As critical cyber-physical systems, urban transport systems are vulnerable to natural disasters and deliberate attacks. Ensuring their resilience is crucial for sustainable operations and includes the ability to withstand, absorb and recover efficiently from disruptions. Assessing the resilience of such systems requires a comprehensive set of performance indicators covering social, economic, organisational, environmental and technical concerns. In addition, the interdependence of the different modes of transport and the resulting human activities requires the inclusion of the spatial dimension to capture potential cascading failures. Furthermore, the integration of both aleatory (data) and epistemic (modelling) uncertainties is essential for robust performance indicators.

With increasing interdependencies and evolving threats, maintaining operational continuity in critical systems has become a significant challenge. This paper presents the DYNABIC (Dynamic business continuity of critical infrastructures on top of adaptive multi-level cybersecurity) approach as a comprehensive framework to enhance the resilience of critical infrastructures. The DYNABIC approach provides the resilience enhancement through dynamic adaptation, automated response, collaboration, risk assessment, and continuous improvement. By fostering a proactive and collaborative approach to resilience, the DYNABIC framework empowers critical infrastructure sectors to effectively mitigate disruptions and recover from incidents. The paper explores the key components and architecture of the DYNABIC approach and highlights its potential to strengthen the resilience of critical infrastructures using the concept of Digital Twins in the face of evolving threats and complex operating environments involving cascading effects.

Current security orchestration and response (SOAR) approaches have primarily focused on specific layers of systems, such as Intrusion Detection Systems, the network layer, or the application layer. We aim to find the gaps in the existing SOAR approaches for IoT/CPS-based systems, especially critical infrastructures, and propose some directions to fill in these gaps. This paper presents a literature survey and future research directions for advancing SOAR towards increased automation and more holistic operation, especially for the cyber-physical security of critical infrastructures. We have found 14 primary SOAR studies and discussed the gaps in general. There is a significant gap when it comes to a comprehensive and systematic approach to SOAR for multi-layered systems using IoT/CPS and considering the computing continuum perspective. To address the gap, we present our on-going work on a framework of multi-layer SOAR decision-making methods and orchestration tools that leverage Reinforcement Learning (RL)-based adaptation intelligence, virtual reality, avatar-human interaction and advanced Cyber Threat Intelligence (CTI) tools.

International audience

One of the most prominent implementations of the serverless programming model is Function-as-a-Service (FaaS). Using FaaS, application developers provide source code of serverless functions, typically describing only parts of a larger application, and define triggers for executing these functions on infrastructure components managed by the FaaS provider. There are still challenges that hinder the wider adoption of the FaaS model across the whole Cloud-Edge-IoT continuum. These include the high heterogeneity of the Edge and IoT infrastructure, vendor lock-in, the need to deploy and adapt serverless functions as well as their supporting services and software stacks into their cyber-physical execution environment. As a first step towards addressing these challenges, we introduce the SERVERLEss4I0T platform for the design, deployment, and maintenance of applications over the Cloud-Edge-IoT continuum. In particular, our platform enables the specification and deployment of serverless functions on Cloud and Edge resources, as well as the deployment of their supporting services and software stacks over the whole Cloud-Edge-IoT continuum.

Abstract Security of the Internet of Things (IoT)-based Smart Systems involving sensors, actuators and distributed control loop is of paramount importance but very difficult to address. Security patterns consist of domain-independent time-proven security knowledge and expertise. How are they useful for developing secure IoT-based smart systems? Are there architectures that support IoT security? We aim to systematically review the research work published on patterns and architectures for IoT security (and privacy). Then, we want to provide an analysis on that research landscape to answer our research questions. We follow the well-known guidelines for conducting systematic literature reviews. From thousands of candidate papers initially found in our search process, we have systematically distinguished and analyzed thirty-six (36) papers that have been peer-reviewed and published around patterns and architectures for IoT security and privacy in the last decade (January 2010–December 2020). Our analysis shows that there is a rise in the number of publications tending to patterns and architectures for IoT security in the last three years. We have not seen any approach of applying systematically architectures and patterns together that can address security (and privacy) concerns not only at the architectural level, but also at the network or IoT devices level. We also explored how the research contributions in the primary studies handle the different issues from the OWASP Internet of Things (IoT) top ten vulnerabilities list. Finally, we discuss the current gaps in this research area and how to fill in the gaps for promoting the utilization of patterns for IoT security and privacy by design.

