the analysis of relevant events from the past in an attempt to understand present dependability state of Smart Grids and pave the way for proactive grid management.

Designing an embedded system is a complex process that involves working on both hardware and software. The first step in the design process is defining functional and non-functional requirements; among them, it is fundamental to also consider security. We propose an effective way for designers to specify security requirements starting from User Security Requirements. User Security Requirements are high-level requirements related to security attacks that the system should be able to withstand. We also provide a mechanism to automatically translate these User Requirements into System Security Requirements, that include a detailed description of security solutions. For expressing requirements we use Unified Modeling Language (UML); specifically, we create a UML profile to describe user requirements and we use model-to-model transformation to automatically generate system requirements. We show the effectiveness of the modeling scheme and of the translation mechanism by applying our methodology to a case study based on wearable devices for e-health monitoring.

Design of complex systems involving a number of experts from various fields necessarily includes modeling at different levels of abstraction. Modeling is particularly important in the initial phase of a joint project when all system requirements and constraints have to be clearly defined and understood by all the partners. Once an unambiguous structural model has been achieved and components interfaces fixed, design of components can be done independently. For the initial phase, a very simplified modeling methodology based on UML that can be easily understood and applied has been proposed. The application of the methodology has been presented through the design of full structural model of the infrastructure for precision farming. Modeled infrastructure is an open ICT hardware-software solution based on ISOBUS specification, that enables partial automation of tractors increasing safety and production efficiency. Model achieved through several steps presents a mutual understanding platform between the partners. Most importantly, a precise model has been achieved without the necessity of in deep study of UML by all the partners.