Complex Cyber-physical Systems Research Articles

The Evolution Mechanism of Correctness for Cyber-Physical System

Cyber-physical Systems (CPS) are widely used in all areas of life. Ensuring the correctness of CPS remains an enduring challenge during its development and design phases. One approach to ascertain the correctness of CPS is behavior equivalence based on bisimulation. However, whether the implementation development process develops in the right direction has an important impact on obtaining the correct system implementation. Especially, the development process of complex CPS often yields a multitude of implementation versions, the correlation among these versions partly signifies the correctness of the development process. This paper formalizes the relationship between the implementation versions and establishes a formal description of the development process progressing in the correct direction, leveraging the concept of a “limit idea.” Initially, we introduce the concept of “limit bisimulation” for CPS systems to delineate implementations acquired during the CPS development process. Specific limit bisimulations are demonstrated to represent distinct scenarios within the development process. Subsequently, the convergence mechanism of implementations is modeled through bisimulation limits, encapsulating the CPS’s specification as the ultimate goal of obtained implementations during the development process. Finally, the congruence property of the bisimulation limit is proved, which accounts for the relation between specification and implementation versions can be decomposed into a refinement hierarchy. The limit theorem of bisimulation in CPS can help the designer and developer of CPS to comprehend the development course better.

Distributed Multi-objective Optimization in Cyber-Physical Energy Systems

Managing complex Cyber-Physical Energy Systems (CPES) requires solving various optimization problems with multiple objectives and constraints. As distributed control architectures are becoming more popular in CPES for certain tasks due to their flexibility, robustness, and privacy protection, multi-objective optimization must also be distributed. For this purpose, we present MO-COHDA, a fully distributed, agent-based algorithm, for solving multi-objective optimization problems of CPES. MO-COHDA allows an easy and flexible adaptation to different use cases and integration of custom functionality. To evaluate the effectiveness of MO-COHDA, we compare it to a central NSGA-2 algorithm using multi-objective benchmark functions from the ZDT problem suite. The results show that MO-COHDA can approximate the reference front of the benchmark problems well and is suitable for solving multi-objective optimization problems. In addition, an example use case of scheduling a group of generation units while optimizing three different objectives was evaluated to show how MO-COHDA can be easily applied to real-world optimization problems in CPES.

Observer‐based event‐triggered consensus control of nonlinear cyber‐physical systems under backlash‐like hysteresis and denial‐of‐service attacks

AbstractIn this article, we investigate the problem of event‐triggered distributed consensus control for a class of complex cyber‐physical systems (CPSs), where unknown backlash‐like hysteresis inputs and denial‐of‐service (DoS) attacks are considered in actuators and communication channels, respectively. In the first place, when communication networks are subjected to malicious DoS attacks, the connection weights of network communication topologies experience disruptions. To solve this issue, this work proposes a solution by constructing topological models for communication networks employing switching topologies and utilizing observers to estimate the combined measurement errors during the time interval of DoS attacks. Secondly, to address the repercussions of DoS attacks on control units and to optimize the utilization of communication resources, a distributed control protocol based on an event‐triggered scheme and employing locally deployed estimators is designed to significantly decrease the update frequency of control signals. Meanwhile, utilizing Lyapunov stability theory, the boundedness of all signals in the closed‐loop system is validated, thereby accomplishing the goal of consensus control. At last, the efficacy of the proposed control scheme is demonstrated through two simulation examples.

PolyARBerNN: A Neural Network Guided Solver and Optimizer for Bounded Polynomial Inequalities

