D5.2: Digital-Twin Enabled multi-physics simulation and model matching

This deliverable presents a report on the developed actions and results concerning Digital-Twin-enabled multi-physics simulations and model matching. Enabling meaningful simulations within new human-infrastructure interfaces such as Digital twins is paramount. Accessing the power of simulation opens manifold new ways for observation, understanding, analysis and prediction of numerous scenarios to which the asset may be faced. As a result, managers can access countless ways of acquiring synthetic data for eventually taking better, more informed decisions. The tool MatchFEM is conceived as a fundamental part of this endeavour. From a broad perspective, the tool is aimed at contextualizing information between multi-physics simulations and vaster information constructs such as digital twins. 3D geometries, measurements, simulations, and asset management coexist in such information constructs. This report provides guidance for the generation of comprehensive adequate initial conditions of the assets to be used during their life span using a DT basis. From a more specific focus, this deliverable presents a set of exemplary recommendations for the development of DT-enabled load tests of assets in the form of a white paper. The deliverable also belongs to a vaster suit of documents encountered in WP5 of the Ashvin project in which measurements, models and assessments are described thoroughly.Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

Performance Evaluation of Cognitive Radio Spectrum Sensing Techniques through a Rayleigh Fading Channel

In recent years, there has been a steep rise in the demand for bandwidth due to a sharp increase in the number of devices connected to the wireless network. Coupled with the expected commercialization of 5G services and massive adoption of IoT, the upsurge in the number of devices connected to the wireless network will continue to grow exponentially into billions of devices. To accommodate the associated demand for wireless spectrum as we step into this new era of wireless connectivity, traditional methods of spectrum utilization based on fixed and static allocation are no longer adequate. New innovative forms that support dynamic assignment of spectrum space on as-per-need basis are now paramount. Cognitive radio has emerged as one of the most promising techniques that allow flexible usage of the scarce spectrum resource. Cognitive radio allows unlicensed users to opportunistically access spectrum bands assigned to primary users when these spectrum bands are idle. As such, cognitive radio reduces the gap between spectrum scarcity and spectrum underutilization. The most critical function of cognitive radio is spectrum sensing, which establishes the occupation status of a spectrum band, paving the way for a cognitive radio to initiate transmission if the band is idle. The most common and widely used methods for spectrum sensing are energy detection, matched filter detection, cyclostationary feature detection and cooperative based spectrum sensing. This dissertation investigates the performance of these spectrum-sensing techniques through a Rayleigh fading channel. In a wireless environment, a Rayleigh fading channel models the propagation of a wireless signal where there is no dominant line of sight between the transmitter and receiver. Understanding the performance of spectrum sensing techniques in a real world simulation environment is important for both industry and academia, as this allows for the optimal design of cognitive radio systems capable of efficiently executing their function. MATLAB software provides an experimental platform for the fusion of various Rayleigh fading channel parameters that mimic real world wireless channel characteristics. In this project, a MATLAB environment test bed is used to simulate the performance for each spectrum sensing technique across a range of signal-to-noise values, through a Rayleigh fading channel with a given set of parameters for channel delay, channel gain and Doppler shift. Simulation results are presented as plots for probability of detection versus signal-tonoise ratio, receiver operating characteristics (ROC) curves and complementary ROC curves. A detailed performance analysis for each spectrum sensing technique then follows, with comparisons done to determine the technique that offers the best relative performance

Fusion of Information and Analytics: A Discussion on Potential Methods to Cope with Uncertainty in Complex Environments (Big Data and IoT)

International audienceInformation overload and complexity are core problems to most organizations of today. The advances in networking capabilities have created the conditions of complexity by enabling richer, real-time interactions between and among individuals, objects, systems and organizations. Fusion of Information and Analytics Technologies (FIAT) are key enablers for the design of current and future decision support systems to support prognosis, diagnosis, and prescriptive tasks in such complex environments. Hundreds of methods and technologies exist, and several books have been dedicated to either analytics or information fusion so far. However, very few have discussed the methodological aspects and the need of integrating frameworks for these techniques coming from multiple disciplines. This paper presents a discussion of potential integrating frameworks as well as the development of a computational model to evolve FIAT-based systems capable of meeting the challenges of complex environments such as in Big Data and Internet of Things (IoT)

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl

Review of Open Source Simulators in ICS/IIoT Security Context

In industrial control systems (ICS), simulation has found widespread use during system design and in tuning process control parameters or exploring the effects of new control algorithms. Simulation enables the assessment of performance at scale and allows research to be conducted by those with limited access to real physical infrastructures. However, as ICSs are often no longer isolated from other networks and the internet, hence are subject to security and safety issues, simulation is also required to understand the issues and their solution. To foster transparent, collaborative and cost-effective studies, demonstrations, and solution development, and attract the broadest interest base, simulation is indeed critical and Open Source is a good way to go since simulators in this category are less expensive to access, install, and use, and can be run with general purpose (non-proprietary) computing equipment and setups. Findings This research presents the following key findings: 1. A lot of Open Source simulation tools exist and span applications areas such as communications and sensor networks (C&WSNs), ICS/SCADA, and IIoT. 2. The functional structures and characteristics that appear common in Open Source simulators include: supported licence types, programming languages, operating systems platforms, user interface types, and available documentation and types. 3. Typical research around Open Source simulators is built around modelling, analysis and optimisation of operations in relations to factors such as flexibility, mobility, scalability, and active user support. No single Open Source simulator addresses all conceivable characteristics. While some are strong in specific contexts relative to their development, they are often weak in other purpose-based research capabilities, especially in the context of IoT. 4. Most of the reviewed Open Source tools are not designed to address security contexts. The few that address security such as SCADASim only consider very limited contexts such as testing and evaluating Denial-of-Service (DoS), Man-in-the-middle (Mitm), Eavesdropping, and Spoofing attacks. Recommendations The following key recommendations are presented: 1. Future developments of Open Source simulators (especially for IIoT) should explore the potential for functionalities that can enable the integration of diverse simulators and platforms to achieve an encompassing setup. 2. Developers should explore the capabilities of generic simulators towards achieving architectures with expansible capabilities into multi-class domains, support easier and faster modelling of complex systems, and which can attract varied users and contributors. 3. Functional characteristics such as; ease of use, degree of community acceptance and use, and suitability for industrial applications, should also be considered as selection and development criteria, and to emphasise simulator effectiveness. This can support consistency, credibility, and simulation system relevance within a domain that is continually evolving. 4. Future Open Source simulation projects developments should consider and adopt the more common structural attributes including; Platform Type, Open Source Licence Type, Programming Language, User Interfaces, Documentation, and Communication Types. These should be further complemented by appropriate editorial controls spanning quality coding, revision control and effective project disseminations and management, to boost simulation tool credibility and wide acceptance. 5. The range of publication dates (earliest to latest) for: citations, code commits, and number of contributors associated to Open Source simulator projects can also support the decision for interests and adoption of specific Open Source projects. 6. Research objectives for ICS/IIoT Open Source simulators should also include security performance and optimisation with considerations towards enhancing confidentiality, integrity and availability. 7. Further studies should explore the evaluation of security topics which could be addressed by simulation – more specifically, proposing how this may be achieved and identifying what can't be addressed by simulation. Investigations into simulation frameworks that can allow multi-mode simulations to be configured and operated are also required. Research into Industry 4.0 System-of-Systems (SoS) security evaluations, dependency, and cascading impacts method or analysis is another area of importanc

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

Privacy-preserving human mobility and activity modelling

The exponential proliferation of digital trends and worldwide responses to the COVID-19 pandemic thrust the world into digitalization and interconnectedness, pushing increasingly new technologies/devices/applications into the market. More and more intimate data of users are collected for positive analysis purposes of improving living well-being but shared with/without the user's consent, emphasizing the importance of making human mobility and activity models inclusive, private, and fair. In this thesis, I develop and implement advanced methods/algorithms to model human mobility and activity in terms of temporal-context dynamics, multi-occupancy impacts, privacy protection, and fair analysis. The following research questions have been thoroughly investigated: i) whether the temporal information integrated into the deep learning networks can improve the prediction accuracy in both predicting the next activity and its timing; ii) how is the trade-off between cost and performance when optimizing the sensor network for multiple-occupancy smart homes; iii) whether the malicious purposes such as user re-identification in human mobility modelling could be mitigated by adversarial learning; iv) whether the fairness implications of mobility models and whether privacy-preserving techniques perform equally for different groups of users. To answer these research questions, I develop different architectures to model human activity and mobility. I first clarify the temporal-context dynamics in human activity modelling and achieve better prediction accuracy by appropriately using the temporal information. I then design a framework MoSen to simulate the interaction dynamics among residents and intelligent environments and generate an effective sensor network strategy. To relieve users' privacy concerns, I design Mo-PAE and show that the privacy of mobility traces attains decent protection at the marginal utility cost. Last but not least, I investigate the relations between fairness and privacy and conclude that while the privacy-aware model guarantees group fairness, it violates the individual fairness criteria.Open Acces