Abstract With the increasing computing and networking capabilities, IoT devices and edge gateways have become part of a larger IoT–edge–cloud computing continuum, where processing and storage tasks are distributed across the whole network hierarchy, not concentrated only in the cloud. At the same time, this also introduced continuous delivery practices to the development of software components for network-connected gateways and sensing/actuating nodes. These devices are placed on end users’ premises and are characterized by continuously changing cyber-physical contexts, forcing software developers to maintain multiple application versions and frequently redeploy them on a distributed fleet of devices with respect to their current contexts. Doing this correctly and efficiently goes beyond manual capabilities and requires an intelligent and reliable automated solution. This paper describes a model-based approach to automatically assigning multiple software deployment plans to hundreds of edge gateways and connected IoT devices implemented in collaboration with a smart healthcare application provider. From a platform-specific model of an existing edge computing platform, we extract a platform-independent model that describes a list of target devices and a pool of available deployment plans. Next, we use constraint solving to automatically assign deployment plans to devices at once with respect to their specific contexts. The result is transformed back into the platform-specific model and includes a suitable deployment plan for each device, which is then consumed by our engine to deploy software components not only on edge gateways but also on their downstream IoT devices with constrained resources and connectivity. We validate the approach with a fleet deployment prototype integrated into a DevOps toolchain used by the partner application provider. Initial experiments demonstrate the viability of the approach and its usefulness in supporting DevOps for edge and IoT software development.

Billions of sensor-enabled IoT devices generate extreme amounts of e- waste. Because of the low cost and short lifespan of electronic components, it is often more convenient for consumers to buy a new device instead of re-using or re-purposing the old one. With the increased computing and connectivity capabilities, IoT devices can already receive code updates for new purposes (and thus extend their lifespan), but the cost of such operations often exceeds the price of device replacement due to constrained resources, hindered network connectivity, and distributed placement. This paper describes how these existing capabilities can enable last-mile software deployment at scale. We propose a hierarchical architecture for provisioning software updates from the cloud to terminal devices via edge gateways in a scalable and targeted manner. By enabling such an end-to-end software deployment architecture, the approach promotes hardware re-use via re-purposing and thus contributes to the creation of a more sustainable IoT.

We have entered a tremendous computerized rev-olution of the Internet of Things (IoT) era when everything is connected. The popularity of IoT systems makes security for the IoT of paramount importance. Security patterns consist of domain-independent time-proven security knowledge and expertise. Would they be applicable to develop secure IoT systems? We aim to draw a research landscape of patterns and architectures for IoT security by conducting a systematic literature review. From more than a thousand of candidate papers, we have systematically distinguished and analyzed twenty-two (22) papers that have been published around patterns and architectures for IoT security (and privacy). Our analysis shows a rise in the number of publications tending to security patterns and architectures in the last two years. Within this rise, we see that most patterns and architectures are applicable for all IoT systems, while some are limited within specific domains. However, there are gaps in this research area that can be filled in to promote the utilization of patterns for IoT security and privacy.

Edge computing brings software in close proximity to end users and IoT devices. Given the increasing number of distributed Edge devices with various contexts, as well as the widely adopted continuous delivery practices, software developers need to maintain multiple application versions and frequently (re-)deploy them to a fleet of many devices with respect to their contexts. Doing this correctly and efficiently goes beyond manual capabilities and requires employing an intelligent and reliable automated approach. Accordingly this paper describes a joint research with a Smart Healthcare application provider on a model-based approach to automatically assigning multiple software deployments to hundreds of Edge gateways. From a Platform-Specific Model obtained from the existing Edge computing platform, we extract a Platform-Independent Model that describes a list of target devices and a pool of available deployments. Next, we use constraint solving to automatically assign deployments to devices at once, given their specific contexts. The resulting solution is transformed back to the PSM as to proceed with software deployment accordingly. We validate the approach with a Fleet Deployment prototype integrated into the DevOps toolchain currently used by the application provider. Initial experiments demonstrate the viability of the approach and its usefulness in supporting DevOps in Edge computing applications.

Data under open licenses and in reusable formats, often referred to as ”open data”, is increasingly being made accessible by both public and private actors. Government institutions, municipalities, private companies and entrepreneurs are among the stakeholders either having visions of new open data-based services, or just looking for new ideas on potential innovations based on open data. It is, however, in both cases, often unclear to the service developers how the open data actually can be utilized. A main reason is that the data needs to be retrieved from multiple sources, understood, quality checked and processed. While gaining insights on possible services that can be created on the top of open data, a service developer has to undergo an iterative ”trying and failing” exercise of service prototyping. In order to be practically feasible, such a process needs to be agile and efficient. Open data from the transport sector is used as a case. The open transport data are characterized by many challenges common for open data in general, but also a few specific ones. One of those challenges is the need for combining (often real-time) data from rather many sources in order to create a new service. In this paper we propose an agile approach to early service prototyping and we try out the approach on an open transport data service. Finally, we propose the priorities for future work towards a comprehensive approach for agile prototyping of open transport data-based services.

Multiple tools have emerged to support the development as well as the continuous deployment of cloud-based software systems. However, currently, there is a lack of proper tool support for the continuous orchestration and deployment of software systems spanning across the IoT, edge, and cloud space. In particular, there is a lack of languages and abstractions that can support the orchestration and deployment of software services across vastly heterogeneous IoT infrastructures. In this paper, we present a tool supported framework for the continuous orchestration and deployment of IoT systems, named GeneSIS. In particular, GeneSIS enables to cope with the heterogeneity at each of the IoT, edge, and cloud levels and allows to control the orchestration and continuous deployment of software systems that executes across IoT, edge, and cloud infrastructures.

