Autonomic Road Transport Support Systems

The work on Autonomic Road Transport Support (ARTS) presented here aims at meeting the challenge of engineering autonomic behavior in Intelligent Transportation Systems (ITS) by fusing research from the disciplines of traffic engineering and autonomic computing. Ideas and techniques from leading edge artificial intelligence research have been adapted for ITS over the last years. Examples include adaptive control embedded in real time traffic control systems, heuristic algorithms (e.g. in SAT-NAV systems), image processing and computer vision (e.g. in automated surveillance interpretation). Autonomic computing which is inspired from the biological example of the body’s autonomic nervous system is a more recent development. It allows for a more efficient management of heterogeneous distributed computing systems. In the area of computing, autonomic systems are endowed with a number of properties that are generally referred to as self-X properties, including self-configuration, self-healing, self-optimization, self-protection and more generally self-management. Some isolated examples of autonomic properties such as self-adaptation have found their way into ITS technology and have already proved beneficial. This edited volume provides a comprehensive introduction to Autonomic Road Transport Support (ARTS) and describes the development of ARTS systems. It starts out with the visions, opportunities and challenges, then presents the foundations of ARTS and the platforms and methods used and it closes with experiences from real-world applications and prototypes of emerging applications. This makes it suitable for researchers and practitioners in the fields of autonomic computing, traffic and transport management and engineering, AI, and software engineering. Graduate students will benefit from state-of-the-art description, the study of novel methods and the case studies provided

Formal methods and tools for the development of distributed and real time systems : Esprit Project 3096 (SPEC)

The Basic Research Action No. 3096, Formal Methods snd Tools for the Development of Distributed and Real Time Systems, is funded in the Area of Computer Science, under the ESPRIT Programme of the European Community. The coordinating institution is the Department of Computing Science, Eindhoven University of Technology, and the participating Institutions are the Institute of Computer Science of Crete. the Swedish Institute of Computer Science, the Programmimg Research Group of the University of Oxford, and the Computer Science Departments of the University of Manchester, Imperial College. Weizmann Institute of Science, Eindhoven University of Technology, IMAG Grenoble. Catholic University of Nijmegen, and the University of Liege. This document contains the synopsis. and part of the sections on objectives and area of advance, on baseline and rationale, on research goals, and on organisation of the action, as contained in the original proposal, submitted June, 198S. The section on the state of the art (18 pages) and the full list of references (21 pages) of the original proposal have been deleted because of limitation of available space

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

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

Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed

The Internet of Things: A Review of Enabled Technologies and Future Challenges

The Internet of Things (IoT) is an emerging classical model, envisioned as a system of billions of small interconnected devices for posing the state-of-the-art findings to real-world glitches. Over the last decade, there has been an increasing research concentration in the IoT as an essential design of the constant convergence between human behaviors and their images on Information Technology. With the development of technologies, the IoT drives the deployment of across-the-board and self-organizing wireless networks. The IoT model is progressing toward the notion of a cyber-physical world, where things can be originated, driven, intermixed, and modernized to facilitate the emergence of any feasible association. This paper provides a summary of the existing IoT research that underlines enabling technologies, such as fog computing, wireless sensor networks, data mining, context awareness, real-time analytics, virtual reality, and cellular communications. Also, we present the lessons learned after acquiring a thorough representation of the subject. Thus, by identifying numerous open research challenges, it is presumed to drag more consideration into this novel paradigm. 2013 IEEE.This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government(MSIT) (No. 2018-0-01411, A Micro-Service IoTWare Framework Technology Development for Ultra small IoT Device).Scopus2-s2.0-8505888625

Internet of Things Applications - From Research and Innovation to Market Deployment