Constraints solvers play a significant role in the analysis, synthesis, and formal verification of complex cyber-physical systems. In this article, we study the problem of designing a scalable constraints solver for an important class of constraints named polynomial constraint inequalities (also known as nonlinear real arithmetic theory). In this article, we introduce a solver named PolyARBerNN that uses convex polynomials as abstractions for highly nonlinears polynomials. Such abstractions were previously shown to be powerful to prune the search space and restrict the usage of sound and complete solvers to small search spaces. Compared with the previous efforts on using convex abstractions, PolyARBerNN provides three main contributions namely (i) a neural network guided abstraction refinement procedure that helps selecting the right abstraction out of a set of pre-defined abstractions, (ii) a Bernstein polynomial-based search space pruning mechanism that can be used to compute tight estimates of the polynomial maximum and minimum values which can be used as an additional abstraction of the polynomials, and (iii) an optimizer that transforms polynomial objective functions into polynomial constraints (on the gradient of the objective function) whose solutions are guaranteed to be close to the global optima. These enhancements together allowed the PolyARBerNN solver to solve complex instances and scales more favorably compared to the state-of-the-art nonlinear real arithmetic solvers while maintaining the soundness and completeness of the resulting solver. In particular, our test benches show that PolyARBerNN achieved 100X speedup compared with Z3 8.9, Yices 2.6, and PVS (a solver that uses Bernstein expansion to solve multivariate polynomial constraints) on a variety of standard test benches. Finally, we implemented an optimizer called PolyAROpt that uses PolyARBerNN to solve constrained polynomial optimization problems. Numerical results show that PolyAROpt is able to solve high-dimensional and high order polynomial optimization problems with higher speed compared to the built-in optimizer in the Z3 8.9 solver.

Complying with ISO 26262 and ISO/SAE 21434: A Safety and Security Co-Analysis Method for Intelligent Connected Vehicle.

A cyber-physical system (CPS) integrates communication and automation technologies into the operational processes of physical systems. Nowadays, as a complex CPS, an intelligent connected vehicle (ICV) may be exposed to accidental functional failures and malicious attacks. Therefore, ensuring the ICV's safety and security is crucial. Traditional safety/security analysis methods, such as failure mode and effect analysis and attack tree analysis, cannot provide a comprehensive analysis for the interactions between the system components of the ICV. In this work, we merge system-theoretic process analysis (STPA) with the concept phase of ISO 26262 and ISO/SAE 21434. We focus on the interactions between components while analyzing the safety and security of ICVs to reduce redundant efforts and inconsistencies in determining safety and security requirements. To conquer STPA's abstraction in describing causal scenarios, we improved the physical component diagram of STPA-SafeSec by adding interface elements. In addition, we proposed the loss scenario tree to describe specific scenarios that lead to unsafe/unsecure control actions. After hazard/threat analysis, a unified risk assessment process is proposed to ensure consistency in assessment criteria and to streamline the process. A case study is implemented on the autonomous emergency braking system to demonstrate the validation of the proposed method.

The convergence of Digital Twins and Distributed Ledger Technologies: A systematic literature review and an architectural proposal

In recent years, the emerging Digital Twin (DT) technology is playing a key role in fostering the transition towards the Industry 4.0. DTs, representing virtual replicas of physical objects, products or processes established thanks to a bidirectional continuous flow of information between the physical and the virtual world, are currently adopted in multiple domains such as manufacturing, aerospace, automotive, energy, construction, smart cities and smart mobility, etc.. DTs live together with the physical system they replicate, receiving the same data and often triggering specific control actions that directly impact on the real system status. When dealing with DTs of complex Cyber-Physical Systems (CPSs), the data sources may be heterogeneous and untrustworthy, which requires the DT to be able to address security issues related to data in transit from/to the physical twin and data at rest. A possible way to address these data security issues consists in adopting Distributed Ledger Technologies (DLTs) which provide several security guarantees on data through cryptographic hashing techniques. In this work, we present a three-fold contribution: (i) we discuss the results of a Systematic Literature Review (SLR) on the state-of-the-art of the research related to the integration between DLT and Digital Twins, aimed at clarifying relevant aspects such as, among others, what is the favourite DLT choice in existing proposals or what is the type of DT-related information to store on-chain; (ii) leveraging the SLR results and other related research, we propose an architectural framework for the integration of DT and DLTs (more specifically, blockchains); (iii) we validate the proposed architecture by means of two proof-of-concept implementations leveraging different technological stacks and taking into account two different operational scenarios. Finally, we conduct a requirements coverage analysis and compare the PoCs through a coverage matrix.