International audience

A recurring issue in generative approaches, in particular if they generate code for multiple target languages, is logging. How to ensure that logging is performed consistently for all the supported languages? How to ensure that the specific semantics of the source language, e.g. a modeling language or a domain-specific language, is reflected in the logs? How to expose logging concepts directly in the source language, so as to let developers specify what to log? This paper reports on our experience developing a concrete logging approach for ThingML, a textual modeling language built around asynchronous components, statecharts and a first-class action language, as well as a set of "compilers" targeting C, Go, Java and JavaScript.

While the number of cloud solutions is continuously increasing, the development and operation of large-scale and distributed cloud applications are still challenging. A major challenge is the lack of interoperability between the existing cloud solutions, which increases the complexity of maintaining and evolving complex applications potentially deployed across multiple cloud infrastructures and platforms. In this article, we show how the Cloud Modelling Framework leverages model-driven engineering and supports the DevOps ideas to tame this complexity by providing: (i) a domain-specific language for specifying the provisioning and deployment of multi-cloud applications, and (ii) a models@run-time environment for their continuous provisioning, deployment, and adaptation.

Industry 4.0 proposes the integration of the new generation of ICT solutions for the monitoring, adaptation, simulation, and optimisation of factories. With the democratization of sensors and actuators, factories and machine tools can now be sensorized and the data generated by these devices can be exploited, for instance, to optimize the utilization of the machines as well as their operation and maintenance. However, analysing the vast amount of data generated is resource demanding both in term of computing power and network bandwidth, thus requiring highly scalable solutions. This paper presents a novel big data platform for the management of machine generated data in the cloud. It brings together standard open source technologies which can be adapted to and deployed on different cloud infrastructures, hence reducing costs, minimising deployment difficulty and providing on-demand access to a virtually infinite set of computing, storage and network resources.

A models@runtime environment keeps a model in synchrony with a running system, this way a reasoning engine adapts the system by modifying this model. Existing models@runtime environments typically fail to let the user control what concepts form the model nor how the model is synchronised with the running system. This is yet mandatory in uncertain environments that are open, dynamic and heterogeneous. In this position paper we evolve the classical models@runtime architectural pattern to address this issue, together with some initial implementation results.

This position paper reports our current endeavour towards a model-driven engineering framework to support the dynamic configuration and deployment of complex data processing applications. In particular, our approach includes a domain-specific modelling language that abstracts the data processing tasks and at the same time exposes the control of how these tasks are deployed on specific platforms and resources. A modelling framework of model transformation and models@runtime engines realises the semi-automatic configuration and deployment of the applications based on the abstract models.

Abstract: Design of a system architecture normally refers to a pre-defined goal. We consider the goal as the desired (functional or non-functional) aspect that is achieved by providing functionalities or mechanisms that support it. For example, a security goal is supported by a set of security mechanisms. However, there are often several ways towards (partially or completely) achieving a goal, which we refer to as design alternatives. Each design alternative is composed of a subset of the mechanisms needed. Some mechanisms are more important than others, and it is not obvious to what degree the different design alternatives fulfill the goal. It can moreover often be difficult to see what combinations of the design alternatives would result in maximum coverage of the goal. We propose an approach to modeling and analysis of the goals of a system. The approach supports specification of the goal, specification of the design alternatives, a quantification of the degree of fulfillment, as well as a quantification of the degree of overlap with respect to the goal fulfillment across design alternatives. We also propose a visual representation of the degrees of fulfillment and the degrees of overlap. We have evaluated the approach on a case study, and the initial results indicate its feasibility. This paper presents the approach and the evaluation results. It also summarizes experiences and suggestions for further improvements. 1

Scalability and elasticity are key capabilities to tackle the variable workload of an application. Cloud elasticity offers opportunities to manage dynamically the underlying resources of an application and improve its scalability. However, managing scalability of cloud-based systems might lead to a management overhead. Self-adaptive systems are a well-known approach to tame this complexity. In this position paper, we propose an approach for the continuous design and management of scalability in multi-cloud systems. Our approach is based on a three-layer architecture and relies on two existing frameworks, namely ScaleDL and CloudML

In the landscape of cloud computing, the competition between providers has led to an ever growing number of cloud solutions offered to consumers. The ability to run and manage multi-cloud systems (i.e., applications on multiple clouds) allows exploiting the peculiarities of each cloud solution and hence optimising the performance, availability, and cost of the applications. However, these cloud solutions are typically heterogeneous and the provided features are often incompatible. This diversity hinders the proper exploitation of the full potential of cloud computing, since it prevents interoperability and promotes vendor lock-in, as well as it increases the complexity of development and administration of multi-cloud systems. This problem needs to be addressed promptly. In this paper, we provide a classification of the state-of-the-art of cloud solutions, and argue for the need for model-driven engineering techniques and methods facilitating the specification of provisioning, deployment, monitoring, and adaptation concerns of multi-cloud systems at design-time and their enactment at run-time.