A Private and Efficient Mechanism for Data Uploading in Smart Cyber-Physical Systems

A dramatic transformation of our technical world towards smart cyber-physical systems can be currently observed. This transformation results in a networked technical world where besides the embedded systems with their interaction with the physical world the interconnection of these nodes in the cyber world becomes a key element. Furthermore, there is a strong trend towards smart systems where artificial intelligence techniques and in particular machine learning is employed to make software behave accordingly. This raises the question whether our capabilities to model these future embedded systems are ready to tackle the resulting challenges. In this presentation, we will first discuss how extensions of graph transformation systems can be employed to design and analyse the envisioned future cyber-physical systems with an emphasis on the synergies networking can offer and then characterise which challenges for the design, production, and operation of these systems and how they can be tacked with graph transformation systems. We will therefore discuss to what extent our current capabilities in particular concerning engineering with graph transformation systems match these challenges and where substantial improvements for the graph transformation systems have been crucial and will be crucial in the future. Models are used in classical engineering to plan systems upfront to maximise envisioned properties resp. minimise cost. For smart cyber-physical systems this decoupling of development-time and run-time considerations vanishes, and self-adaptation and runtime models have been advocated as concepts to shift some considerations to run-time. We will review the underlying causes for this shift to run-time, discuss some our work with graph transformation systems in this direction, and outline related open challenges and implications for future work for graph transformation systems to engineer smart cyber-physical systems.

At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems.

Cyber-physical systems are a kind of emerging large-scale distributed systems that are integrations of computation, networking, and physical worlds. Cyber physical systems are increasing scale and complexity with the growing pervasiveness of ICT and the development of the smart worlds such as smart factory, smart robotics, smart grids, smart cities, smart vehicles, smart healthcare systems, and smart homes. The growing interconnection of physical and virtual worlds of complex cyber physical systems, and the development of increasingly sophisticated intelligence techniques, has given rise to the next generation of CPS, that is referred to as smart cyber-physical systems (sCPS) which can efficiently operate, manage and control physical world and virtual world, and offer broad range of novel applications and services. Thus, smart cyber physical systems are complex cyber physical systems endowed with intelligent abilities. The main characteristic of such a new generation of smart cyber physical systems is the ability to interact with the physical worlds and adapt to new working environments through distributed intelligent mechanisms that executing and operating at the unit (single device), cluster (groups of devices) and network level (the network of devices). The properties like uncertainties, safety, reliability, and performance requirement, spatial-temporal requirement, and adaptation ability are very important, modeling smart cyber-physical systems must face these challenges and satisfy these specific requiremnts. In order to specify and model such kind of systems, we propose a new paradigm for specifying and modeling smart cyber physical systems based on software defined approach using Modelicaml. Software defined approach is a new way that help abstract the real world and virtual world with software components, this approach can simplify system specification, modeling and design. The main aim of ModelicaML is to enable an efficient and effective way to use Modelica, UML and SysML models reusing notations that are also used for software modeling based on software defined approach. The effectiveness of the approach is demonstrated with a case study of Vehicular Ad-hoc NETwork.

An efficient and scalable privacy preserving algorithm for big data and data streams

The contemporary advancement in smart technology has led to a greater business prospective. Smart system like cyber-physical system (CPS) has become prevalent in many area of applications. Challenges in integration of CPS with group of network has physical components to sense the state of changes in the real world is paving way towards the introduction of enhanced security mechanism. In this paper, we propose a security framework for group of network is a pseudo-free to communicate with the cyber physical system to assure high security. Pseudo-freeness is a very strong assumption of security used in cryptography. Group is said to be pseudo-free group, if an adversary who is allowed to see group description, is unable to solve non-trivial equations. Non-trivial equations are the equations that have no solution in a free group. The notion of pseudo-freeness was introduced with few open problems whether such groups exists in cryptosystem or not. The conjecture that the RSA group is pseudo-free has been settled for case of static adversary who is given only the group description and its goal is to come up with an equation and solution. Later on the notion of pseudo-freeness has been extended to adaptive adversary for case of univariate equations. In case of adaptive pseudo-free groups, the adversary learns solutions of some non-trivial equations before producing a new equation. Here we make extension to the notion of pseudo-free groups from univariate to multivariate equations and system of equations in face of adaptive adversary. We define and prove the security of adaptive pseudo-free groups for case of multivariate equations and system of equations. According to the results, this framework can be used as a system design for a group to have strong security with cyber physical system.

