Meta-Information driven Modeling of Services for Experimentable Digital Twins

Ensuring the correct behavior of cyber physical systems at run time is of critical importance for their safe deployment. Any malfunctioning of such systems should be detected in a timely manner for further actions. This paper addresses the issue of how quickly a monitor raises an alarm after the occurrence of a failure in cyber physical systems. Towards this end, it introduces a class of systems called exponentially converging monitorable systems. The paper shows that failures in these systems can be detected fast by employing the traditional threshold monitors. It shows that the expected failure detection time for exponentially converging monitorable systems has logarithmic relationship with the inverse of the chosen threshold value. The paper identifies well defined natural classes of these systems. Experimental results are presented that confirm the theoretical results on the relationship between the failure detection time and the chosen threshold values.

Time plays a major role in the specification of Cyber-physical Systems (CPS) behavior with concurrency, timeliness, asynchrony, and resource limits as their main characteristics. In addition to timeliness , the specification of CPS needs to assess and unambiguously define its behavior with respect to the other Quality-of-Information (QoI) properties: (1) Correctness, (2) Completeness, (3) Consistency, and (4) Accuracy. Very often, CPS need to handle these QoI properties, and any combination thereof, multiple times when performing computation and communication processes. However, a model-driven and systematic approach to specify CPS behavior that jointly considers combined QoI aspects is possible but missing in existing methodologies. As the first contribution of this work, we provide an extension to an established model of computation (MoC) based on “Functions driven by Finite State Machine” (FunState) to enable a model-driven composition mechanism to create CPS behavior specifications from reusable components. Second, we present a novel set of design patterns to illustrate the modeling of QoI-aware CPS specifications that can be applied in several state-of-the-art Electronic System Level (ESL) methodologies. The time semantics of the MoC are formalized using the tagged-signal-model, and the presented model-driven approach enables the composition of multiple design patterns. The main benefits of the presented model-driven approach and design patterns to create CPS specifications are as follows: (a) reduce modeling effort, errors, and time through the reuse of known recipes to re-incurring tasks and allow to automatically generate repetitive control flows based on extended Finite State Machines; (b) increase system robustness and facilitate the creation of holistic QoI management allowing to unambiguously define system behavior for scenarios with single/multiple QoI requirement violations in different models of computation; (c) dynamically validate timing behavior of system implementations to enable a multi-objective optimization of nonfunctional properties that influence CPS timing. We demonstrate the aforementioned benefits through the modeling and evaluation of an infrastructure-assisted automated driving case study using Infrastructure-to-Vehicle (I2V) communications to distribute QoI critical road environment information.

Non-Commutativity, Incompatibility, Emergent Behavior and Decision Support Systems

The paper considers the idea of detecting security incidents and anomalies in physical access control systems (PACS) based on combined use of different types of user identification. As an example, an implementation of PACS is considered that performs identification by RFID cards and face recognition. The architecture of such system is introduced. A BPMN diagram for RFID identification of employees by entry and exit to the organization premises is built. A basic data model is proposed for storing information about users, devices, and events. Situations that can be interpreted as anomalous in the considered system are described and classified. The theoretical contribution of the work consists in enhancing the approaches to modeling user behavior in cyber-physical systems, especially in case when this modeling is performed on the basis of heterogeneous data collected from sources of different nature.

Cyber-physical production systems are highly flexible systems that enable adaptive production processes. In these systems, all participants of the production process possess individual information about themselves and are equipped with sensors, actors, and communication interfaces. They can interact with each other and autonomously develop and execute process relevant decisions. For each component, a standard process sequence and alternative process sequences are defined. If a deviation in the standard process occurs during the production of an individual component, the process participants can interact with each other and autonomously define an appropriate response strategy and execute it by using actors of the participants and the intralogistics. Regardless of the deviation, the manufacturing and assembly process of individual components in cyber-physical production systems can still proceed. For this purpose, process deviations and the response behavior of cyber-physical production systems are analyzed, modeled, and simulated, to illustrate the benefits of cyber-physical production systems and to develop a process deviation management system for actual, physical production systems based on cyber-physical systems.

