The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

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

From Network to Web dimension in supply chain management

Cette thèse soutient que la dimension réseau, étant actuellement la portée du domaine de la gestion de chaîne logistique, contraint l’avancement de ce domaine et restreint des innovations conceptuelles et fondamentales capables d’adresser les grands défis économiques, environnementaux et sociaux. Les concepts de chaîne et de réseau ne reflètent pas la complexité des flux physiques, informationnels et financiers générés par les interactions qui ont lieu dans des réseaux interconnectés. Ces concepts n’offrent pas les fondations théoriques pour supporter des interventions allant au-delà d’un seul réseau et laissent échapper des opportunités nécessitant une vision multi-réseau. Ainsi, la dimension “web”, celle des réseaux de réseaux, est proposée comme une extension de la dimension réseau. Cette extension peut être vue comme l’étape naturelle suivante dans la progression qui a commencé par le niveau de gestion des opérations internes, est passée au niveau de la chaîne logistique et se trouve actuellement au niveau du réseau logistique. Après l’investigation théorique des raisons et de la façon d’intégrer la dimension web dans le domaine de la gestion de la chaîne logistique, la thèse étudie des implications importantes de cette intégration sur la collaboration inter-organisationnelle et le processus de prise de décision dans des environnements de webs logistiques. Elle démontre, en exploitant l’exemple des réseaux interconnectés ouverts, des potentialités inimaginables sans une vision web. Une méthodologie de conception d’un modèle de simulation permettant l’évaluation et la comparaison des webs ouverts par rapport aux webs existants est proposée. Puisque l’aide à la décision est une composante importante de la gestion de la chaîne logistique, la thèse contribue à déterminer les besoins des gestionnaires et à identifier les lignes directrices de la conception des outils d’aide à la décision offrant le support adéquat pour faire face aux défis et à la complexité des webs logistiques. Ces lignes directrices ont été compilées dans un cadre de conception des logiciels d’aide à la décision supportant la dimension web. Ce cadre est exploité pour développer quatre applications logicielles offrant aux praticiens et aux chercheurs des outils nécessaires pour étudier, analyser et démêler la complexité des webs logistiques.This thesis argues that the network dimension as the current scope of supply chain management is confining the evolution of this field and restricting the conceptual and fundamental innovations required for addressing the major challenges imposed by the evolution of markets and the increased intricacies of business relationships. The concepts of chain and network are limitative when attempting to represent the complexity of physical, informational and financial flows resulting from the interactions occurring in overlapping networks. They lack the theoretical foundations necessary to explain and encompass initiatives that go beyond a single chain or network. They also lead to overlook substantial opportunities that require beyond a network vision. Therefore, the “web” dimension, as networks of networks, is proposed as an extension to the network dimension in supply chain management. This new scope is the natural next step in the progression from the internal operations management level to the supply chain level and then to the supply network level. After a theoretical investigation of why and how the web dimension should be integrated into the supply chain management field, the thesis studies and discusses important implications of this integration on inter-organisational collaboration and of the decision-making processes in the logistic web environments. It demonstrates through the example of open interconnected logistic webs some of the potentials that cannot be imagined without a web vision. A methodology for designing a simulation model to assess the impact of such open webs versus existing webs is proposed. Since decision support is a key element in supply chain management, the thesis contributes to determine the needs of supply chain managers and identify the important axes for designing decision support systems that provide adequate assistance in dealing with the challenges and complexity presented by logistic web environments. The identified elements result in the establishment of a foundation for designing software solutions required to handle the challenges revealed by the web dimension. This conceptual framework is applied to the prototyping of four applications that have the potential of providing practitioners and researchers with the appropriate understanding and necessary tools to deal with the complexity of logistics webs

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

Supporting Device Mobility and State Distribution through Indirection, Topological Isomorphism and Evolutionary Algorithms

The Internet of Things will result in the deployment of many billions of wireless embedded systems, creating interactive pervasive environments. These pervasive networks will provide seamless access to sensor actuators, enabling organisations and individuals to control and monitor their environment. The majority of devices attached to the Internet of Things will be static. However, it is anticipated that with the advent of body and vehicular networks, we will see many mobile Internet of Things Devices. During emergency situations, the flow of data across the Internet of Things may be disrupted, giving rise to a requirement for machine-to-machine interaction within the remaining environment. Current approaches to routing on the Internet and wireless sensor networks fail to address the requirements of mobility, isolated operation during failure or deal with the imbalance caused by either initial or failing topologies when applying geographic coordinate-based peer-to-peer storage mechanisms. The use of global and local DHT mechanisms to facilitate improved reachability and data redundancy are explored in this thesis. Resulting in the development of an Architecture to support the global reachability of static and mobile Internet of Things Devices. This is achieved through the development of a global indirection mechanism supporting position relative wireless environments. To support the distribution and preservation of device state within the wireless domain a new geospatial keying mechanism is presented, this enables a device to persist state within an overlay with certain guarantees as to its survival. The guarantees relating to geospatial storage rely on the balanced allocation of distributed information. This thesis details a mechanism to balance the address space utilising evolutionary techniques. Following the generation of an initial balanced topology, we present a protocol that applies Topological Isomorphism to provide the continued balancing and reachability of data following partial network failure. This dissertation details the analysis of the proposed protocols and their evaluation through simulation. The results show that our proposed Architecture operates within the capabilities of the devices that operate in this space. The evaluation of Geospatial Keying within the wireless domain showed that the mechanism presented provides better device state preservation than would be found in the random placement exhibited by the storage of state in overlay DHT schemes. Experiments confirm device storage imbalance when using geographic routing; however, the results provided in this thesis show that the use of genetic algorithms can provide an improved identity assignment through the application of alternating fitness between reachability and ideal key displacement. This topology, as is commonly found in geographical routing, was susceptible to imbalance following device failure. The use of topological isomorphism provided an improvement over existing geographical routing protocols to counteract the reachability and imbalance caused by failure

On Learning in Collective Self-adaptive Systems: State of Practice and a 3D Framework

Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS