R4.2 In the Industry 4.0 era, Digital Twins (DTs) serve as virtual representations of physical objects and intermediaries between the physical world and the digital realm. DTs require proper modeling, design, and development to ensure their seamless integration along the cloud-to-edge continuum. In particular, this work introduces a microservices-based and serverless-ready model for DTs, laying the foundation for cost-effective DT deployment and orchestration. The joint adoption of microservices and serverless computing offers significant potential to address various challenges, including accommodating variable application requirements, managing load imbalances, and mitigating network faults. The proposed DT model has been implemented in different flavors: two serverless implementations—one that relies on a serverless framework of a cloud provider and one running at the edge on-premises—and a microservices one. These implementations have been experimentally evaluated with particular emphasis on the quality of cyber–physical entanglement. This work not only discusses the advantages and drawbacks of different implementations from a qualitative perspective but also quantitatively evaluates them with the in-the-field collection of experimental performance results. Notably, we report that a serverless implementation typically performs an order of magnitude worse than a microservices one in terms of entanglement, i.e., hundreds vs. tens of milliseconds.

Within the Industry 4.0 vision, digital twins (DTs) have gained great attention as a promising approach to improve remote monitoring and control by means of virtual representations of physical objects. However, while DTs are becoming more and more sophisticated and even adopted for mission-critical applications, their resilience assessment has not received the required consideration yet. This article originally proposes chaos engineering to assess and improve the resilience of DTs by testing multiple aspects of industrial environments in a coordinated, automated, and replicable manner. First, the article discusses why and how chaos engineering is promising to improve the resilience of DTs. Then, it identifies and introduces a set of chaos engineering profiles specifically designed to take into account the many aspects an industrial environment is composed of. Finally, it shows the feasibility of assessing the resilience of a proof-of-concept DT through a testbed based on widely-adopted, open-source tools.

The development of the Digital Twin (DT) approach is tilting research from initial approaches that aim at promoting early adoption to sophisticated attempts to develop, deploy, and maintain applications based on DTs. In this context, we propose a highly dynamic and distributed ecosystem where containerized DTs co-evolve with an orchestration middleware. DTs provide digitalized representations of the targeted physical systems, while the orchestration middleware monitors and re-configures the deployed DTs in light of application constraints, available resources, and the quality of cyber-physical entanglement. First, we lay out the reference scenario. Then, we discuss the limitations of current approaches and identify a set of requirements that shape both DTs and the orchestration middleware. Subsequently, we describe a blueprint architecture that meets those requirements. Finally, we report empirical evidence on both the feasibility and the effectiveness of a proof-of-concept implementation of the proposed ecosystem.

Digital Twins (DTs) have recently emerged as a valuable approach for modeling, monitoring, and controlling physical objects in Industrial Internet of Things applications. Measuring the quality of entanglement between the digital and physical counterparts plays a crucial role in the adoption of DTs. In this paper, we propose a concise yet expressive metric for representing the quality of entanglement, namely Overall Digital Twin Entanglement (ODTE), based on two key factors: timeliness and completeness. Furthermore, the paper presents the development of our industrial testbed implemented on top of Kubernetes, where we show practical applications of the proposed ODTE metric by highlighting and discussing its benefits in realistic use cases.

Traditional industrial networks were characterized by flat topologies, where industrial equipment exchanged a limited number of messages. In sharp contrast, modern manufacturing plants are evolving towards articulated environments generating an ever-increasing amount of network traffic. Such emerging environments prevent the adoption of traditional solutions based on end-to-end dispatching of few messages in reliable networks with bandwidth availability much greater than needed. This leads to the need to adopt proper message management strategies as close as possible to industrial equipment to avoid overwhelming the industrial network with non-mission-critical traffic at the expense of mission-critical one. This paper originally proposes edge-powered in-network processing to i) transparently manage messages sent by industrial equipment, ii) support a broad spectrum of message management strategies, ranging from efficient header-based solutions to expressive content-based ones, and iii) fulfill the application-dependent requirements demanded by industrial environments nowadays. Achieved performance results based on a proof-of-concept prototype demonstrate that the proposed solution efficiently provides content-based message management at the edge, even considering edge nodes with limited hardware capabilities.