Mixed-Trust Computing: Safe and Secure Real-Time Systems

Verifying complex Cyber-Physical Systems (CPS) is increasingly important given the push to deploy safety-critical autonomous features. Unfortunately, traditional verification methods do not scale to the complexity of these systems and do not provide systematic methods to protect verified properties when not all the components can be verified. To address these challenges, this article proposes a real-time mixed-trust computing framework that combines verification and protection. The framework introduces a new task model, where an application task can have both an untrusted and a trusted part. The untrusted part allows complex computations supported by a full OS with a real-time scheduler running in a VM hosted by a trusted hypervisor. The trusted part is executed by another scheduler within the hypervisor and is thus protected from the untrusted part. If the untrusted part fails to finish by a specific time, the trusted part is activated to preserve safety (e.g., prevent a crash) including its timing guarantees. This framework is the first allowing the use of untrusted components for CPS critical functions while preserving logical and timing guarantees, even in the presence of malicious attackers. We present the framework its schedulability analysis and the coordination protocol between the trusted and untrusted parts. Our implementation on a Raspberry Pi 3 is also discussed along with experiments showing the behavior of the system under failures of untrusted components, and a drone application to demonstrate its practicality.

Optimal control of differentially flat systems is surprisingly easy

As we move to increasingly complex cyber–physical systems (CPS), new approaches are needed to plan efficient state trajectories in real-time. In this paper, we propose an approach to significantly reduce the complexity of solving optimal control problems for a class of CPS with nonlinear dynamics. We exploit the property of differential flatness to simplify the Euler–Lagrange equations that arise during optimization, and this simplification eliminates the numerical instabilities that plague optimal control in general. We also present an explicit differential equation that describes the evolution of the optimal state trajectory, and we extend our results to consider both the unconstrained and constrained cases. Furthermore, we demonstrate the performance of our approach by generating the optimal trajectory for a planar manipulator with two revolute joints. We show in simulation that our approach is able to generate the constrained optimal trajectory in 4.5ms while respecting workspace constraints and switching between a ‘left’ and ‘right’ bend in the elbow joint.

Validity Frame–enabled model-based engineering processes

Model-based systems engineering (MBSE) focuses on using models to support the design, optimization, simulation, and ultimately deployment of complex cyber-physical systems (CPSs). These models enable reasoning about and predicting the behavior of the (realized) real-world system in silico. The value of using such (predictive) model depends on its validity against its real-world counterpart. As such, the validity context of a model is critical to ensure correct model use. Reasoning on validity is only possible if the validity of the model was captured explicitly at design time. In previous work, the validity frame (VF) was presented as a way to explicitly capture a model’s validity; however, no guidance on the integration process within MBSE processes was given. Within this article, we present the creation and evolution of the model and its VFs to ensure model validity consistency and completeness. This evolution results in a set of interrelated models and VFs. By capturing these relations, we create a lightweight frame-enabled library of model variants. We show our contribution using an F1/10 vehicle simulation test bench.

Systematic Reliability Optimization (ASRO)

Reliability optimization is a critical aspect of modern engineering systems and products, particularly with the growing complexity and interconnectedness of systems. This paper delves into the significance of reliability optimization and the techniques employed to achieve it. It highlights the benefits of optimizing reliability, including reduced costs, enhanced customer retention, and a competitive advantage. The paper discusses the challenges of balancing performance, cost, and reliability, especially in real-world systems with intricate nonlinear interactions between subcomponents. It introduces various reliability optimization techniques, including redundancy analysis, physics-based models, accelerated testing, and data-driven methods. The paper emphasizes the potential of advanced sensing and AI-based methods for reliability optimization. It highlights the importance of AI in optimizing the design and management of complex cyber-physical systems (CPS), where failures can have severe economic and safety consequences. The paper also discusses the empirical review of over 50 studies since 2016, which provides insights into the effectiveness of various optimization approaches across industry verticals. In conclusion, reliability optimization is crucial for the development and operation of modern engineering systems and products. Advanced sensing and AI-based methods offer promising solutions for optimizing reliability in complex systems, particularly CPS. By systematically optimizing reliability, companies can reap significant benefits and ensure the successful operation of their products and services.

