Cyber-physical Systems Applications Research Articles

SmAuto: A domain-specific-language for application development in smart environments

A common problem in the development of Internet-of-Things (IoT) and Cyber-Physical System (CPS) applications is the complexity of these domains, due to their hybrid and distributed nature in multiple layers (hardware, network, communication, application etc.). Apart from other issues, this inherent complexity often gives room for implementation errors, which can be in many cases fatal and drive the application and/or the system to undesired states. The current work aspires to alleviate this problem by introducing a low-code approach for building IoT and CPS applications. We argue that, through the proposed approach it is possible to lower development time and risk (errors/bug-related ones) and allow a wide range of end-users to build and monitor applications for state-of-the-art domains, such as smart home and smart industry. In this context, Model-Driven Engineering (MDE) approaches are explored and SmAuto, a Domain-specific Language (DSL) is proposed for creating and executing automation tasks for smart environments. Through SmAuto it is possible to handle the heterogeneity and complexity issues of the IoT and CPS domains, this way allowing end-users are non-technical application experts to build well-designed and properly functioning smart applications. The proposed DSL implements a Sense-Think-Act-Communicate model for smart environments and enables the creation, validation, and dynamic execution of composite automation models in physical, virtual and hybrid environments, while it also enables automated code generation of virtual entities for verification purposes. By using layered abstractions to automate the development process, end-users can concentrate on the real problem instead of dwelling into technical details, thus increasing their productivity. The results of the empirical evaluation and the comparison to existing approaches show that SmAuto can make application development more rigorous, improves productivity of end-users including non-experts, i.e. citizen developers and satisfies several functional and non-functional requirements of modern DSLs, such as tool support, modular deployment, reusability, availability and extensibility.

Cybersecurity vulnerability and resilience of cooperative driving automation for energy efficiency and flow stability in smart cities

Increasing levels of communication and automation has led to the development of cyber physical systems applications known as cooperative driving automation. While such applications provide enormous benefits in improving transportation systems operation and environmental sustainability, they also introduce new risks to smart cities. These systems provide wider access to adversaries and increase the attack surface to compromise the operation of smart cities. This study develops a vehicle to infrastructure (V2I) based cooperative driving automation platform to understand and analyze the cyber risks in these systems from a V2I perspective in a representative traffic environment. Three cyberattacks (Fake BSMs, replay and denial of service) were emulated as a cooperative game to assess the impact on volatility, energy efficiency and traffic flow stability. The critical nature of cooperative driving automation is revealed by this research and flow stability, and energy efficiency of cooperative driving automation are severely impacted by the three categories of cyberattacks. Further, a 35 %-40 % increase in volatility in acceleration-deceleration regimes along with 20 % increase in emission is observed as opposed to the baseline case with no cyberattacks. The proposed long-short term memory neural networks (LSTM) showed an average detection accuracy of around 98 % for the analyzed cyberattacks. The research findings would foster the development of algorithms to continuously monitor the state of cooperative automated driving environment for anomalous behavior and promote resilient and environmentally sustainable cities in the future.

A model-based approach for self-adaptive security in CPS: Application to smart grids

Security risk assessment is an important challenge in the design of Cyber Physical Systems (CPS). Even more importantly, the intrinsically dynamical nature of these systems, due to changes in their environment, as well as evolutions in their infrastructures, makes them self-adaptive systems, where security aspects have to be considered in terms of management of detections and reactions for self-protection. In this work, we propose an approach to autonomously mitigate the threats in each reconfiguration at application or infrastructure levels of CPS. We propose and implement a framework for self-adaptive security: software architecture, design method, and integration with model-based decision. We use Attack-Defense Trees for modeling threats, and our approach involves security risk assessment, taking into account its balancing and coordination with quality-of-service aspects. We formulate and formalize the on-line decision problem to be solved at each cycle of the self-adaptation control loop in terms of Constraint Programming (CP) modeling and resolution. The CP model implements a set of constraints that allow to specify secure configurations, evaluated regarding their impact on system performance to pinpoint the most relevant one portraying a good balance between the security and quality of service. We perform validation of our approach with its application to Smart Grids, more particularly to an industrial case study from RTE (the French Energy Transmission company).

