The interconnected nature of the Industry 4.0-driven operations, combined with the rapid pace of digital transformation, poses a unique challenge in terms of cybersecurity, impacting businesses and their continuity and, thus, in citizens’ daily lives. The SecureIoT project introduced an open standards-based framework for IoT (Internet of Things) security, which enables IoT solution developers, integrators, and deployers to integrate and deploy secure IoT services that span multiple IoT platforms and involve networks of smart objects with embedded intelligence and semi-autonomous behavior. In addition to the implementation of the technical requirements, however, these primarily serve to provide work assistance to the people deployed in production, starting with safe and optimized processes of operators and machine setters, involving different levels within the production chain. Here, SecureIoT supports humans in adhering to the Key Performance Indicators (KPIs) of intime, in-quality, and in-cost maxims amidst the diversity of industrial complexity. This is exemplified by automated Machine-2-Machine (M2M) show-offs being controlled at new levels of trustworthiness, which are usually only applied to human-2-human communication. With this type of control, humans can provide a higher level of trust to the agile resource-driven automated decision-making processes for manufacturing and product security decisions. This way, SecureIoT contributes to the principles of human-centric industry and toward a secure and resilient society.

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.

New technologies have been progressively integrated into vehicles during the last thirty years. Commercial vehicles today can be seen as 2 tonne IoT devices on wheels that continuously collect high-quality information not only from the internal performance and behaviour of the vehicle but also from the external environment. The adoption of cutting-edge technologies like 5G, Edge Computing and Artificial Intelligence (AI) will be essential pillars for the actual implementation of new Intelligent Transportation Systems (ITS) leveraging Vehicle-to-everything (V2X) paradigm. This paper is focused on the design and implementation of a connected vehicle-based system to enable new services and applications to be developed, by exploiting high-quality data collected from onboard sensors and ECUs and leveraging state-of-the-art machine learning technologies.

To increase security and to offer user experiences according to the requirements of a hyper-connected world, modern vehicles are integrating complex electronic systems, being transformed into systems of Cyber-Physical Systems (CPS). While a great diversity of heterogeneous hardware and software components must work together and control in real-time crucial functionalities, cybersecurity for the automotive sector is still in its infancy. This paper provides an analysis of the most common vulnerabilities and risks of connected vehicles, using a real example based on industrial and market-ready technologies. Several components have been implemented to inject and simulate multiple attacks, which enable security services and mitigation actions to be developed and validated.

Even though connectivity services have been introduced in many of the most recent car models, access to vehicle data is currently limited due to its proprietary nature. The European project AutoMat has therefore developed an open Marketplace providing a single point of access for brand-independent vehicle data. Thereby, vehicle sensor data can be leveraged for the design and implementation of entirely new services even beyond traffic-related applications (such as hyper-local traffic forecasts). This paper presents the architecture for a Vehicle Big Data Marketplace as enabler of cross-sectorial and innovative vehicle data services. Therefore, the novel Common Vehicle Information Model (CVIM) is defined as an open and harmonized data model, allowing the aggregation of brand-independent and generic data sets. Within this work the realization of a prototype CVIM and Marketplace implementation is presented. The two use-cases of local weather prediction and road quality measurements are introduced to show the applicability of the AutoMat concept and prototype to non-automotive applications.

Nowadays vehicles have evolved from a mean of transport to something much more complex and powerful. The paradigm change that electric vehicles have brought requires new tools and methodologies for improving not only the way vehicles behave, but also all the elements that are influenced by them. Moreover, electric vehicle are also connected to the Internet, thus they are able to access to a large repository of information and also to contribute to emerging opportunities that should be exploited. The application of Internet of Things and Big Data technologies in electric vehicles enables the development of new methodologies for offering personalised energy efficiency advices. JOSPEL aims at exploiting all these possibilities making them tangible through an energy consumption reduction reached by means of the provision of different services and improving the physical elements in the vehicle. This paper presents the ICT architecture that enables the achievement of those objectives by the digitalisation and analysis of vehicle information, and also to expose existing vehicle resources for achieving other benefits in different sectors.

The Internet of Things (IoT) presents itself as a promising set of key technologies to provide advanced smart applications. IoT has become a major trend lately and smart solutions can be found in a large variety of products. Since it provides a flexible and easy way to gather data from huge numbers of devices and exploit them ot provide new applications, it has become a central research area lately. However, due to the fact that IoT aims to interconnect millions of constrained devices that are monitoring the everyday life of people, acting upon physical objects around them, the security and privacy challenges are huge. Nevertheless, only lately the research focus has been on security and privacy solutions. Many solutions and IoT frameworks have only a minimum set of security, which is a basic access control. The EU FP7 project RERUM has a main focus on designing an IoT architecture based on the concepts of Security and Privacy by design. A central part of RERUM is the implementation of a middleware layer that provides extra functionalities for improved security and privacy. This work, presents the main elements of the RERUM middleware, which is based on the widely accepted OpenIoT middleware.