A Framework for Ethics in Cyber-Physical-Human Systems

Security and Privacy Aspects in Cyber Physical Systems

Detecting anomalies and attacks in smart cyber-physical systems are of paramount importance owing to their growing prominence in controlling critical systems. However, this is a challenging task due to the heterogeneity and variety of components of a CPS, and the complex relationships between sensed values and potential attacks or anomalies. Such complex relationships are results of physical constraints and domain norms which exist in many CPS domains. In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ABATe</i> , an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> utomatic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i> ehavioral <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> bstraction <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Te</i> chnique based on neural networks for detecting anomalies in smart cyber-physical systems. Unlike traditional techniques which abstract the statistical properties of different sensor values, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ABATe</i> learns complex relationships between event vectors from normal operational data available in abundance with smart CPS and uses this abstracted model to detect anomalies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ABATe</i> detected more than 88 percent of attacks in the publicly available SWaT dataset featuring data from a scaled down sewage water treatment plant with a very low false positive rate of 1 percent. We also evaluated our technique's ability to capture domain semantics and multi-domain adaptability using a real-world automotive dataset, as well as a synthetic dataset.

To be able to provide appropriate services in social and human application contexts, smart cyber-physical systems (S-CPSs) need ampliative reasoning and decision-making (ARDM) mechanisms. As one option, procedural abduction (PA) is suggested for self-managing S-CPSs. PA is a knowledge-based computation and learning mechanism. The objective of this article is to provide a comprehensive description of the computational framework proposed for PA. Towards this end, first the essence of smart cyber-physical systems is discussed. Then, the main recent research results related to computational abduction and ampliative reasoning are discussed. PA facilitates beliefs-driven contemplation of the momentary performance of S-CPSs, including a ‘best option’-based setting of the servicing objective and realization of any demanded adaptation. The computational framework of PA includes eight clusters of computational activities: (i) run-time extraction of signals and data by sensing, (ii) recognition of events, (iii) inferring about existing situations, (iv) building awareness of the state and circumstances of operation, (v) devising alternative performance enhancement strategies, (vi) deciding on the best system adaptation, (vii) devising and scheduling the implied interventions, and (viii) actuating effectors and controls. Several cognitive algorithms and computational actions are used to implement PA in a compositional manner. PA necessitates not only a synergic interoperation of the algorithms, but also an objective-dependent fusion of the pre-programmed and the run time acquired chunks of knowledge. A fully fledged implementation of PA is underway, which will make verification and validation possible in the context of various smart CPSs.

The proliferation of Smart Cyber-Physical Systems (SCPS) is increasingly blurring the boundaries between physical and virtual entities. This trend is revolutionizing multiple application domains along the whole human activity spectrum, while pushing the growth of new businesses and innovations such as smart manufacturing, cities and transportation systems, as well as personalized healthcare. Technological advances in the Internet of Things, Big Data, Cloud Computing and Artificial Intelligence have effected tremendous progress toward the autonomic control of SCPS operations. However, the inherently dynamic nature of physical environments challenges SCPS’ ability to perform adequate control actions over managed physical assets in myriad of contexts. From a design perspective, this issue is related to the system states of operation that cannot be predicted entirely at design time, and the consequential need to define adequate capabilities for run-time self-adaptation and self-evolution. Nevertheless, adaptation and evolution actions must be assessed before realizing them in the managed system in order to ensure resiliency while minimizing the risks. Therefore, the design of SCPS must address not only dependable autonomy but also operational resiliency. In light of this, the contribution of this paper is threefold. First, we propose a reference architecture for designing dependable and resilient SCPS that integrates concepts from the research areas of Digital Twin, Adaptive Control and Autonomic Computing. Second, we propose a model identification mechanism for guiding self-evolution, based on continuous experimentation, evolutionary optimization and dynamic simulation, as the architecture’s first major component for dependable autonomy. Third, we propose an adjustment mechanism for self-adaptation, based on gradient descent, as the architecture’s second major component, addressing operational resiliency. Our contributions aim to further advance the research of reliable self-adaptation and self-evolution mechanisms and their inclusion in the design of SCPS. Finally, we evaluate our contributions by implementing prototypes and showing their viability using real data from a case study in the domain of intelligent transportation systems.