A type system for formal verification of cyber-physical systems C/C++ software

The subject: This study focuses on improving the quality of Cyber-Physical System (CPS) software by eliminating incorrect usage of units of measurement and orientation in C/C++ programs. Incorrect usage often leads to critical errors that conventional systems cannot effectively prevent. Manual examination of code using dimensional and orientation analysis can detect these errors in physical equations, but these methods become impractical when dealing with complex physical computations. Objectives: As suggested by Siano, the proposed approach uses physical quantities and prefixes defined by the International System of Units and orientation operations on physical objects. The elaborated system incorporates dimensional and orientation analysis and metaprogramming techniques. The methods used are dimensional & orientational analysis and metaprogramming. The following results were obtained: ensuring consistency of the units, incorporating orientation operations into the programming model for accurately handling physical object rotations and alignments, and using Siano’s work to precisely manipulate object orientation, thereby reducing the likelihood of orientation-related errors. Checking physical dimensions and orientations during the compilation stage identifies potential software defects before code execution, thereby reducing debugging time and lowering the cost of addressing issues later in development. The elaborated system represents a crucial step towards safer and more dependable Cyber-Physical System applications. This approach allows us to identify approximately 90% of incorrect usage of program variables; additionally, it detects over 50% of erroneous operations during compilation and execution of large-scale programs in real-world conditions. Conclusions. Scientific novelty: it proposed and developed a specialized C++-type library for formal compile-time software verification of Cyber-Physical Systems software. The proposed C++-type library leverages dimensional and orientational analysis to enhance software quality, reliability, and real-time formal verification. Although the proposed method for formal verification is not tailor-made for cyber-physical objects and systems, given its primary focus on software-level concerns, it does exhibit adaptability for verifying general-purpose software that incorporates various physical parameters. This versatility extends to diverse domains such as educational, gaming, and simulation software.

Mechanism for Efficient Media Propagation in Event-Driven Cyber-Physical Systems

Many key applications in Cyber-Physical Systems require the transmission of speech, audio, or video. These scenarios involve the use of traditional Real-Time Communication (RTC) protocols and technologies, which cannot always be used in the context of core networks. This is particularly critical in the context of Event-Driven Architectures (EDAs), where RTC protocols require the use of complex topologies that rely on costly infrastructure. One way to avoid this is by encapsulating all media traffic in EDA protocols. However, this approach does not come without challenges. Specifically, the nature of the transport protocols causes the media to be heavily affected by application layer impairments that render their usage highly impractical. To prevent this from happening, this paper introduces a unified scheme that supports the efficient encapsulation of media traffic in EDA scenarios. This is accomplished through a mechanism that relies on a Machine Learning (ML) model that is exercised in an experimental framework.

Neurotechnologies Applied to Society's Perception of Cyber-Physical Systems (CPS) in Smart Cities

This study aims to quantify the perception of value and acceptance by citizens of the use of cyber-physical systems (CPS) in transportation systems and smart cities using neurotechnologies. The work has been developed in the main cities of the following Latin American countries: Spain, Ecuador, Colombia, and Argentina. Targeting urban, public transport-using graduates, it assesses CPS in smart cities and user experiences. Triangulating qualitative research and neurotechnology, the study extends the taxonomy of emotional domains. The results indicate that users do not always assign equivalent importance to what they truly feel, and it is noteworthy that the most important factor, both quantitatively and emotionally, is the application of CPS to improve efficiency in public transportation. The implications of these analyses are discussed in the final part of the article with the aim of providing recommendations to policymakers on the key aspects to be considered in the design and development of CPS for use in smart cities.

Depend Ability Analysis on CPS Using Machine Learning Techniques

A Cyber-Physical System (CPS) is an entity that effortlessly monitors and controls physical operations by integrating computational and physical elements. Dependability on the CPS application program and also proposes a real-time analysis approach to CPS application dependability based upon Artificial Intelligence and Machine Learning (ML). For starters, pick complicated networks to determine tips within the system topology on the CPS application process. Unsupervised mastering category by a quick density clustering algorithm to classify the value of nodes could be successfully put on the crucial analysis of nodes in CPS application program as well as help support the setting up of CPS software phone Secondly, a real-time CPS dependability automated internet analysis technique is suggested. Unreliable methods are able to imply big losses, each monetarily in addition to within man's life. On a good mention, CPS has information like the main component of the operation of theirs. The prevalence and availability of information demonstrate a brand-new chance to change the methods within what dependability evaluation continues to be usually performed. This process utilizes printer mastering tips to create an analysis framework, design, and style an internet queuing algorithm, as well as put into action real-time internet evaluation and analysis of CPS dependability. Preventive steps make sure that the device works ordinarily as well as with no interruption, which significantly betters method dependability. Last but not least, simulation final results verify the usefulness of the analysis technique and the broad application prospects of its.

