Real-time systems need time-predictable platforms to allow static analysis of the worst-case execution time (WCET). Standard multi-core processors are optimized for the average case and are hardly analyzable. Within the T-CREST project we propose novel solutions for time-predictable multi-core architectures that are optimized for the WCET instead of the average-case execution time. The resulting time-predictable resources (processors, interconnect, memory arbiter, and memory controller) and tools (compiler, WCET analysis) are designed to ease WCET analysis and to optimize WCET performance. Compared to other processors the WCET performance is outstanding.The T-CREST platform is evaluated with two industrial use cases. An application from the avionic domain demonstrates that tasks executing on different cores do not interfere with respect to their WCET. A signal processing application from the railway domain shows that the WCET can be reduced for computation-intensive tasks when distributing the tasks on several cores and using the network-on-chip for communication. With three cores the WCET is improved by a factor of 1.8 and with 15 cores by a factor of 5.7.The T-CREST project is the result of a collaborative research and development project executed by eight partners from academia and industry. The European Commission funded T-CREST.

Large scale wildfire events that occurred around the world involved a massive loss of animal lives, with a consequent economic impact on agricultural holdings and damages to ecosystems. Preparing animals for a wildfire evacuation requires an extra level of planning, preparedness and coordination, which is missing in the current practice. This paper describes a conceptual framework of an ICT system implemented to support the activities of the Regional Veterinary referral Center for non-epidemic emergencies (CeRVEnE) in the Campania Region for the twofold objectives. On the one hand, it realizes the monitoring of the wooded areas under risk of fire in the so-called “Mount Vesuvius’ red zone”. On the other hand, it determines the OPtimal Evacuation Route for Animals (OPERA) in case of fire, for each of the reported animal species living in the mentioned red zone. The main innovation of the proposed system lies in its software architecture that aims at integrating a Distributed Sensor Network (DSN), an ad-hoc software to generate timely simulations for fire risk modeling, and a GIS (Geographic Information System) for both the activities of web mapping and OPERA definition. This paper shows some effective preliminary results of the system implementation. The importance of the system mainly lies in its accordance with the so-called “Foresight approach” perspective, that provides models and tools to guarantee the prevention of systematic failure in disaster risk management, and becomes moreover critical in the case of Mount Vesuvius, which hosts a unique combination of both animal and anthropic elements within a delicate natural ecosystem.

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The paper deals with the problem of designing and implementing a measurement node based on compressive sampling (CS). The considered node is tailored for wide area sensors networks aimed to carry out measurements in frequency domain. To this aim, the node takes advantage from some known or recently proposed CS features in such a way as to outperform the nominal specification of its data acquisition module. To make the spectrum estimation feasible on the node level, a suitable strategy for input signal random sampling and an efficient CS implementation, i.e. the greedy algorithms based on the so-called match-pursuit approach, are exploited. First tests are presented, related to a cost-effective microcontroller from STMicrocontroller, namely STM32F429ZI, characterized by a data memory depth sufficient to execute the agile computational scheme of the greedy algorithm. The estimated spectra concur with those obtained through standard discrete Fourier transform-based approaches, thus highlighting the feasibility of the proposed measurement node