The industrial internet of things has radically modified industrial environments, not only enabling novel services but also dramatically increasing the amount of generated traffic. Nowadays, a major concern within industrial plants is to support network-intensive services, such as real-time remote vibration monitoring of autonomous guided vehicles, while ensuring the prompt and reliable delivery of mission-critical safety-related messages among machines and the control room. To this purpose, we present a novel solution jointly orchestrating content-based message management and traffic flow steering: the former enables edge-powered in-network processing modules to process packet payloads as they traverse the industrial backbone, the latter supports dynamic (re)routing of traffic flows towards such processing modules. In particular, we exploit software-defined networking for flexible traffic flow (re)routing and Kubernetes for dynamic deployment on edge nodes of in-network processing modules for content-based message management. As demonstrated by performance results based on our working proof-of-concept prototype, our solution efficiently allows to manage industrial traffic flows in a coordinated fashion, by considering requirements of concurrently running industrial applications and the current state of the overall topology.

The advent of the Internet of Things (IoT) and its spread in industrial environments has changed production lines by dramatically fostering the dynamicity of data sharing and the connectivity of machines. However, such increased flexibility (also pushed by the adoption of edge devices) must not negatively affect the security and safety of industrial environments. The proposed solution adopts the blockchain to securely store in distributed ledgers topology information and access rules, maximizing the cyber resiliency of industrial networks. Topology information and access rules are stored and queried in a completely distributed manner, ensuring data availability even in case a centralized controller is temporarily down or the network partitioned. Moreover, blockchain consensus algorithms foster a participative validation of topology information, to ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a nonrepudiable manner. Finally, the adoption of configurable edge gateways allows to take prompt countermeasures in case potential threats are identified, by activating access rules stored in ledgers in a secure and distributed manner. In addition to solution design guidelines and architectural considerations, the article also presents performance results achieved with our CyberChain working prototype, with the goal of not only demonstrating the feasibility of the proposed solution but also its suitability in industrial environments.

Industry 4.0 environments pose unique challenges for the realization of the communication substrate at the shop floor, due to the strict Quality of Service (QoS) requirements, the high heterogeneity of the employed data exchange protocols, and the different network technologies and addressing schema toward the machines. To address those issues, the paper proposes a distributed support based on a Message Oriented Middleware (MOM) and a Software Defined Network (SDN) control plane that coordinate to enable semantic routing by also allowing traffic differentiation as well as in-network processing at intermediate network nodes. Seminal results, collected in realistic industrial settings, confirm the feasibility of our proposal.

Today Industrial IoT environments are still based on traditional IP technologies, which makes difficult, if not impossible, to bridge the missing link between the application and the network layer. Indeed, IIoT applications should be able to ask for network services (with specific QoS guarantees) independently of the addressing at the network and transport layer. In this paper, we start from a novel patented method for routing packets to demonstrate with a prototype implementation the feasibility and efficiency of a software defined solution based on flow tagging with the purpose of supporting IIoT dynamic network programmability. We show how the combination of IP datagram options and SDN allows IIoT environments to reach per-device and per-application flow management granularity. The presented proof of concept prototype outlines the capability to dynamically program the network based on tagged flows and to differentiate network behaviors based on IIoT requirements together with the whole network state.

The application of Internet of Things (IoT) within industrial environments is fostering the adoption of the digital twin (DT) approach, applied at the edge of the network to handle heterogeneity stemming from siloed application management solutions and from protocols originated by different manufacturing tools and enterprise services. In this challenging context, network heterogeneity also represents a critical element that can significantly limit the design and deployment of DT-oriented applications. This article proposes the Application-driven digital twin networking middleware with the twofold objective of: 1) Simplifying the interaction among heterogeneous devices by allowing DTs to exploit IP-based protocols instead of specialized industrial ones and to enhance packet content expressiveness, by enriching data via well-defined standards. 2) Dynamically managing network resources in edge industrial environments, applying software defined networking to exploit the communication mechanisms most suitable to application requirements, ranging from native IP to more articulated based on packet content.

Compared to Cloud computing, Fog computing is proving to support challenging scenarios imposing stricter delay requirements, e.g., tactile Internet and Industrial Internet of Things (IIoT), and demanding increased flexibility, e.g., dynamic Smart Cities and users' follow-me provisioning cases. However, such scenarios are characterized by increased heterogeneity of nodes in terms of hardware/software characteristics, of time-varying services/applications possibly offered by multiple service providers, and frequent joining/leaving of nodes. The paper originally proposes Multi-Layer Advanced Networking Environment (Multi-LANE), a Multi-Layer Routing (MLR) solution based on Software Defined Networking (SDN). Multi-LANE dynamically selects and exploits routing strategies and mechanisms suitable for applications with heterogeneous capabilities and requirements. Based on application requirements and its centralized point of view, our SDN controller determines the most suitable path and configures the proper MLR forwarding mechanism, ranging from traditional IP and sequence-based overlay to more articulated ones based on payload content type and value inspection.