Provably secure and lightweight three-factor authentication scheme for industrial medical CPS

Cyber-Physical System (CPS) is a multidimensional complex system that integrates computing, network, and physical environment, which is widely used to promote the upgrading of industrial production and technology. Recently, the application of industrial CPS in the medical field has attracted the attention of scholars and medical experts. Medical CPS can establish a perfect medical network to help doctors monitor patients’ conditions in real-time and make treatments. However, how to design a provably secure and lightweight authentication protocol for industrial medical CPS is a challenge. Very recently, Qi et al. proposed an authentication protocol for industrial medical CPS based on the chaotic map, the Artificial Intelligence (AI) biometric technique is used in the protocol to resist password guessing attack and smart card lost attack. However, we find that their protocol is still vulnerable to identity guessing attack, user impersonation attack, trace attack, desynchronization attack, and has no perfect forward secrecy. Therefore, we propose a security-enhanced and lightweight authentication protocol for industrial medical CPS. In the protocol, a dynamic temporary identity strategy is designed to protect anonymity and privacy, which enables the updating of temporary identities while resisting desynchronization attacks. The protocol is proved secure through formal security proof in random oracle model. Meanwhile, compared with the related protocols, our protocol is superior in security and cost to meet the lightweight requirements in medical scenarios.

Research on Secure State Estimation and Recovery Control for CPS under Stealthy Attacks

As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation, or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

Leveraging a novel NFT-enabled blockchain architecture for the authentication of IoT assets in smart cities

The concept of smart city architecture requires a comprehensive solution that can combine real-time response applications for cyber-physical systems. However, the architecture faces challenges that can obstruct the operations in terms of systems, processes, and data flow as far as the breach risk is concerned. Though the field has been researched with the existence of centralized and distributed architectures to support smart cities. Research gaps regarding security concerns, platform assistance, and resource management continue to persist. This research article presents a novel blockchain-based architecture that proposes expansion in the non-fungible tokens (NFTs) to cater to the expansion of IoT-enabled smart assets. It enables NFTs to employ fog computing for all users and smart devices connected to a fog node in a cyber-physical system. The proposed expansion suggested in Non-Fungible Tokens (NFTs) for IoT assets representation in a cyber-physical system, provides devices and user identification and authentication functionality. The proposed NFT architecture has been designed to provide a smart city solution for cyber-physical systems that ensures robust security features (such as CIA) by introducing new attributes and functions for Owner, User, Fog, and IoT device/s authentication. The validation and rigor of the security services, efficiency, and latency have been achieved by deployments on private and public ledgers. The efficiency, and cost-effectiveness of the suggested functions and components have been evaluated in terms of evaluation cost and time complexity which resulted in promising results, obtained and validated on a testnet. The evaluation cost for the devised mint component was approximately 81%, and devised approve() was approximately 23% more efficient than other solutions.

Evaluating virtualization for fog monitoring of real-time applications in mixed-criticality systems

