Pervasive Monitoring - An Intelligent Sensor Pod Approach for Standardised Measurement Infrastructures

Geo-sensor networks have traditionally been built up in closed monolithic systems, thus limiting trans-domain usage of real-time measurements. This paper presents the technical infrastructure of a standardised embedded sensing device, which has been developed in the course of the Live Geography approach. The sensor pod implements data provision standards of the Sensor Web Enablement initiative, including an event-based alerting mechanism and location-aware Complex Event Processing functionality for detection of threshold transgression and quality assurance. The goal of this research is that the resultant highly flexible sensing architecture will bring sensor network applications one step further towards the realisation of the vision of a “digital skin for planet earth”. The developed infrastructure can potentially have far-reaching impacts on sensor-based monitoring systems through the deployment of ubiquitous and fine-grained sensor networks. This in turn allows for the straight-forward use of live sensor data in existing spatial decision support systems to enable better-informed decision-making.Seventh Framework Programme (European Commission) (FP7 project GENESIS no. 223996)Austria. Federal Ministry of Transport, Innovation and TechnologyERA-STAR Regions Project (G2real)Austria. Federal Ministry of Science and Researc

Cognitive Hyperconnected Digital Transformation

Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business

Design and evaluation of a scalable Internet of Things backend for smart ports

Internet of Things (IoT) technologies, when adequately integrated, cater for logistics optimisation and operations' environmental impact monitoring, both key aspects for today's EU ports management. This article presents Obelisk, a scalable and multi-tenant cloud-based IoT integration platform used in the EU H2020 PortForward project. The landscape of IoT protocols being particularly fragmented, the first role of Obelisk is to provide uniform access to data originating from a myriad of devices and protocols. Interoperability is achieved through adapters that provide flexibility and evolvability in protocol and format mapping. Additionally, due to ports operating in a hub model with various interacting actors, a second role of Obelisk is to secure access to data. This is achieved through encryption and isolation for data transport and processing, respectively, while user access control is ensured through authentication and authorisation standards. Finally, as ports IoTisation will further evolve, a third need for Obelisk is to scale with the data volumes it must ingest and process. Platform scalability is achieved by means of a reactive micro-services based design. Those three essential characteristics are detailed in this article with a specific focus on how to achieve IoT data platform scalability. By means of an air quality monitoring use-case deployed in the city of Antwerp, the scalability of the platform is evaluated. The evaluation shows that the proposed reactive micro-service based design allows for horizontal scaling of the platform as well as for logarithmic time complexity of its service time

Cognitive Hyperconnected Digital Transformation

Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business

IoT-enabled smart cities: a hybrid systematic analysis of key research areas, challenges, and recommendations for future direction

Cities are expected to face daunting challenges due to the increasing population in the near future, putting immense strain on urban resources and infrastructures. In recent years, numerous studies have been developed to investigate different aspects of implementing IoT in the context of smart cities. This has led the current body of literature to become fairly fragmented. Correspondingly, this study adopts a hybrid literature review technique consisting of bibliometric analysis, text-mining analysis, and content analysis to systematically analyse the literature connected to IoT-enabled smart cities (IESCs). As a result, 843 publications were selected for detailed examination between 2010 to 2022. The findings identified four research areas in IESCs that received the highest attention and constituted the conceptual structure of the field. These include (i) data analysis, (ii) network and communication management and technologies, (iii) security and privacy management, and (iv) data collection. Further, the current body of knowledge related to these areas was critically analysed. The review singled out seven major challenges associated with the implementation of IESCs that should be addressed by future studies, including energy consumption and environmental issues, data analysis, issues of privacy and security, interoperability, ethical issues, scalability and adaptability as well as the incorporation of IoT systems into future development plans of cities. Finally, the study revealed some recommendations for those interconnected challenges in implementing IESCs and effective integrations within policies to support net-zero futures

Networked world: Risks and opportunities in the Internet of Things

The Internet of Things (IoT) – devices that are connected to the Internet and collect and use data to operate – is about to transform society. Everything from smart fridges and lightbulbs to remote sensors and cities will collect data that can be analysed and used to provide a wealth of bespoke products and services. The impacts will be huge - by 2020, some 25 billion devices will be connected to the Internet with some studies estimating this number will rise to 125 billion in 2030. These will include many things that have never been connected to the Internet before. Like all new technologies, IoT offers substantial new opportunities which must be considered in parallel with the new risks that come with it. To make sense of this new world, Lloyd’s worked with University College London’s (UCL) Department of Science, Technology, Engineering and Public Policy (STEaPP) and the PETRAS IoT Research Hub to publish this report. ‘Networked world’ analyses IoT’s opportunities, risks and regulatory landscape. It aims to help insurers understand potential exposures across marine, smart homes, water infrastructure and agriculture while highlighting the implications for insurance operations and product development. The report also helps risk managers assess how this technology could impact their businesses and consider how they can mitigate associated risks