In this work, an electronic tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) for the discrimination of different commercial beer types is presented. The array was formed by 21 ISEs combining both cationic and anionic sensors with others with generic response. For this purpose beer samples were analyzed with the ET without any pretreatment rather than the smooth agitation of the samples with a magnetic stirrer in order to reduce the foaming of samples, which could interfere into the measurements. Then, the obtained responses were evaluated using two different pattern recognition methods, principal component analysis (PCA), which allowed identifying some initial patterns, and linear discriminant analysis (LDA) in order to achieve the correct recognition of sample varieties (81.9% accuracy). In the case of LDA, a stepwise inclusion method for variable selection based on Mahalanobis distance criteria was used to select the most discriminating variables. In this respect, the results showed that the use of supervised pattern recognition methods such as LDA is a good alternative for the resolution of complex identification situations. In addition, in order to show an ET quantitative application, beer alcohol content was predicted from the array data employing an artificial neural network model (root mean square error for testing subset was 0.131 abv).

A Differential Mobility Analyser (DMA) is a specific configuration of an Ion Mobility Spectrometer (IMS) where ions with different electrical mobilities are separated in space, instead of in time of drift, as in classical drift-time IMS. This work presents results obtained with a parallel plate DMA instrument, with crucial differences in the sheath flow and the detection system when compared to other instruments in the market. These differences improve the resolving powers and sensitivities of the instrument. Additionally, datasets from IMS or DMA instruments are typically processed with univariate techniques when only qualitative detection is of interest. However, good performance in quantitative measurements can be achieved using multivariate data processing. This work presents for the first time, measurements with a stand-alone DMA instrument and the multivariate data processing related to VOCs and environmentally interesting samples.

In this work, an electronic tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) for the discrimination of different commercial beer types is presented. The array was formed by 21 ISEs combining both cationic and anionic sensors with others with generic response. For this purpose beer samples were analyzed with the ET without any pretreatment rather than the smooth agitation of the samples with a magnetic stirrer in order to reduce the foaming of samples, which could interfere into the measurements. Then, the obtained responses were evaluated using two different pattern recognition methods, principal component analysis (PCA), which allowed identifying some initial patterns, and linear discriminant analysis (LDA) in order to achieve the correct recognition of sample varieties (81.9% accuracy). In the case of LDA, a stepwise inclusion method for variable selection based on Mahalanobis distance criteria was used to select the most discriminating variables. In this respect, the results showed that the use of supervised pattern recognition methods such as LDA is a good alternative for the resolution of complex identification situations. In addition, in order to show an ET quantitative application, beer alcohol content was predicted from the array data employing an artificial neural network model (root mean square error for testing subset was 0.131 abv).

In this article, we present the work-in-progress of the EU FP7 PHARAON project, started in September 2011. The first objective of the project is the development of new techniques and tools capable to assist the designer in the development of parallel embedded systems, from executable specifications to target-specific implementation and debugging on a multicore platform. This tool chain will offer and implement several parallelization strategies, reflecting the functional and non-functional constraints of the system, and driving the designer into incremental parallelization and adaptation steps. The second objective of the project is to develop monitoring and control techniques in the middleware of the system capable to automatically adapt platform services to application requirements and therefore reduce power consumption transparently.

This paper presents a framework which, starting from a UML/MARTE model of the embedded system, relies on an enhanced IP-XACT description of the platform for the automatic generation of fast performance executable models. The IP-XACT description of the HW architecture is automatically generated from the UML/MARTE model. The enhancement proposed extends the current capabilities of the IP-XACT standard in order to add semantic information to the HW architecture and to support the integration of Hardware Dependent Software (HdS). This way, HW and SW aspects of the integration of a component in a virtual platform model are covered. The applicability of the proposed extensions is shown by supporting the proposed IP-XACT descriptions at the front-end of a simulation infrastructure suited for fast performance assessment, and for supporting design space exploration.

Background: Sepsis Sepsis is one of the main causes of death in adult intensive care units. The major drawbacks of the different methods used for its diagnosis and monitoring are their inability to provide fast responses and unsuitability for bedside use. In this study, performed using a rat sepsis model, we evaluate breath analysis with Ion Mobility Spectrometry (IMS) as a fast, portable and non-invasive strategy.Methods: This study was carried out on 20 Sprague-Dawley rats. Ten rats were injected with lipopolysaccharide from Escherichia colt and ten rats were IP injected with regular saline. After a 24-h period, the rats were anaesthetized and their exhaled breaths were collected and measured with IMS and SPME-gas chromatography/mass spectrometry (SPME-GC/MS) and the data were analyzed with multivariate data processing techniques.Results: The SPME-GC/MS dataset processing showed 92% accuracy in the discrimination between the two groups, with a confidence interval of between 90.9% and 92.9%. Percentages for sensitivity and specificity were 98% (97.5-98.5%) and 85% (84.6-87.6%), respectively. The IMS database processing generated an accuracy of 99.8% (99.7-99.9%), a specificity of 99.6% (99.5-99.7%) and a sensitivity of 99.9% (99.8-100%).Conclusions: IMS involving fast analysis times, minimum sample handling and portable instrumentation can be an alternative for continuous bedside monitoring. IMS spectra require data processing with proper statistical models for the technique to be used as an alternative to other methods. These animal model results suggest that exhaled breath can be used as a point-of-care tool for the diagnosis and monitoring of sepsis. (C) 2011 Elsevier By. All rights reserved.