The book aims to provide a broad overview of various topics of Internet of Things from the research, innovation and development priorities to enabling technologies, nanoelectronics, cyber physical systems, architecture, interoperability and industrial applications. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from technology to international cooperation and the global "state of play".The book builds on the ideas put forward by the European research Cluster on the Internet of Things Strategic Research Agenda and presents global views and state of the art results on the challenges facing the research, development and deployment of IoT at the global level. Internet of Things is creating a revolutionary new paradigm, with opportunities in every industry from Health Care, Pharmaceuticals, Food and Beverage, Agriculture, Computer, Electronics Telecommunications, Automotive, Aeronautics, Transportation Energy and Retail to apply the massive potential of the IoT to achieving real-world solutions. The beneficiaries will include as well semiconductor companies, device and product companies, infrastructure software companies, application software companies, consulting companies, telecommunication and cloud service providers. IoT will create new revenues annually for these stakeholders, and potentially create substantial market share shakeups due to increased technology competition. The IoT will fuel technology innovation by creating the means for machines to communicate many different types of information with one another while contributing in the increased value of information created by the number of interconnections among things and the transformation of the processed information into knowledge shared into the Internet of Everything. The success of IoT depends strongly on enabling technology development, market acceptance and standardization, which provides interoperability, compatibility, reliability, and effective operations on a global scale. The connected devices are part of ecosystems connecting people, processes, data, and things which are communicating in the cloud using the increased storage and computing power and pushing for standardization of communication and metadata. In this context security, privacy, safety, trust have to be address by the product manufacturers through the life cycle of their products from design to the support processes. The IoT developments address the whole IoT spectrum - from devices at the edge to cloud and datacentres on the backend and everything in between, through ecosystems are created by industry, research and application stakeholders that enable real-world use cases to accelerate the Internet of Things and establish open interoperability standards and common architectures for IoT solutions. Enabling technologies such as nanoelectronics, sensors/actuators, cyber-physical systems, intelligent device management, smart gateways, telematics, smart network infrastructure, cloud computing and software technologies will create new products, new services, new interfaces by creating smart environments and smart spaces with applications ranging from Smart Cities, smart transport, buildings, energy, grid, to smart health and life. Technical topics discussed in the book include: • Introduction• Internet of Things Strategic Research and Innovation Agenda• Internet of Things in the industrial context: Time for deployment.• Integration of heterogeneous smart objects, applications and services• Evolution from device to semantic and business interoperability• Software define and virtualization of network resources• Innovation through interoperability and standardisation when everything is connected anytime at anyplace• Dynamic context-aware scalable and trust-based IoT Security, Privacy framework• Federated Cloud service management and the Internet of Things• Internet of Things Application

Food Industry 4.0 readiness in Hungary

In terms of production value, the food industry is the third-largest in Hungary, the first in Hungary in terms of the number of employees, and the first in Europe in the processing industry, as well as a significant user of resources. The research examined the state of art of digitalization readiness, focusing on I4.0 technologies, which supports the management to operate more efficiently the enterprise and to make better decisions. So the focus was on integrated enterprise information systems, management support systems, business intelligence systems, industry 4.0 technologies, and issues related to their application. The analysis based on an online questionnaire survey the request sent to 4.600 enterprises, the response rate was 5% which was representative of the branches of production, covered the Hungarian food and beverage manufacturing sectors in 2019. The companies were asked the most critical technologies in development, going towards Industry 4.0. The research tools were LimeSurvey, Mailing List Server, Excel, Power BI (Desktop, Publishing Server to distribute the results). The used analysing methods were making calculations, pivot tables, models, dasboards. We found that a significant portion of businesses, 78 %, use mobile devices in the manufacturing process. The three most relevant digital technologies are geolocating (GPS, GNSS), cloud computing, and sensor technology. The current level of digitalization and integration cannot be said to be high, but respondents are very optimistic about expectations. Improvements are expected in all areas in the next 2-3 years in terms of digitalisation and integration. Vertical integration involves, first and foremost, cooperation with partners in the supply chain. Horizontal integration means close, real-time connectivity and collaboration within the company. Unfortunately, between 6% and 15% of SMEs (approximately 9% on average) and large enterprises, 36% have a digital strategy. According to the survey, the sector needs significant improvement and creating a digitalization strategy.In terms of production value, the food industry is the third-largest in Hungary, the first in Hungary in terms of the number of employees, and the first in Europe in the processing industry, as well as a significant user of resources. The research examined the state of art of digitalization readiness, focusing on I4.0 technologies, which supports the management to operate more efficiently the enterprise and to make better decisions. So the focus was on integrated enterprise information systems, management support systems, business intelligence systems, industry 4.0 technologies, and issues related to their application. The analysis based on an online questionnaire survey the request sent to 4.600 enterprises, the response rate was 5% which was representative of the branches of production, covered the Hungarian food and beverage manufacturing sectors in 2019. The companies were asked the most critical technologies in development, going towards Industry 4.0. The research tools were LimeSurvey, Mailing List Server, Excel, Power BI (Desktop, Publishing Server to distribute the results). The used analysing methods were making calculations, pivot tables, models, dasboards. We found that a significant portion of businesses, 78 %, use mobile devices in the manufacturing process. The three most relevant digital technologies are geolocating (GPS, GNSS), cloud computing, and sensor technology. The current level of digitalization and integration cannot be said to be high, but respondents are very optimistic about expectations. Improvements are expected in all areas in the next 2-3 years in terms of digitalisation and integration. Vertical integration involves, first and foremost, cooperation with partners in the supply chain. Horizontal integration means close, real-time connectivity and collaboration within the company. Unfortunately, between 6% and 15% of SMEs (approximately 9% on average) and large enterprises, 36% have a digital strategy. According to the survey, the sector needs significant improvement and creating a digitalization strategy

