TrustE-VC: Trustworthy Evaluation Framework for Industrial Connected Vehicles in the Cloud

The integration between cloud computing and vehicular ad hoc networks, namely, vehicular clouds (VCs), has become a significant research area. This integration was proposed to accelerate the adoption of intelligent transportation systems. The trustworthiness in VCs is expected to carry more computing capabilities that manage large-scale collected data. This trend requires a security evaluation framework that ensures data privacy protection, integrity of information, and availability of resources. To the best of our knowledge, this is the first study that proposes a robust trustworthiness evaluation of vehicular cloud for security criteria evaluation and selection. This article proposes three-level security features in order to develop effectiveness and trustworthiness in VCs. To assess and evaluate these security features, our evaluation framework consists of three main interconnected components: 1) an aggregation of the security evaluation values of the security criteria for each level; 2) a fuzzy multicriteria decision-making algorithm; and 3) a simple additive weight associated with the importance-performance analysis and performance rate to visualize the framework findings. The evaluation results of the security criteria based on the average performance rate and global weight suggest that data residency, data privacy, and data ownership are the most pressing challenges in assessing data protection in a VC environment. Overall, this article paves the way for a secure VC using an evaluation of effective security features and underscores directions and challenges facing the VC community. This article sheds light on the importance of security by design, emphasizing multiple layers of security when implementing industrial VCsThis work was supported in part by the Ministry of Education, Culture, and Sport, Government of Spain under Grant TIN2016-76373-P, in part by the Xunta de Galicia Accreditation 2016–2019 under Grant ED431G/08 and Grant ED431C 2018/2019, and in part by the European Union under the European Regional Development FundS

Collaborative and intelligent networks and decision systems and services for supporting engineering and production management

Collaborative networks and systems (CNS) have received much attention in recent decades to reach a competitive advantage [...]This work was supported by national funds through the FCT-Fundacao para a Ciencia e Tecnologia, through the R&D Units Project Scopes: UIDB/00319/2020, UIDB/50014/2020, and EXPL/EME-SIS/1224/2021

Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge

AbstractThe Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture.</jats:p

Examples of trends in water management systems under influence of modern technologies

Dobivanje pouzdanih i pravovremenih informacija o trenutačnom i o budućem stanju voda omogućava učinkovito upravljanje vodnogospodarskim sustavima. U ovom se radu prikazuju prednosti i izazovi primjene naprednih tehnologija pri prikupljanju, obradi i integraciji podataka unutar nekoliko primjera sustava gospodarenja vodama. Pokazuje se kako napredne tehnologije imaju izraženu učinkovitost u preciznom praćenju različitih fenomena okoliša, u povećanju sigurnosti vodnih resursa i objekata te omogućavaju smanjenje potrošnje vode i energije uz povećanje kvalitete vode.Reliable and timely information about the current and future condition of water enables an efficient management of water management systems. Advantages and challenges of the use of modern technologies in the collection, analysis, and integration of data, are presented in this paper by means of several examples of water management systems. It is shown how advanced technologies demonstrate a pronounced efficiency in accurate monitoring of various environmental phenomena and in increasing safety of water resources and facilities, while also enabling low water and energy consumption, with simultaneous increase in water quality

Geospatial Information Research: State of the Art, Case Studies and Future Perspectives

Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future

The internet of things in healthcare

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Διοίκηση Επιχειρήσεων (ΜΒΑ)

Modern SIEM Analysis and Critical Requirements Definition in the Context of Information Warfare

Today Security Information and Event Management (SIEM) systems are used to prevent information loss in computer systems and networks. There are many approaches to SIEM realization. This paper is devoted to the analysis of existing SIEM and their characteristics in accordance with international standards and specifications, as well as a comparative description of their capabilities and differences, advantages and disadvantages. These results will be used in research project realization devoted to open source SIEM development and implementation in critical infrastructure to improve the cybersecurity level in the context of information warfare and cyber threats realization

An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge

The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained devices populating the Internet of Things. The proliferation of such applications (e.g., critical monitoring in smart cities) demands new strategies to make these systems also sustainable from an energetic point of view. In this paper, we present an energy-aware approach for the design and deployment of self-adaptive AI-based applications that can balance application objectives (e.g., accuracy in object detection and frames processing rate) with energy consumption. We address the problem of determining the set of configurations that can be used to self-adapt the system with a meta-heuristic search procedure that only needs a small number of empirical samples. The final set of configurations are selected using weighted gray relational analysis, and mapped to the operation modes of the self-adaptive application. We validate our approach on an AI-based application for pedestrian detection. Results show that our self-adaptive application can outperform non-adaptive baseline configurations by saving up to 81\% of energy while loosing only between 2% and 6% in accuracy