Energy-Aware Real-Time Scheduling of Multiple Periodic DAGs on Heterogeneous Systems

Many of today’s complex Cyber-Physical Systems (CPSs) are represented as a set of independent co-executing real-time control applications, where each such application is represented as a precedence-constrained task graph. The applications execute in infinite loops, periodically acquiring data from the environment through sensors at a particular frequency, processing the same, and then producing processed data via actuators. These CPSs often execute under stringent resource constraints (such as limited energy budgets) in distributed networked environments and may be heterogeneous to be able to satisfactorily meet stipulated performance specifications. This work presents a list-based energy-aware scheduler called DPMRS for a set of real-time control applications co-executing in a heterogeneous distributed environment. DPMRS introduces a novel approach for the integrated behavioural representation of a set of co-executing real-time DAG-structured applications. Each task in this integrated representation is then scheduled by determining its relative execution start time on a particular processor, which operates at an appropriately chosen frequency when the task runs on this processor. The overall objective of DPMRS is to minimize aggregate energy consumed in the execution of all tasks. The efficacy of the proposed scheduler has been exhibited through extensive simulation experiments using benchmark task graphs from different application domains. Additionally, a case study on automotive control systems has been included to show the applicability of the proposed work in real world settings.

Flexible Smart Energy-Management Systems Using an Online Tendering Process Framework for Microgrids

Currently, modern power grids are evolving into complex cyber-physical systems integrated with distributed energy resources that can be controlled and monitored by computer-based algorithms. Given the increasing prevalence of artificial intelligence algorithms, it is essential to explore the possibility of energy management in microgrids by implementing control methodologies with advanced processing centers. This study proposes a novel smart multi-agent-based framework under a tendering process framework with a bottom-up approach to control and manage the flow of energy into a grid-connected microgrid (MG). The tendering organization in this structure as an upstream agent allocates demand among generators, creates a balance between supply and demand, and provides optimal energy cost for the MG. To optimize the electricity cost and decrease the use of grid power, the first-price sealed-bid (FPSB) algorithm is implemented over the tendering process. The proposed approach from one side optimally allocates energy among generators, and, from the other side, guarantees the system from blackouts. Theoretical analysis and results demonstrate that the proposed technique is easy to implement and provides a robust and stable control for MGs, which can guarantee energy management as well as flexible and online control. Furthermore, results show the proposed framework besides the real-time allocation of power among providers to optimize the injected power from the grid so that the total injected power by the grid is 146.92 kWh and the injected power to the grid is 214.34 kWh.

Review on the key technologies of power grid cyber‐physical systems simulation

AbstractA Cyber‐Physical System (CPS) is a spatiotemporal multi‐dimensional and heterogeneous hybrid autonomous system composed of deep integration of information resources and physical systems. With the development of digitisation and digitalisation, a large number of data acquisition equipment, computing equipment, and electrical equipment are interconnected between the power grid and the information communication network. The power grid has thus been restructured as a mature and highly complex CPS. In order to promote the development of power grid CPS technologies and provide a reference for relevant researchers in the field, the origin and concept of CPS and features in power grid CPS are introduced firstly. Then the key technologies of power grid CPS simulation are discussed and further analysed from three perspectives, including modelling theory, simulation methods, and system‐level simulation. On this basis, the application of CPS simulation technology in future power grids has been prospected.

Where digital meets physical innovation: Reverse salients and the unrealized dreams of3Dprinting

AbstractFor more than three decades, enthusiasts have predicted that direct manufacturing enabled by 3D printing would inevitably supplant traditional manufacturing methods. Alas, for nearly as long, these utopian predictions have failed to materialize. One reason is a flawed assumption that hybrid digital‐physical systems such as 3D printing would advance as rapidly as purely digital innovations enabled by Moore's law. Instead, like other examples of cyber‐physical systems (CPSs), technological progress in 3D printing faces inherent limitations that are emblematic of the differences between CPSs and purely digital innovations. As with any complex CPS, improved performance of a 3D printing system has been limited by that of its key components—the sort of limiting problem previously defined as a reverse salient. Unlike previously studied technologies, several reverse salients for 3D printing performance have neither resolved nor signs of resolving soon. Here we analyze these key reverse salients, and show how they have hampered the suitability of 3D printing for direct manufacturing and other predicted applications. We contrast predicted versus actual capabilities for 3D printing‐enabled transformation in six key areas: product innovation, mass customization, home fabrication, distributed manufacturing, supply chain optimization and business model innovation. From this, we suggest opportunities for greater realism in future 3D printing research, as well as broader implications for our understanding of CPSs and reverse salients.