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

Special issue “Towards a higher education of the future: Transformational roles of edge intelligence”

Higher Education of the Future (HEF) is anticipated to be a scalable educational framework that is driven by new digital learning architectures and platforms, as well as collaborative learning systems, that are able to completely guarantee self-paced, customizable, personalized and flexible teaching/learning experiences [4]. The HEF concept strongly points toward a “learning from everywhere” model. The need for HEF is motivated, among other things, by the fact that most state-of-the-art higher education system models currently being used for driving and transitioning higher education are structurally, socially, and technologically incapacitated to meet the key requirements towards delivering a foreseeable smart, real-time intelligence driven HEF. The proliferation of edge devices, smart devices, intelligent applications, and Internet of Things in the higher education domain is now shifting the teaching/learning process, research, educational services, and data computation needs from the cloud to the network edge [5]. These edge devices and innovative digital technologies have potentials to exponentially drive seamless knowledge creation and increased learning. However, the massive volume of data being generated by these edge devices is currently becoming a challenge for cloud computing infrastructures in most HES to manage and analyse.Cloud services can be moved from the network core to the edges via a distributed computing architecture through a distributed framework known as edge computing. This way, all enterprise applications and computation tasks are brought closer to the data sources, (for example, the local edge servers and edge devices) and the end-users. Edge is great and more secure than cloud in managing overloaded networks. Even in the situation of a cyber break-in, edge computing does not allow security disruption from a single node to spread and impact the activities of the entire network since its data is stored and processed locally. It can also allow for seamless and timely-efficient interactions between teachers and learners in virtual classrooms to effectively improve learning outcomes.In a HEF concept, a valuable databank can be built within the network from a number of digital interactions of the devices in order to bring data closer to HEF’ stakeholders, including the researcher, the learner or the administrators, who may require immediate access to it in real-time. In turn, as the data in the edge servers begins to aggregate, Artificial Intelligence (AI) is introduced to the edge for analytic purposes such that the edge device data is able to drive HEF-based AI applications via a concept known as edge intelligence. Edge intelligence (EI) is a promising and highly flexible technology evolving from some social and digital innovations with potentials to deliver a total digitally sustainable HEF that enhances learning experience. Among other things, EI can provide seamless real-time access to, and analytic insights of, the massive data generated by edge devices. This may include learning and prediction, of time-sensitive educational data to facilitate effective decision making or change needs. For example, evaluation of students’ outcome while assessment is in progress. In a connected HEF, EI can help to support IoT capabilities, maximize bandwidth requirements and manage costs in a manner that teaching/learning effectiveness, continued independent learning.This special issue provides an overview of the research being carried out in the higher education of the future focusing on transformational roles of edge intelligence methods and approaches for teaching, learning and entrepreneurship, as well as applications of them in the higher education sector.To that end, the special issue brought together academics from a variety of disciplines to discuss the development and application of innovative edge intelligence-based solutions to effectively drive Higher Education of the Future. Original contributions in this field encompassed a wide spectrum of theoretical and practical aspects, technologies, and methods