2,810 research outputs found

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

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    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

    TACTICAL BLOCKCHAIN TO PROVIDE DATA PROVENANCE IN SUPPORT OF INTERNET OF BATTLEFIELD THINGS AND BIG DATA ANALYTICS

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    This capstone project evaluated the use of blockchain technology to address a number of challenges with increasing amounts of disparate sensor data and an information-rich landscape that can quickly overwhelm effective decision-making processes. The team explored how blockchain can be used in a variety of defense applications to verify users, validate sensor data fed into artificial intelligence models, limit access to data, and provide an audit trail across the data life cycle. The team developed a conceptual design for implementing blockchain for tactical data, artificial intelligence, and machine learning applications; identified challenges and limitations involved in implementing blockchain for the tactical domain; described the benefits of blockchain for these various applications; and evaluated this project’s findings to propose future research into a wider set of blockchain applications. The team did this through the development of three use cases. One use case demonstrated the use of blockchain at the tactical edge in a “data light” information environment. The second use case explored the use of blockchain in securing medical information in the electronic health record. The third use case studied blockchain’s application in the use of multiple sensors collecting data for chemical weapons defense to support measurement and signature intelligence analysis using artificial intelligence and machine learning.Civilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the ArmyApproved for public release. Distribution is unlimited

    New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

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    The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

    Cognitive Hyperconnected Digital Transformation

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    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

    Decentralized Identity and Access Management Framework for Internet of Things Devices

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    The emerging Internet of Things (IoT) domain is about connecting people and devices and systems together via sensors and actuators, to collect meaningful information from the devices surrounding environment and take actions to enhance productivity and efficiency. The proliferation of IoT devices from around few billion devices today to over 25 billion in the next few years spanning over heterogeneous networks defines a new paradigm shift for many industrial and smart connectivity applications. The existing IoT networks faces a number of operational challenges linked to devices management and the capability of devices’ mutual authentication and authorization. While significant progress has been made in adopting existing connectivity and management frameworks, most of these frameworks are designed to work for unconstrained devices connected in centralized networks. On the other hand, IoT devices are constrained devices with tendency to work and operate in decentralized and peer-to-peer arrangement. This tendency towards peer-to-peer service exchange resulted that many of the existing frameworks fails to address the main challenges faced by the need to offer ownership of devices and the generated data to the actual users. Moreover, the diversified list of devices and offered services impose that more granular access control mechanisms are required to limit the exposure of the devices to external threats and provide finer access control policies under control of the device owner without the need for a middleman. This work addresses these challenges by utilizing the concepts of decentralization introduced in Distributed Ledger (DLT) technologies and capability of automating business flows through smart contracts. The proposed work utilizes the concepts of decentralized identifiers (DIDs) for establishing a decentralized devices identity management framework and exploits Blockchain tokenization through both fungible and non-fungible tokens (NFTs) to build a self-controlled and self-contained access control policy based on capability-based access control model (CapBAC). The defined framework provides a layered approach that builds on identity management as the foundation to enable authentication and authorization processes and establish a mechanism for accounting through the adoption of standardized DLT tokenization structure. The proposed framework is demonstrated through implementing a number of use cases that addresses issues related identity management in industries that suffer losses in billions of dollars due to counterfeiting and lack of global and immutable identity records. The framework extension to support applications for building verifiable data paths in the application layer were addressed through two simple examples. The system has been analyzed in the case of issuing authorization tokens where it is expected that DLT consensus mechanisms will introduce major performance hurdles. A proof of concept emulating establishing concurrent connections to a single device presented no timed-out requests at 200 concurrent connections and a rise in the timed-out requests ratio to 5% at 600 connections. The analysis showed also that a considerable overhead in the data link budget of 10.4% is recorded due to the use of self-contained policy token which is a trade-off between building self-contained access tokens with no middleman and link cost

    Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

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    The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities
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