As computers and communication bandwidth become ever faster and cheaper, computing and communication capabilities are embedded in all types of objects and structures in the physical environment. Harnessing these capabilities to bridge the cyber-world with the physical world will allow the development of applications with great societal impact and economic benefit. At the heart of these applications are cyber-physical systems consisting of integrated computational and communication cores that interact with the physical world, with intelligence provided by embedded software. Cyber Physical Systems (CPS) are engineered systems that provide tight integration of and coordination between the cyber world of computing and communications and the physical world. CPS are to meet the needs of the new generation of engineered systems that are highly dependable, efficiently produced and certified, and capable of advanced performance in computation, communication, and control. CPS will transform how we interact with and control the physical world around us, as the Internet transformed how we interact and communicate with one another and revolutionized how and where we access information. One application domain of CPS is Medical Cyber-Physical Systems (MCPS), which are life-critical, context-aware, networked systems of medical devices. Medical device industry is undergoing a transformation, embracing the potential of embedded software and network connectivity. Instead of stand-alone devices that can be designed, certified, and used to treat patients independent of each other, distributed systems that simultaneously control multiple aspects of the patient's physiology are increasingly used in hospitals to provide high-quality continuous care for patients. The combination of embedded software controlling medical devices, networking capabilities, and complicated physiological dynamics of patient bodies makes MCPS complex. The need to design complex MCPS that are both safe and effective presents numerous challenges, including achieving high assurance in system software, interoperability, context-aware intelligence, autonomy, security and privacy, and device certification. In this talk, I will discuss these challenges in developing MCPS and present some of our work in addressing them, and several open research and development issues.

Cyber-Physical Systems (CPSs) seamlessly integrate lots of heterogeneous cyber and physical entities to realize precise controlling of the physical world. Traditional service-oriented models are insufficient to effectively compose the atomic services provided by cyber and physical entities to implement a specific task. In this paper, we presented an efficient context-sensitive service composition framework for precise controlling in CPSs. We firstly proposed an ontology model for context-sensitive specification of the service abilities of physical entities. And then, the business workflow logic model was given for functionality decomposition of a task. Furthermore, the key sub-modules in service composition including the atomic service clustering scheme and QoS-aware service selection algorithm were initially investigated. Our proposed service composition framework is helpful to automate the composition process under real-time constraints and under various physical resource constraints. This preliminary solution for service composition in CPS is important and helpful for in-depth study and practical applications of CPSs.

Reliability assessment of a power system with cyber-physical interactive operation of photovoltaic systems

Deep Reinforcement Learning (DRL) has the potential to be used for synthesizing feedback controllers (agents) for various complex systems with unknown dynamics. These systems are expected to satisfy diverse safety and liveness properties best captured using temporal logic. In RL, the reward function plays a crucial role in specifying the desired behaviour of these agents. However, the problem of designing the reward function for an RL agent to satisfy complex temporal logic specifications has received limited attention in the literature. To address this, we provide a systematic way of generating rewards in real-time by using the quantitative semantics of Signal Temporal Logic (STL), a widely used temporal logic to specify the behaviour of cyber-physical systems. We propose a new quantitative semantics for STL having several desirable properties, making it suitable for reward generation. We evaluate our STL-based reinforcement learning mechanism on several complex continuous control benchmarks and compare our STL semantics with those available in the literature in terms of their efficacy in synthesizing the controller agent. Experimental results establish our new semantics to be the most suitable for synthesizing feedback controllers for complex continuous dynamical systems through reinforcement learning.

Platoon vehicles with communication capability can dynamically form a mobile wireless network on the road with two types of communications: vehicle to vehicle (V2V) and vehicle to infrastructure (V2I). This complex networked control system integrates computing, communication and control technologies being considered a platoon-based vehicular cyberphysical system (CPS), with a closed feedback loop between the cyber process and the physical one. Platoon control using vehicular communication notably improves traffic safety and efficiency. In this paper, an application of cooperative platoonbased vehicular CPS with V2V communications is presented. The networked control architecture is of a Cooperative Adaptive Cruise Control (CACC) type where the vehicle only communicates with the preceding one. The standard control structure of the CACC system is composed of a PID CC controller for the leader vehicle and for the follower vehicle a GPC ACC feedback controller with implicit feed-forward compensator. The networked controllers of the platoon CACC architecture are obtained using CPS-oriented design approaches and the performances were tested via simulation considering a platoon with a leader and 15 followers.