Technological advances in embedded systems and the advent of fog computing led to improved quality of service of applications of cyber-physical systems. In fact, the deployment of such applications on powerful and heterogeneous embedded systems, such as multiprocessors system-on-chips (MPSoCs), allows them to meet latency requirements and real-time operation. Highly relevant to the industry and our reference case-study, the challenging field of nuclear fusion deploys the aforementioned applications, involving high-frequency control with hard real-time and safety constraints. The use of fog computing and MPSoCs is promising to achieve safety, low latency, and timeliness of such control. Indeed, on one hand, applications designed according to fog computing distribute computation across hierarchically organized and geographically distributed edge devices, enabling timely anomaly detection during high-frequency sampling of time series, and, on the other hand, MPSoCs allow leveraging fog computing and integrating monitoring by deploying tasks on a flexible platform suited for mixed-criticality software, leading to so-called mixed criticality systems (MCSs). However, the integration of such software on the same MPSoC opens challenges related to predictability and reliability guarantees, as tasks interfering with each other when accessing the same shared MPSoC resources may introduce non-deterministic latency, possibly leading to failures on account of deadline overruns. Addressing the design, deployment, and evaluation of MCSs on MPSoCs, we propose a model-based system development process that facilitates the integration of real-time and monitoring software on the same platform by means of a formal notation for modeling the design and deployment of MPSoCs. The proposed notation allows developers to leverage embedded hypervisors for monitoring real-time applications and guaranteeing predictability by isolation of hardware resources. Providing evidence of the feasibility of our system development process and evaluating the industry-relevant class of nuclear fusion applications, we experiment with a safety-critical case-study in the context of the ITER nuclear fusion reactor. Our experimentation involves the design and evaluation of several prototypes deployed as MCSs on a virtualized MPSoC, showing that deployment choices linked to the monitor placement and virtualization configurations (e.g., resource allocation, partitioning, and scheduling policies) can significantly impact the predictability of MCSs in terms of Worst-Case Execution Times and other related metrics.

Improving Causal Learning Scalability and Performance using Aggregates and Interventions

Smart homes are Cyber-Physical Systems (CPS) where multiple devices and controllers cooperate to achieve high-level goals. Causal knowledge on relations between system entities is essential for enabling system self-adaption to dynamic changes. As house configurations are diverse, this knowledge is difficult to obtain. In previous work, we proposed to generate Causal Bayesian Networks (CBN) as follows. Starting with considering all possible relations, we progressively discarded non-correlated variables. Next, we identified causal relations from the remaining correlations by employing “ do-operations .” The obtained CBN could then be employed for causal inference. The main challenges of this approach included “non-doable variables” and limited scalability. To address these issues, we propose three extensions: (i) early pruning weakly correlated relations to reduce the number of required do-operations, (ii) introducing aggregate variables that summarize relations between weakly coupled sub-systems, and (iii) applying the method a second time to perform indirect do interventions and handle non-doable relations. We illustrate and evaluate the efficiency of these contributions via examples from the smart home and power grid domain. Our proposal leads to a decrease in the number of operations required to learn the CBN and in an increased accuracy of the learned CBN, paving the way toward applications in large CPS.

A Detection and Response Architecture for Stealthy Attacks on Cyber-Physical Systems

There has been an increased reliance on interconnected Cyber-Physical Systems (CPS) applications. This reliance has caused tremendous growth in high assurance challenges. Due to the functional interdependence between the internal systems of CPS applications, the utilities' ability to reliably provide services could be disrupted if security threats are not addressed. To address this challenge, we propose a multi-level, multi-agent detection and response architecture built on the formalisms of Hidden Markov Models (HMM) and Markov Decision Processes (MDP). We have evaluated the performance of the proposed architecture on one of the critical smart grid applications, Advanced Metering Infrastructure (AMI). This paper utilizes a simulation tool called SecAMI for performance evaluation. A Stealthy attack scenario contains multiple distinct multi-stage attacks deployed concurrently in a network to compromise the system and stop several critical services in a CPS. The results show that the proposed architecture effectively detects and responds to stealthy attack scenarios against Cyber-Physical Systems. In particular, the simulation results show that the proposed system can preserve the availability of more than 93% of the AMI network under stealthy attacks. A future study may evaluate the effectiveness of various stealthy attack strategies and detection and response systems. The high availability of any AMI should be protected against new attack techniques. The proposed system will also determine a distributed IDS's efficient placement for intrusion detection sensors and response nodes within an AMI.

A Novel Data Management Scheme in Cloud for Micromachines

In cyber-physical systems (CPS), micromachines are typically deployed across a wide range of applications, including smart industry, smart healthcare, and smart cities. Providing on-premises resources for the storage and processing of huge data collected by such CPS applications is crucial. The cloud provides scalable storage and computation resources, typically through a cluster of virtual machines (VMs) with big data tools such as Hadoop MapReduce. In such a distributed environment, job latency and makespan are highly affected by excessive non-local executions due to various heterogeneities (hardware, VM, performance, and workload level). Existing approaches handle one or more of these heterogeneities; however, they do not account for the varying performance of storage disks. In this paper, we propose a prediction-based method for placing data blocks in virtual clusters to minimize the number of non-local executions. This is accomplished by applying a linear regression algorithm to determine the performance of disk storage on each physical machine hosting a virtual cluster. This allows us to place data blocks and execute map tasks where the data blocks are located. Furthermore, map tasks are scheduled based on VM performance to reduce job latency and makespan. We simulated our ideas and compared them with the existing schedulers in the Hadoop framework. The results show that the proposed method improves MapReduce performance in terms of job latency and makespan by minimizing non-local executions compared to other methods taken for evaluation.

Optimizing intrusion detection in industrial cyber-physical systems through transfer learning approaches

Applications of Cyber-Physical Systems (CPSs) greatly influenceseveral industrial sectors. Treating security-related concerns with utmost seriousness is necessary for the CPS to work correctly. Although CPS supervises the manufacturing process, the type and volume of cyberattacks that try to obtain data from CPS are significantly increasing. Since attacks on CPS can disrupt production, cause financial losses, and endanger national security, they must be prevented and detected. The general operation of the physical process can nevertheless be affected, and system failure is caused by specific traditional measures designed to anticipate CPS cyber-attacks. Also, as the system appears to be extremely complicated and no pertinent information about the item under investigation is available, the productive prediction of cyber-attacks in CPS remains a complex problem. This work will handle these issues using the proposed framework using Transfer Learning with the VGG16 model. The proposed TL-VGG16 achieves 96% accuracy, higher than existing CPS intrusion detection techniques.

Increasing the construction production efficiency based on the use of cyber-physical systems and technologies

Objective: The article proposes a classification of cyber-physical systems and technologies at the stages of the life cycle of a construction project, considers an algorithm for choosing these technologies depending on the effectiveness of the project.   Theoretical framework: The study is based on the review of publications in international scientific journals, methodological literature on the topic of the use of digital tools in construction. The experience of different countries in the application of cyber-physical systems and technologies in construction was represented.   Method: The study was conducted based on materials published in open sources, including scientific articles, reports, regulatory documents. In addition, the study used the experience of several companies operating in the construction industry.   Results and conclusion: The main approaches to planning construction projects at the stage of the life cycle are studied. Approaches to the formation of a graphical hierarchical block diagram of a simulation model, consisting of different levels of nesting, are proposed. The results may be of interest to manufacturing companies that decide to implement digital technologies in construction, as well as individual scientists who study the problem of increasing the efficiency of production organization at the stages of the construction projects life cycles. Implications of the research: The results of the study can be used by construction companies when planning the choice of cyber-physical systems and technologies at the stages of the life cycle of construction objects   Originality/value: The authors propose an approach that allows choosing tools for designing and building objects using cyber-physical systems and technologies at the stages of the life cycle. A hierarchical block diagram of the simulation model is proposed, which contains more than a dozen child diagrams that combine several hundred smaller typical modules.

Birds of the Same Feather Flock Together: A Dual-Mode Circuit Candidate for Strong PUF-TRNG Functionalities

Physically Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) are two highly useful hardware primitives to build up the root-of-trust for embedded devices in Internet-of-Things and Cyber-Physical System applications. These applications demand the primitives be lightweight, yet flexible. However, PUFs are designed to offer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">repetitive and instance-specific</i> randomness, whereas TRNGs are expected to be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">invariably</i> random. A challenging but thought-provoking problem from a hardware designer's perspective would be to design a circuit that serves the purpose of both PUF and TRNG depending on the exact requirement of the application. Here, we present a dual-mode PUF-TRNG design that utilises two different hardware-intrinsic properties, i.e. oscillatory metastability of Transition Effect Ring Oscillator (TERO) cell and propagation delay of a buffer within the cell to achieve this goal. A 48.62% reduction in area is accomplished due to the integration in comparison to separate instances of standalone PUFs/ TRNG designs, built from Programmable Delay Line (PDL) based Arbiter PUFs (APUFs) and TERO-TRNG. Our final design has a hardware footprint of 618 Look-Up Tables (LUTs) and 447 Flip-Flops (FFs). Furthermore, experimental analysis of the state-of-the-art modelling attacks, reliability attacks on the proposed PUF design shows a prediction accuracy of 55.37% and 50.14% respectively for 5.2M Challenge Response Pairs (CRPs). Additionally, the TRNG passes evaluation through National Institute of Standards and Technology (NIST) Special Publication (SP) 800-22 and German Federal Office for Information Security (BSI) Application Notes and Interpretation of the Scheme (AIS)-31 tests.