The advent of the Internet of Things (IoT) together with its spread in industrial environments have changed pro-duction lines, by dramatically fostering the dynamicity of data sharing and the openness of machines. However, the increased flexibility and openness of the industrial environment (also pushed by the adoption of Edge devices) must not negatively affect the security and safety of production lines and its opera-tional processes. In fact, opening industrial environments towards the Internet and increasing interactions among machines may represent a security threat, if not properly managed. The paper originally proposes the adoption of the Blockchain to securely store in distributed ledgers topology information and access rules, with the primary goal of maximizing the cyber-resiliency of industrial networks. In this manner, it is possible to store and query topology information and security access rules in a completely distributed manner, ensuring data availability even in case a centralized control point is temporarily down or the network partitioned. Moreover, Blockchain consensus algorithms can be used to foster a participative validation of topology information, to reciprocally ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a non-repudiable manner.

In modern society, food safety is becoming more and more important. The adoption of appropriate practices, such as the ones defined in the HACCP system, during food production, handling, preparation, and storage can reasonably guarantee food safety. However, it is not easy to apply HACCP methodologies in an automatic form, thus hindering its use in industrial machines. To solve this problem, the paper presents a novel solution adopting Internet of Things (IoT) and Blockchain technologies in the ice cream production process to automate the enforcement of HACCP directives. The new Carpigiani ice cream making machines exploit IoT for the automation of data gathering (in particular the temperature, that is of particular concern for dairy products) and a Blockchain solution for a tamper-proof and non-repudiable distributed storage of HACCP sensitive production data.

Already available WiFi direct and upcoming 5G Device-to-Device (D2D) communication mechanisms are paving the way for the development of Mobile Ad-hoc Networks (MANET) applications. This trend involves the cooperation of nearby mobile nodes in charge of dispatching messages. In addition, the consolidation of the Fog paradigm enables innovative scenarios characterized by the interaction of MANET and Edge nodes. For instance, tourists visiting a city form a MANET to share pictures while the municipality provides Internet connectivity via Edge devices. However, it is required to address specific issues stemming from the collaborative nature of D2D communication, ranging from limited node capabilities providing multi-hop networks to unreliable connectivity due to node mobility. This paper presents the Reliable and Dynamic Routing Technique (RaDRT) solution, adopting the Software Defined Networking (SDN) approach to regulate routing of traffic flows in such Edge-MANET environments. To this purpose, RaDRT originally exploits the joint combination of three primary guidelines: 1) SDN to monitor/manage the state of the mobile network also considering different Quality of Service (QoS) requirements of concurrently running applications, 2) dynamic management of service priority to tune if and how packets are forwarded in a fine-grained per-flow differentiated manner, and 3) joined mobile/fixed solution to maximize the overall QoS also evaluating path reliability based on node mobility. This paper presents the Reliable and Dynamic Routing Technique (RaDRT) solution, adopting the Software Defined Networking (SDN) approach to regulate routing of traffic flows in such Edge-MANET environments. To this purpose, RaDRT originally exploits the joint combination of three primary guidelines: 1) SDN to monitor/manage the state of the mobile network also considering different Quality of Service (QoS) requirements of concurrently running applications, 2) dynamic management of service priority to tune if and how packets are forwarded in a fine-grained per-flow differentiated manner, and 3) joined mobile/fixed solution to maximize the overall QoS also evaluating path reliability based on node mobility.

If compared with Cloud computing, Fog computing is proving to support challenging scenarios imposing strict delay requirements, e.g., tactile Internet and Industrial Internet of Things (IIoT), and increased flexibility, e.g., dynamic Smart City and users' follow-me provisioning case. In fact, by exploiting computing, storage, and connectivity resources in the proximity of sensors and actuators (for IIoT) and of mobile nodes carried by citizens (for Smart Cities), significant portions of services and functionalities can be migrated outside datacenters. However, such scenarios are characterized by increased heterogeneity of nodes in terms of hardware/software, of time-varying applications possibly offered by multiple service providers at the same time, and frequent joining/leaving of nodes as a typical behavior. To overcome these issues, the paper originally proposes Multi-Layer Advanced Networking Environment (Multi-LANE), a Multi Layer Routing (MLR) solution based on Software Defined Networking (SDN) that specifically targets the emerging and promising Fog-based deployment environments. Multi-LANE dynamically selects and exploits (even at the same time) different routing strategies and mechanisms suitable for applications with heterogeneous features and requirements. Based on its centralized point of view, our Multi-LANE SDN controller determines the most suitable path and configures the proper MLR forwarding mechanism, ranging from traditional IP and sequence-based overlays to more articulated ones based on the inspection of payload content types and values. In addition to design/implementation insights and to the availability of the Multi-LANE prototype, this paper also provides the community with a significant contribution in terms of novel models for forwarding mechanisms specialized for Fog computing scenarios.