Self-healing is becoming an essential behavior of smart Cyber-Physical Systems (CPSs), which enables them to recover from faults by themselves. Such behaviors make decisions autonomously at runtime and they often operate in an uncertain physical environment making testing even more challenging. To this end, we propose Fragility-Oriented Testing (FOT), which relies on model execution and reinforcement learning to cost-effectively test self-healing behaviors of CPSs in the presence of environmental uncertainty. We evaluated FOT’s performance by comparing it with a Coverage-Oriented Testing (COT) algorithm. Evaluation results show that FOT significantly outperformed COT for testing nine self-healing behaviors implemented in three case studies. On average, FOT managed to find 80% more faults than COT and for cases when both FOT and COT found the same faults, FOT took on average 50% less time than COT.

Recently, cyber-physical systems (CPS) have emerged as a promising research paradigm. This development is the convergence of control, communication, and computation. As computing and communication capabilities have become both faster and cheaper, we can expect to find them embedded in diverse objects and structures in the physical environment. This provides the basis for applications that link the "cyberworld" of computing and communications with the physical world, thereby promising great potential for significant social and economic impact. Unlike traditional embedded systems that primarily focus on computing elements, CPS also takes into account the physical, real-world components that interact with computing elements in cyber space. Examples of CPS encompass a broad range of complex man-made systems such as avionics, vehicles, transportation, healthcare, smart grid systems, and other fields.

Cyber-Physical Systems are one of the emerging technologies which involve the integration of cyber system physical and control systems. This Cyber-physical System automates the industrial process like manufacturing, monitoring and control. Since the system involves three different cyber, physical and control optimization domains, such systems are complex in nature and cannot be done with a traditional optimization mechanism. Machine learning and deep learning are efficient mechanisms to model the behavior of such complex systems for design and optimization. In this work, the application of machine learning mechanisms in the cyber-physical system for various purposes like security, re-organization, and scheduling. This systematic review will give more insight into the latest application and mechanism of machine learning and deep learning for the cyber-physical system.

In cyber-physical systems (CPSs), embedded computing systems and communication capability are used to streamline and fortify the operation of a physical system. Intelligent critical infrastructure systems are among the most important CPSs and also prime examples of pervasive computing systems, as they exploit computing to provide anytime, anywhere transparent services. While the added intelligence offers the promise of increased utilization, its impact must be assessed, as unrestricted cyber control can actually lower the reliability of existing infrastructure systems. As a practical example, water distribution networks (WDNs) are an emerging CPS domain. Physical components, e.g., valves, pipes, and reservoirs, are coupled with the hardware and software that support intelligent water allocation. An example is depicted in Fig. 1. The primary goal of WDNs is to provide a dependable source of potable water to the public. Information such as demand patterns, water quantity (flow and pressure head), and water quality (contaminants and minerals) is critical in achieving this goal, and beneficial in guiding maintenance efforts and identifying vulnerable areas requiring fortification and/or monitoring. Sensors dispersed in the physical infrastructure collect this information, which is fed to algorithms (often distributed) running on the cyber infrastructure. These algorithms provide decision support to hardware controllers that are used to manage the allocation (quantity) and chemical composition (quality) of the water. As WDNs become larger and more complex, their reliability comes into question. Modeling and simulation can be used to analyze CPS performability, as direct observation of critical infrastructure is often infeasible. Accurate representation of a CPS encompasses three aspects: computing, communication, and the physical infrastructure. Fundamental differences exist between the attributes of cyber and physical components, significantly complicating representation of their behavior with a single comprehensive model or simulation tool. Specialized simulation tools exist for the engineering domains represented in critical infrastructure, including power, water, and transportation. These tools have been created with the objective of accurately reflecting the operation of the physical system, at high spatial and temporal resolution. As is the case with specialized models of physical systems, intelligent control is not reflected in these tools. Despite the existence of simulation tools for cyber aspects such as computing and communication, differences in temporal resolution and 7

UAVs (unmanned aerial vehicles) have gained considerable interest from the research community due to their widespread use in military and civil applications. The unique features of UAVs like easy deployment, high mobility, ability to fly, and hover have provided a practical and cost-effective solution to various complex applications. Recently, with the emergence of cyber-physical systems (CPS), it has become possible to integrate communication and computational capabilities with the physical world. The UAV system can also be viewed as a CPS since they are equipped with all the necessary capabilities like sensing, communication, control, and computation to monitor and control processes in the physical world. In this chapter, we aim to present basic concepts related to UAV networks, the architecture of UAV-enabled CPS, and issues associated with sensing, communication, computation, and control units. Additionally, the potential usage of UAVs in CPS applications is also highlighted.