A Low-Code Approach for Connected Robots

Advanced robotic systems are finally becoming a reality; following the increased attention that robotics have attracted during the last few decades, new types of robotic applications are launched, from robotic space vessels and fully autonomous cars to robotic dancers and robot companions. Even more, following the advancements in the Internet of Things (IoT) domain, robots can now participate in more complex systems, namely Cyber-physical systems (CPS). In such systems, robots, software, sensors and/or “things” cooperate seamlessly in order to exhibit the desired outcome. However, the high heterogeneity of the components comprising CPS systems requires expertise in various scientific domains, a fact that makes development of CPS applications a resource- and time-consuming process. In order to alleviate this pain, model-driven (or model-based) approaches have been introduced. They employ a low code software engineering approach and hide the domain-specific knowledge needed, by providing an abstract representation that can be more easily understood. Following the low-code paradigm, current work focuses on the development of Domain-specific Languages (DSL) for ROS2 (Robot Operating System 2) systems in order to hide low-level middleware-specific setup and configuration details and enable access to robot development by non ROS experts. Furthermore, in order to enable the integration of ROS2 robots in CPS, a second DSL was developed. The first language, GeneROS, is used for the development and configuration of the core functionalities of the robot (such as hardware drivers and algorithms), while the second language, ROSbridge-DSL, implements the interfaces for connecting robots to the Edge and the Cloud, enabling this way remote monitoring and control in the context of IoT and CPS.

Assistance from the Ambient Intelligence: Cyber–physical​ system applications in smart buildings for cognitively declined occupants

Caregivers have traditionally provided assistance and care to patients with cognitive decline, but this has resulted in financial and emotional burdens for both caregivers and patients, impacting their quality of life. To address this issue, Ambient Assistive Living (AAL) technologies that incorporate Internet of Things (IoT) and Artificial Intelligence (AI) can replace or complement caregivers by enabling intelligent learning in smart buildings. This review evaluates the intelligence complements provided by smart buildings enabled with such capabilities to increase the quality of life and autonomy of cognitively declined occupants. Existing contributions primarily focus on learning occupants’ behavior to identify assistive services and solutions, which are delivered through technological interventions or caregivers. However, there are several key research gaps that need to be addressed. The most important is the lack of adequate implementation of technological interventions to fully support the occupants’ autonomy and independence. Other gaps include challenges in usability and acceptability, ethical concerns, systems’ comprehensiveness, and the need for human-in-the-loop. To address these gaps, a conceptual framework is proposed as future research directions for the applications of smart buildings supporting cognitively declined occupants. The framework aims to facilitate the implementation of technological interventions that can enhance occupants’ autonomy and independence, address usability and acceptability challenges, and ensure ethical considerations and system comprehensiveness. This review provides insights into the current state-of-the-art of AAL technologies and highlights research directions for improving the quality of life and autonomy of cognitively declined occupants.

Cyber-Physical System-Based Path Tracking Control of Autonomous Vehicles Under Cyber-Attacks

As one of the typical applications of cyber-physical system (CPS), autonomous vehicles (AVs) are vulnerable to malicious disturbance from cyber-attacks while tracking the desired path. This paper focuses on CPS-based path tracking control problem of AVs under cyber-attacks. Firstly, the nonlinear state and measurement equations of AVs under cyber-attacks are established based on vehicle dynamics model. Secondly, to improve the robustness of AVs against cyber-attacks, sensor redundancy is introduced. A cyber-attack detection method is designed by using extended Kalman filter (EKF), and the computational complexity of the cyber-attack detection method is analyzed. In addition, sensor switching rules are developed to isolate the disturbance of cyber-attacks. Then, the control problem of the AVs is formulated based on model predictive control (MPC). Input-to-state stability (ISS) of the control system under cyber-attacks is established, and a link between the tolerable attack intensity and the detection thresholds is clearly revealed. Finally, the simulation results demonstrate the effectiveness of the proposed control strategy.