Integrating Continuous and Batch Processes with Shared Resources in Closed-Loop Scheduling: A Case Study on Tuna Cannery.

Scheduling tasks in production facilities are usually hybrid optimization problems of a large combinatorial nature. They involve solving, in near-real time, the integration of the operation of several batch units of continuous dynamics with the discrete manufacture of items in processing lines. Moreover, one has to deal with uncertainty (process delays, unexpected stops) and the management of shared resources (energy, water, etc.) including decisions made by plant operators: still, some tasks in the scheduling layers are done manually. Manufacturing Execution Systems (MESs) are intended to support plant personnel at this level. However, there is still much work to do in terms of performing automatic scheduling, computed in real time, that guides managers to achieve an optimal operation of such complex cyber-physical systems. This work proposes a closed-loop approach to handle the uncertainty arising when facing the online scheduling of supply lines and parallel batch units. These units often share some resources, so effects due to concurrent resource consumption on the system dynamics are explicitly considered in the presented formulation. The proposed decision support system is tested onsite in a tuna cannery, to handle short-term online scheduling of sterilization processes that deal with limited steam, carts, and operators as shared resources.

Scheduling Complex Cyber-Physical Systems with Mixed-Criticality Components

Two emerging trends for designing a complex, cyber-physical systems are the component-based and mixed-criticality (MC) approaches. A component-based approach independently develops individual components and subsequently integrates them to reduce system complexity. This approach provides strong isolation among components but incurs resource inefficiency. Alternatively, an MC approach integrates components of different criticality with different levels of guarantee for resource efficiency, while components are not isolated. To leverage MC and component-based approaches, we investigate how to balance component isolation and resource efficiency under component-based MC systems. We introduce the concept of component-MC schedulability, where isolated tasks are protected from external events outside the component, and shared tasks may be suspended for the critical events of other components. Under component-MC schedulability, we propose a component-based mixed-criticality scheduling framework with dynamic resource allocation (CMC-DRA), which suspends low-criticality tasks differently depending on internal or external component behavior. We also develop scheduling semantics and analyze the schedulability for CMC-DRA. Through simulation on synthetic workloads, we demonstrate that CMC-DRA has up to 88.3% higher schedulability than existing approaches and reduces the deadline miss ratio by up to 47.7%.

Robotic-biological systems for detection and identification of explosive ordnance: concept, general structure, and models

The subject of this study is systems for detection and identification (D&I) of explosive ordnance (EO). The aim of this study is to develop a concept, general structure, and models of a robotic-biological system for D&I of EO (RBS-D&I). The objectives are as follows: 1) to classify mobile systems for D&I of EO and suggest a concept of RBS-D&I; 2) to develop the general structure of RBS-D&I consisting of robotic (flying and ground) and biological subsystems; 3) to develop models of RBS-D&I including automaton, hierarchical, and operational ones; 4) to describe tasks and planned results of the article-related scientific project; and 5) to discuss research results. The following results were obtained. 1) The general structure of the RBS-D&I. The structure comprises the following levels: control and processing centres (mobile ground control and processing centre (MGCPC) and virtual control and processing centre); forces for detection and identification (fleet of unmanned aerial vehicles (FoU), biological detection information subsystem (BDIS), and robotic detection information subsystem (RDIS)); interference; natural covers and a bedding surface; and target objects (all munitions containing explosives, nuclear fission or fusion materials and biological and chemical agents). 2) A concept of RBS-D&I. The concept is based on RBS-D&I description, analysis, development, and operation as an integrated complex cyber-physical and cyber-biological system running in changing physical and information environments. 3) The RBS-D&I automata model. The model describes RBS-D&I operating in two modes. In mode 1, FoU and BDIS operate separately and interact through the MGCPC only. In mode 2, depending on the specifics of the tasks performed, FoU and RDIS can directly interact among themselves or through the MGCPC. 4) hierarchical model. The model has two sets of vertices: EO detection and platforms equipped with the necessary sensors. 5) An operational cycle model. The model describes land release operations via a methodology of functional modeling and graphic description of IDEF0 processes. Conclusions. The proposed concept and RBS-D&I solutions can provide high-performance and guaranteed EO detection in designated areas by the implementation of an intelligent platform and tools for planning the use of multifunctional fleets of UAVs and other RBS-D&I subsystems.