The proliferation of Smart Cyber-Physical Systems (SCPS) is increasingly blurring the boundaries between physical and virtual entities. This trend is revolutionizing multiple application domains along the whole human activity spectrum, while pushing the growth of new businesses and innovations such as smart manufacturing, cities and transportation systems, as well as personalized healthcare. Technological advances in the Internet of Things, Big Data, Cloud Computing and Artificial Intelligence have effected tremendous progress toward the autonomic control of SCPS operations. However, the inherently dynamic nature of physical environments challenges SCPS’ ability to perform adequate control actions over managed physical assets in myriad of contexts. From a design perspective, this issue is related to the system states of operation that cannot be predicted entirely at design time, and the consequential need to define adequate capabilities for run-time self-adaptation and self-evolution. Nevertheless, adaptation and evolution actions must be assessed before realizing them in the managed system in order to ensure resiliency while minimizing the risks. Therefore, the design of SCPS must address not only dependable autonomy but also operational resiliency. In light of this, the contribution of this paper is threefold. First, we propose a reference architecture for designing dependable and resilient SCPS that integrates concepts from the research areas of Digital Twin, Adaptive Control and Autonomic Computing. Second, we propose a model identification mechanism for guiding self-evolution, based on continuous experimentation, evolutionary optimization and dynamic simulation, as the architecture’s first major component for dependable autonomy. Third, we propose an adjustment mechanism for self-adaptation, based on gradient descent, as the architecture’s second major component, addressing operational resiliency. Our contributions aim to further advance the research of reliable self-adaptation and self-evolution mechanisms and their inclusion in the design of SCPS. Finally, we evaluate our contributions by implementing prototypes and showing their viability using real data from a case study in the domain of intelligent transportation systems.

In this paper, we consider the energy management problem for smart cyber-physical power systems with conventional utility companies, microgrids (MGs) and customers. We have adopted a game-theoretical approach to model the problem as a hierarchical game, which takes the interactions and interconnections among utility companies, MGs and customers into consideration. We have considered two completely different situations depending on whether utility companies can enforce their strategies upon MGs and customers, and have modelled the problem as a two-stage Stackelberg game and three-stage Stackelberg game, respectively. The backward induction method is used to analyse the proposed games and closed-form expressions are derived for optimum strategies. We prove that there exists a unique Nash equilibrium for each non-cooperative price competition game, and these Nash equilibria constitute the Stackelberg equilibrium. Simulation results show the effectiveness of the proposed algorithm and the relationships among system parameters.

A smart cyber physical multi-source energy system for an electric vehicle prototype

Editorial: Smart Cyber–Physical Systems: Toward Pervasive Intelligence systems

Smart cities, businesses, workplaces, and even residences have all been converged by the Internet of Things (IoT). The types and characteristics of these devices vary depending on the industry 4.0 and have rapidly increased recently, especially in smart homes. These gadgets can expose users to serious cyber dangers because of a variety of computing constraints and vulnerabilities in the security-by-design concept. The smart home network testbed setup presented in this study is used to evaluate and validate the protection of the smart cyber-physical system. The context-aware threat intelligence and response model identifies the states of the aligned smart devices to distinguish between real-world typical and attack scenarios. It then dynamically writes specific rules for protection against potential cyber threats. The context-aware model is trained on IoT Research and Innovation Lab - Smart Home System (IRIL-SHS) testbed dataset. The labeled dataset is utilized to create a random forest model, which is subsequently used to train and test the context-aware threat intelligence SHS model's effectiveness and performance. Finally, the model's logic is used to gain rules to be included in Suricata signatures and the firewall rulesets for the response system. Significant values of the measuring parameters were found in the results. The presented model can be used for the real-time security of smart home cyber-physical systems and develops a vision of security challenges for Industry 4.0.