In this work, a new methodology to analyze spectra time-series obtained from ion mobility spectrometry (IMS) has been investigated. The proposed method combines the advantages of multivariate curve resolution-alternating least squares (MCR-ALS) for an optimal physical and chemical interpretation of the system (qualitative information) and a multivariate calibration technique such as polynomial partial least squares (poly-PLS) for an improved quantification (quantitative information) of new samples. Ten different concentrations of 2-butanone and ethanol were generated using a volatile generator based on permeation tubes. The different concentrations were measured with IMS. These data present a non-linear behavior as substance concentration increases. Although MCR-ALS is based on a bilinear decomposition, non-linear behavior can be modeled by adding new components to the model. After spectral pre-processing, MCR-ALS was applied aiming to get information about the ionic species that appear in the drift tube and their evolution with the analyte concentration. By resolving the IMS data matrix, concentration profiles and pure spectra of the different ionic species have been obtained for both analytes. Finally, poly-PLS was used in order to build a calibration model using concentration profiles obtained from MCR-ALS for ethanol and 2-butanone. The results, with more than 99% of explained variance for both substances, show the feasibility of using MCR-ALS to resolve IMS datasets. Furthermore, similar or better prediction accuracy is achieved when concentration profiles from MCR-ALS are used to build a calibration model (using poly-PLS) compared to other standard univariate and multivariate calibration methodologies.

The off-flavor of "tainted wine" is attributed mainly to the presence of 2,4,6-trichloroanisole (2,4,6-TCA) in the wine. In the present study the atmospheric pressure gas-phase ion chemistry, pertaining to ion mobility spectrometry, of 2,4,6-trichloroanisole was investigated. In positive ion mode the dominant species is a monomer ion with a lower intensity dimer species with reduced mobility values (K(0)) of 1.58 and 1.20 cm(2)V(-1) s(-1), respectively. In negative mode the ion with K(0) =1.64 cm(2)V(-1)s(-1) is ascribed to a trichlorophenoxide species while the ions with K(0) =1.48 and 1.13 cm(2)V(-1)s(-1) are attributed to chloride attachment adducts of a TCA monomer and dimer, respectively. The limit of detection of the system for 2,4,6-TCA dissolved in dichloromethane deposited on a filter paper was 2.1 μg and 1.7 ppm in the gas phase. In ethanol and in wine the limit of detection is higher implying that pre-concentration and pre-separation are required before IMS can be used to monitor the level of TCA in wine.

The continuous advance in technology enables the design of more complex embedded systems making use of increasingly powerful Multi-Processing Systems-on-Chip. These new systems can be used in new applications that require increasing performance with lower cost and power consumption and higher reliability. Finding the right compromise among these contradictory constraints makes initial architectural design decisions crucial as they may compromise the quality of the final implementation. In Multi-Processing Systems-on-Chip design, the analysis of chip temperature is becoming increasingly important due to its impact on system reliability, power consumption and cost. In this paper, we propose a complete framework based on native simulation for early estimation of power consumption and thermal flow in Multi-Processing Systems-on-Chip. Native simulation enables fast modeling and simulation of the embedded software running on a specific platform in close interaction with all the platform components. The activity of each component is monitored by using high-level models, which enable the estimation of the power consumed. The power figures feed a high-level Multi-Processing Systems-on-Chip thermal model which supplies chip temperature estimations. At the same time, advanced techniques to manage power and temperature have been modeled. Thus, the proposed framework enables the detection of power and thermal hot spots in the first stages of the design flow, allowing the exploration of different alternatives to address these problems. The framework has been validated by comparing the results provided with an Instruction Set Simulator. The results show around 2% error in the estimations, while simulation time is speeded up three orders of magnitude. Different examples have been developed to demonstrate the capabilities of the proposed methodology in a variety of cases.

A first step towards the multidetection, identification and quantification of anabolic androgenic steroids by enzyme-linked immunosorbent assay (ELISA) has been performed in this study. This proposal combines multiple competitive ELISA assays with different cross-reactivity profiles and multivariate data analysis techniques. Data have been analyzed by principal component analysis in conjunction with a novel K-nearest line classifier. This proposal allows simultaneous detection of up to four different steroids in the range of concentration from 0.1 to 316.2 nM with a total rate of 90.6% of correct detection, even in the presence of cross-reactivities. A methodology for concentration prediction is also presented with satisfactory results.