Modelling and Analysis of a Sigfox-Based IoT Network Using uppaalsmc

Wireless sensor nodes are usually powered by batteries that have limited energy capacity. In many applications, the nodes are installed in inaccessible locations where they are problematic to replace or recharge. Therefore, energy optimisation is crucial for increasing the node’s lifetime. This study presents a method for the analysis and prediction of the energy consumption of Sigfox-based Wireless Sensor Nodes. The method is illustrated in a use case where the nodes monitor the water level in drainage lines in cities to improve surface and wastewater management. We propose a formal model-based technique using the UPPAAL Statistical model checker tool to model and analyse the node’s lifetime. Statistical model checking provides a highly scalable technique for performance analysis of complex cyber-physical systems. The model captures the energy-related behaviour of the node, the Sigfox radio specification, and the sensor, each parameterised with values from the device’s datasheets. Furthermore, we calibrate the model using measurements obtained from real-world hardware. Finally, we evaluate a collection of strategies to optimise the battery lifetime of the node. We simulate the model with a 10,000 mAh battery, and the results indicate that we can extend the node’s lifetime from 202 days to 2.71 years using our most optimised transmission strategy.

HVA_CPS proposal: a process for hazardous vulnerability analysis in distributed cyber-physical systems

Society is increasingly dependent upon the use of distributed cyber-physical systems (CPSs), such as energy networks, chemical processing plants and transport systems. Such CPSs typically have multiple layers of protection to prevent harm to people or the CPS. However, if both the control and protection systems are vulnerable to cyber-attacks, an attack may cause CPS damage or breaches of safety. Such weaknesses in the combined control and protection system are described here as hazardous vulnerabilities (HVs). Providing assurance that a complex CPS has no HVs requires a rigorous process that first identifies potential hazard scenarios and then searches for possible ways that a cyber-attacker could cause them. This article identifies the attributes that a rigorous hazardous vulnerability analysis (HVA) process would require and compares them against related works. None fully meet the requirements for rigour. A solution is proposed, HVA_CPS, which does have the required attributes. HVA_CPS applies a novel combination of two existing analysis techniques: control signal analysis and attack path analysis. The former identifies control actions that lead to hazards, known as hazardous control actions (HCAs); the latter models the system and searches the model for sequences of attack steps that can cause the HCAs. Both analysis techniques have previously been applied alone on different CPSs. The two techniques are integrated by extending the formalism for attack path analysis to capture HCAs. This converts the automated search for attack paths to a selected asset into an exhaustive search for HVs. The integration of the two techniques has been applied using HCAs from an actual CPS. To preserve confidentiality, the application of HVA_CPS is described on a notional electricity generator and its connection to the grid. The value of HVA_CPS is that it delivers rigorous analysis of HVs at system design stage, enabling assurance of their absence throughout the remaining system lifecycle.

FlexiChain 3.0: Distributed Ledger Technology-Based Intelligent Transportation for Vehicular Digital Asset Exchange in Smart Cities.

Due to the enormous amounts of data being generated between users, Intelligent Transportation Systems (ITS) are complex Cyber-Physical Systems that necessitate a reliable and safe infrastructure. Internet of Vehicles (IoV) is the term that describes the interconnection for every single node, device, sensor, and actuator that are Internet enabled, whether attached or unattached to vehicles. A single smart vehicle will generate a huge amount of data. Concurrently, it needs an instant response to avoid accidents since vehicles are fast-moving objects. In this work, we explore Distributed Ledger Technology (DLT) and collect data about consensus algorithms and their applicability to be used in the IoV as the backbone of ITS. Multiple distributed ledger networks are currently in operation. Some are used in finance or supply chains, and others are used for general decentralized applications. Despite the secure and decentralized nature of the blockchain, each of these networks has trade-offs and compromises. Based on the analysis of consensus algorithms, a conclusion has been made to design one that fits the requirements of ITS-IOV. FlexiChain 3.0 is proposed in this work to serve as a Layer0 network for different stakeholders in the IoV. A time analysis has been conducted and shows a capacity of 2.3 transactions per second, which is an acceptable speed to be used in IoV. Moreover, a security analysis was conducted as well and shows high security and high independence of the node number in terms of security level per the number of participants.