The widespread adoption of information and communication technologies (ICT) is profoundly changing manufacturing. Several Internet-of-Things (IoT) and industry 4.0 solutions deployed in production environments have pushed for standardization efforts, most notably reference architecture model industrie 4.0 (RAMI 4.0), typically focusing on smart factory environments. However, the ICT evolution is also enabling novel smart appliance scenarios, where relatively cheap machines, connected and integrated, are deployed outside the typical industrial environment with a wide range of stakeholders involved. The paper reports about a real world use case composed of more than 12000 ice cream machines connected worldwide and shows how, anticipating the state of the art, the underlying design of the ICT platform presents many interesting similarities with RAMI 4.0. The integration of appliances in a smart value chain enables to develop novel services for different stakeholders, ranging from ice cream manufacturer and maintenance technicians to ice cream shop owners and final consumers. The important synergies with RAMI 4.0 and the extensive on-the-field validation make the proposed solution a compelling reference application, from which to draw useful and generally applicable guidelines for the development of future Industry 4.0 smart appliance platforms.

Software Defined Networking (SDN), with its clear distinction of control and data planes, as well with its simple paradigm of logically centralized controller with global visibility of the whole targeted network status, is gaining momentum in different scenarios. However, its effective exploitation in Mobile Ad hoc Networks (MANETs) is still an open research issue, mainly due to the peculiar dynamicity of the related environments (e.g., in the case of spontaneous MANETs) and to the need to exploit locality considerations to achieve efficient solutions. Here we originally present an architecture and an associated prototype for SDN-based quality management of selected traffic flows, which advance the state-of-the-art in the field by i) allowing high flexibility via deployment of new flow management policies at provisioning time, ii) properly handling node join/leave events, and, most relevant, iii) determining MANET “islands”, usually managed by separated SDN controllers that can seldom interact to federate their management decisions. In addition to design/implementation insights and to the availability of the prototype code, this paper provides the community with a significant and novel contribution in terms of first experimental performance results, which show the feasibility and the effectiveness of the proposed approach.

The growing interest in the Internet of Things (IoT) has resulted in a number of wide-area deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multi-hop ad-hoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT traffic across a common set of sensors and networking resources must be designed. In this paper, we will design a software-defined approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reflective (self-observing and adapting via an embodied Observe-Analyze-Adapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate flow scheduling over task-level, multi-hop, and heterogeneous ad-hoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINA SDN prototype in the challenging IoT scenario of wide-scale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efficient exploitation of the IoT multinetwork capabilities.

The networking landscape of today is characterized by diverse access technologies including cellular, WiFi, Ethernet, MANETs, and ZigBee, and properly managing this heterogeneous networking infrastructure is a key challenge to take full advantage of its many opportunities. In this paper, we propose MINA (Multinetwork INformation Architecture), a reflective (self-observing and adapting) middleware approach to realize and manage dynamic and heterogeneous multi-networks in pervasive environments. A novel aspect of MINA is that it embodies an Observe-Analyze-Adapt (OAA) loop to i) achieve a reasonably accurate, centralized global view of the multi-network through the design of novel techniques for overlay structuring, network state collection and formal methods-based analysis, and ii) take advantage of the global view for adapting multi-network structure by reallocating application flows across networks and proactively planning and deploying additional network resources.

Private cloud computing has been recently pushed as a promising solution to more efficiently exploit available hardware equipment and quickly reconfigure software components depending on the current load. However, private cloud computing, and in particular the Infrastructure as a Service (IaaS) model, has been primarily adopted only by large companies, at least so far. In fact, Small and Medium Enterprises (SMEs) usually have difficulties in adopting the private computing paradigm, either since they are not able to afford the cost of off-the-shelf proprietary solutions or they do not have the know-how required to adapt open-source solutions to their own requirements. The paper presents our novel framework for Easy Monitoring and Management of IaaS (EMMI) solutions, with the main objective of making easier the adoption of the private cloud paradigm. The EMMI framework is based on open-source software components, i.e., OpenStack for IaaS management, Collectd for distributed system monitoring, and Apache jk for load balancing, allowing to adopt the IaaS model easily and in a cost effective manner. The reported performance results demonstrate that the EMMI framework efficiently supports two primary features of the IaaS model, i.e., failover and scale-out, promptly identifying crucial events and autonomously taking suitable countermeasures.

