Biblio

A kitchen underpinned by the Internet of Things (IoT) requires the management of complex procedural processes. This is due to the fact that when supporting an end-user in the preparation of even only one dish, various devices may need to coordinate with each other. Additionally, it is challenging— yet desirable—to enable an end-user to program their kitchen devices according to their preferred behaviour and to allow them to visualise and track their cooking workflows. In this paper, we compared two semantic representations, namely, Behaviour Trees and the DX-MAN model. We analysed these representations based on their suitability for a range of end-users (i.e., novice to experienced). The methodology required the analysis of smart kitchen user requirements, from which we inferred that the main architectural requirements for IoT cooking workflows are variability and compositionality. Guided by the user requirements, we examined various scenarios and analysed workflow complexity and feasibility for each representation. On the one hand, we found that execution complexity tends to be higher on Behaviour Trees. However, due to their fallback node, they provide more transparency on how to recover from unprecedented circumstances. On the other hand, parameter complexity tends to be somewhat higher for the DX-MAN model. Nevertheless, the DX-MAN model can be favourable due to its compositionality aspect and the ease of visualisation it can offer.

To conceive a CPS is a complex and multidisciplinary endeavour involving different stakeholders, potentially using a plethora of different languages to describe their views of the system at different levels of abstraction. Model-Driven Engineering comes, precisely, as a methodological approach to tackle the complexity of systems development with models as first-class citizens in the development process. The measure of realism of these models with respect to the real (sub)system is called fidelity. Usually, different models with different fidelity are then developed during the development process. Additionally, it is very common that the development process of CPS includes an incremental (and collaborative) use of simulations to study the behaviour emerging from the heterogeneous models of the system. Currently, the different models, with different fidelity, are managed in an ad hoc manner. Consequently, when a (Co)simulation is used to study a specific property of the system, the choice of the different models and their setup is made manually in a non-tractable way. In this paper we propose a structured new vision to CPS development, where the notion of simulation goal and multi-fidelity simulation unit are first-class citizens. The goal is to make a clear link between the system requirements, the system properties, the simulation goal and the multi-fidelity simulation unit. The outcome of this framework is a way to automatically determine the model at an adequate fidelity level suitable for answering a specific simulation goal.

IoT networks today face a myriad of security vulnerabilities in their infrastructure due to its wide attack surface. Large-scale networks are increasingly adopting a Software-Defined Networking approach, it allows for simplified network control and management through network virtualization. Since traditional security mechanisms are incapable of handling virtualized environments, SDSec or Software-Defined Security is introduced as a solution to support virtualized infrastructure, specifically aimed at providing security solutions to SDN frameworks. To further aid large scale design and development of SDN frameworks, Model-Driven Engineering (MDE) has been proposed to be used at the design phase, since abstraction, automation and analysis are inherently key aspects of MDE. This provides an efficient approach to reducing large problems through models that abstract away the complex technicality of the total system. Making adaptations to these models to address security issues faced in IoT networks, largely reduces cost and improves efficiency. These models can be simulated, analysed and supports architecture model adaptation; model changes are then reflected back to the real system. We propose a model-driven security approach for SDSec networks that can self-adapt using machine learning to mitigate security threats. The overall design time changes can be monitored at run time through machine learning techniques (e.g. deep, reinforcement learning) for real time analysis. This approach can be tested in IoT simulation environments, for instance using the CAPS IoT modeling and simulation framework. Using self-adaptation of models and advanced machine learning for data analysis would ensure that the SDSec architecture adapts and improves over time. This largely reduces the overall attack surface to achieve improved end-to-end security in IoT environments.

Safety and security of complex critical infrastructures are very important for economic, environmental and social reasons. The complexity of these systems introduces difficulties in the identification of safety and security risks that emerge from interdisciplinary interactions and dependencies. The discovery of safety and security design weaknesses late in the design process and during system operation can lead to increased costs, additional system complexity, delays and possibly undesirable compromises to address safety and security weaknesses.