Explainable Predictive Maintenance

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.Comment: 51 pages, 9 figure

Multi-objective optimisation of aircraft flight trajectories in the ATM and avionics context

The continuous increase of air transport demand worldwide and the push for a more economically viable and environmentally sustainable aviation are driving significant evolutions of aircraft, airspace and airport systems design and operations. Although extensive research has been performed on the optimisation of aircraft trajectories and very efficient algorithms were widely adopted for the optimisation of vertical flight profiles, it is only in the last few years that higher levels of automation were proposed for integrated flight planning and re-routing functionalities of innovative Communication Navigation and Surveillance/Air Traffic Management (CNS/ATM) and Avionics (CNS+A) systems. In this context, the implementation of additional environmental targets and of multiple operational constraints introduces the need to efficiently deal with multiple objectives as part of the trajectory optimisation algorithm. This article provides a comprehensive review of Multi-Objective Trajectory Optimisation (MOTO) techniques for transport aircraft flight operations, with a special focus on the recent advances introduced in the CNS+A research context. In the first section, a brief introduction is given, together with an overview of the main international research initiatives where this topic has been studied, and the problem statement is provided. The second section introduces the mathematical formulation and the third section reviews the numerical solution techniques, including discretisation and optimisation methods for the specific problem formulated. The fourth section summarises the strategies to articulate the preferences and to select optimal trajectories when multiple conflicting objectives are introduced. The fifth section introduces a number of models defining the optimality criteria and constraints typically adopted in MOTO studies, including fuel consumption, air pollutant and noise emissions, operational costs, condensation trails, airspace and airport operations

Robot CeDRI 2023: sub-system integration and health dashboard

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáWith the constant increase in the volume of data generated and collected in several areas, data visualization has become more relevant to improve equipment management, reduce operational costs and increase process efficiency. This paper proposes developing a health monitoring system for an Autonomous Mobile Robots (AMR) equipment, which allows data acquisition and analysis for decision-making performed autonomously and by the equipment manager. Implementing the proposed system demonstrated favourable results in data acquisition, analysis, and visualization for decision-making. Using a hybrid control architecture, the data acquisition and processing showed to be effective, without significant impacts on the battery consumption or in the use of microcomputer resources embedded in the AMR. The developed dashboard demonstrated efficient data navigation and visualization, providing essential tools for decision-making by the platform administrator. This work contributes to the health monitoring of types of equipment as AMRs. It may be of interest to professionals and researchers in areas related to robotics and automation, especially those who work with equipment that uses Robot Operating System (ROS). Besides, the developed system is open-source, making it accessible and customizable in different contexts and applications.Com o aumento contínuo da quantidade de dados produzidos e coletados em diversas áreas, a visualização de dados tem se mostrado cada vez mais relevante para melhorar a manutenção de equipamentos, reduzir custos operacionais e aumentar a eficiência de processos. Este trabalho propõe o desenvolvimento de um sistema de monitoramento da saúde de um equipamento do tipo Autonomous Mobile Robots (AMR), que permita a coleta e análise de dados para tomadas de decisão realizadas tanto autonomamente quanto pelo gestor da plataforma. A implementação do sistema proposto apresentou resultados favoráveis na coleta, análise e visualização de dados para a tomada de decisões. Utilizando uma arquitetura de controle híbrida, a aquisição e processamento dos dados mostrouse eficiente, sem impactos significativos no consumo de bateria ou uso de recursos do microcomputador embarcado no AMR. O dashboard desenvolvido mostrou-se eficiente na navegação e visualização dos dados, fornecendo ferramentas importantes para a tomada de decisão do gestor da plataforma. Este trabalho contribui para a monitorização de saúde de equipamentos como AMRs, podendo ser de interesse para profissionais e pesquisadores em áreas relacionadas à robótica e automação, em especial aqueles que trabalham com equipamentos que utilizam do Robot Operating System (ROS). Além disso, o sistema apresentado é open-source, tornando-o acessível e personalizável para uso em diferentes contextos e aplicações