The widespread availability of mobile devices with multiple wireless interfaces, such as UMTS/GPRS, IEEE 802.11a/b/g and Bluetooth, is pushing for the support of multi-homing and multi-channel connectivity, also enabled by multi-hop cooperative paths to the Internet. The goal is to transparently allow the synergic exploitation of "best" connectivity opportunities available at runtime, by enabling cooperative connectivity, extended wireless coverage, and effective load balancing (for both energy and bandwidth consumption). To this purpose, we claim the need for innovative, lightweight, and proactive evaluation metrics for connectivity management by exploiting application-level awareness of expected node mobility, path throughput, and energy availability. To demonstrate the effectiveness of these solution guidelines for Multi-hop Multi-path Heterogeneous Connectivity (MMHC), we have designed, implemented, and thoroughly evaluated our evaluation metrics on top of the MMHC middleware, which are original because they i) enable the management of multiple multi-hop paths, also made up by heterogeneous wireless links, ii) support connectivity management decisions depending on dynamically gathered context indicators, and iii) can proactively trigger management operations with limited overhead. The extensive set of reported results, from both simulations and real testbed, provides a useful guide for the full understanding of how, to what extent, and which context-based evaluation metrics can enable effective MMHC management in differentiated application/deployment scenarios.

Spontaneous (or opportunistic) networks are multi-hop ad-hoc networks where nodes opportunistically exploit peer-to-peer contacts to share content and available resources in an impromptu way. Even if spontaneous networking has recently received growing interest, there is still the lack of impactful and wide-scale applications fully exploiting its potential. We claim that this is due to the intrinsic complexity of spontaneous network management, unsuitable to be directly handled by application developers. Therefore, this paper proposes a novel easy-to-use middleware, called RAMP, for the autonomic, cross-, and application-layer management of spontaneous networks. RAMP enables the dynamic sharing of all resources available via multiple, heterogeneous, intermittent, infrastructure-based, and ad-hoc links, which are orchestrated in a lightweight way to compose the multi-hop paths needed by sharing applications at runtime. The RAMP prototype is a useful tool for the community of researchers in the field and can be rapidly deployed over real execution environments. The reported experimental results demonstrate the feasibility of our approach and the limited RAMP overhead over common deployment scenarios.

The diffusion of wireless terminals with multiple communication interfaces, e.g., IEEE 802.11, Bluetooth, and UMTS on the same device, is pushing towards the necessity of middleware solutions to dynamically and seamlessly select the proper connectivity technology to exploit at any time. That selection should consider several elements, at very different abstraction layers, from application bandwidth to energy consumption requirements, from connectivity costs to user preferences, i.e., it should be context-dependent. The paper presents our context-aware MAC middleware for multi-interface wireless terminals that enables the dynamic determination and selection of the most suitable interface and connectivity provider among the available ones. MAC novelty is primarily in two crucial challenging elements. On the one hand, it considers not only infrastructure-based connectivity providers, e.g., UMTS base stations, but also peer nodes, e.g., neighbor nodes accessible via Bluetooth and connected via Wi-Fi to the Internet infrastructure. On the other hand, MAC can evaluate both infrastructure and peer connectivity providers not only based on usual parameters such as available bandwidth and energy consumption, but also taking into account innovative and crucial indicators such as the degree of mobility, even relatively to mobile connecting clients.

The rapid diffusion of heterogeneous forms of wireless connectivity is pushing the tremendous growth of the commercial interest in mobile services, i.e., distributed applications to portable wireless terminals that roam during service provisioning. In the case of both location-dependent mobile services and mobile services with session continuity requirements, there is a growing need for decentralized and lightweight solutions to predict cell handovers, in order to enable proactive service management operations that anticipate actual terminal reconnections at their newly visited cells. The paper discusses how to predict client handovers between IEEE 802.11 cells in a portable and completely decentralized way, only by exploiting RSSI monitoring and with no need of external global positioning systems. In particular, the paper focuses on proposing and comparing different filtering techniques for mitigating Received Signal Strength Indication abrupt fluctuations. Experimental results point out that i) filtering techniques can relevantly improve the efficiency and effectiveness of handover prediction, and ii) the choice of the most appropriate filtering solution to adopt should be made at provisioning time depending on specific service/system requirements, e.g., privileging minimum overhead vs. greater prediction proactivity.