Decision-Making Support for Data Integration in Cyber-Physical-System Architectures

Cyber-Physical Systems (CPS) design is a complex challenge involving physical and digital components working together to accomplish a specific goal. Integrating such systems involves combining data from various distributed Internet of Things (IoT) devices and cloud services to create meaningful insights and actions. Service-based IoT data integration involves several steps: collection, processing, analysis, and visualization. Adopting a holistic approach that considers physical and digital aspects is crucial when designing data integration in distributed CPS. Architectural design decisions are vital in shaping a CPS' functionality and system qualities, such as performance, security, and reliability. Although several patterns and practices for CPS architecture have been proposed, much of the knowledge in this area is informally discussed in the grey literature, e.g., in practitioner blogs and system documentation. As a result, this architectural knowledge is dispersed across many sources that are often inconsistent and based on personal experience. In this study, we present the results of a qualitative, in-depth study of the best practices and patterns of distributed CPS architecture as described by practitioners. We have developed a formal architecture decision model using a model-based qualitative research method. We aim to bridge the science-practice gap, enhance comprehension of practitioners' CPS approaches, and provide decision-making support

Decision-Making Support for Data Integration in Cyber-Physical-System Architectures

Cyber-Physical Systems (CPS) design is a complex challenge involving physical and digital components working together to accomplish a specific goal. Integrating such systems involves combining data from various distributed Internet of Things (IoT) devices and cloud services to create meaningful insights and actions. Service-based IoT data integration involves several steps: collection, processing, analysis, and visualization. Adopting a holistic approach that considers physical and digital aspects is crucial when designing data integration in distributed CPS. Architectural design decisions are vital in shaping a CPS' functionality and system qualities, such as performance, security, and reliability. Although several patterns and practices for CPS architecture have been proposed, much of the knowledge in this area is informally discussed in the grey literature, e.g., in practitioner blogs and system documentation. As a result, this architectural knowledge is dispersed across many sources that are often inconsistent and based on personal experience. In this study, we present the results of a qualitative, in-depth study of the best practices and patterns of distributed CPS architecture as described by practitioners. We have developed a formal architecture decision model using a model-based qualitative research method. We aim to bridge the science-practice gap, enhance comprehension of practitioners' CPS approaches, and provide decision-making support

Multi-Layer Cyber-Physical Security and Resilience for Smart Grid

The smart grid is a large-scale complex system that integrates communication technologies with the physical layer operation of the energy systems. Security and resilience mechanisms by design are important to provide guarantee operations for the system. This chapter provides a layered perspective of the smart grid security and discusses game and decision theory as a tool to model the interactions among system components and the interaction between attackers and the system. We discuss game-theoretic applications and challenges in the design of cross-layer robust and resilient controller, secure network routing protocol at the data communication and networking layers, and the challenges of the information security at the management layer of the grid. The chapter will discuss the future directions of using game-theoretic tools in addressing multi-layer security issues in the smart grid.Comment: 16 page

Methodology for Designing Decision Support Systems for Visualising and Mitigating Supply Chain Cyber Risk from IoT Technologies

This paper proposes a methodology for designing decision support systems for visualising and mitigating the Internet of Things cyber risks. Digital technologies present new cyber risk in the supply chain which are often not visible to companies participating in the supply chains. This study investigates how the Internet of Things cyber risks can be visualised and mitigated in the process of designing business and supply chain strategies. The emerging DSS methodology present new findings on how digital technologies affect business and supply chain systems. Through epistemological analysis, the article derives with a decision support system for visualising supply chain cyber risk from Internet of Things digital technologies. Such methods do not exist at present and this represents the first attempt to devise a decision support system that would enable practitioners to develop a step by step process for visualising, assessing and mitigating the emerging cyber risk from IoT technologies on shared infrastructure in legacy supply chain systems

Weak Resilience of Networked Control Systems

In this paper, we propose a method to establish a networked control system that maintains its stability in the presence of certain undesirable incidents on local controllers. We call such networked control systems weakly resilient. We first derive a necessary and sufficient condition for the weak resilience of networked systems. Networked systems do not generally satisfy this condition. Therefore, we provide a method for designing a compensator which ensures the weak resilience of the compensated system. Finally, we illustrate the efficiency of the proposed method by a power system example based on the IEEE 14-bus test system