Cyber-physical System Approach Research Articles

Quantifying the reciprocal impacts of capital and logistics networks in the supply chains: A cyber–physical system approach

Capital flow and logistics are closely linked in the supply chain. Current research primarily assesses their independent resilience during disruptions, but this study reveals an iterative bidirectional impact. The interconnection of shareholding, mutual guarantees, and borrowing within the supply chain has created financial relationships that were previously nonexistent. However, the evolution of logistics has not aligned with these financial developments, leading to a growing heterogeneity between capital flow and logistics. While the heterogeneity between capital flow and logistics within supply chains is crucial, previous studies, especially those rooted in complex networks, often overlook its impact. Drawing inspiration from the cyber–physical system, this paper abstracts logistics and capital flow into respective networks. A model of mutual dependence quantifies reciprocal impacts between capital and logistics networks, exploring the consequences of heterogeneity on the supply chain. Simulation results are compared with traditional single-layer network models. Finally, an innovative edge removal method is proposed to enhance supply chain robustness. Our simulation results indicate that the influence of the capital network on the logistics network and their relationship to heterogeneity is nonlinear. The edge removal method enhances supply chain network robustness in a nonlinear manner, with optimal effectiveness achieved at p_l=0.2.

A Survey on Cyber-Resilience Approaches for Cyber-Physical Systems

Concerns for the resilience of Cyber-Physical Systems (CPS)s in critical infrastructure are growing. CPS integrate sensing, computation, control, and networking into physical objects and mission-critical services, connecting traditional infrastructure to internet technologies. While this integration increases service efficiency, it has to face the possibility of new threats posed by the new functionalities. This leads to cyber-threats, such as denial-of-service, modification of data, information leakage, spreading of malware, and many others. Cyber-resilience refers to the ability of a CPS to prepare, absorb, recover, and adapt to the adverse effects associated with cyber-threats, e.g., physical degradation of the CPS performance resulting from a cyber-attack. Cyber-resilience aims at ensuring CPS survival by keeping the core functionalities of the CPS in case of extreme events. The literature on cyber-resilience is rapidly increasing, leading to a broad variety of research works addressing this new topic. In this article, we create a systematization of knowledge about existing scientific efforts of making CPSs cyber-resilient. We systematically survey recent literature addressing cyber-resilience with a focus on techniques that may be used on CPSs. We first provide preliminaries and background on CPSs and threats, and subsequently survey state-of-the-art approaches that have been proposed by recent research work applicable to CPSs. In particular, we aim at differentiating research work from traditional risk management approaches based on the general acceptance that it is unfeasible to prevent and mitigate all possible risks threatening a CPS. We also discuss questions and research challenges, with a focus on the practical aspects of cyber-resilience, such as the use of metrics and evaluation methods as well as testing and validation environments.

Cyber Resilience Approaches for Cyber Physical Systems

Cyber-physical systems (CPS) integrate physical processes with computing, communication, and control systems to increase efficiency, reliability, and safety. However, these systems are also vulnerable to cyber attacks, which could have severe consequences, such as loss of life, property damage, and economic disruption. To ensure the safety and security of modern society, it is crucial to ensure that CPS are cyber- resilient, meaning they can continue to function and recover from cyber attacks. This requires a multi-faceted approach that includes secure design, risk assessment, monitoring and response, redundancy and backup, and training and education. By implementing these strategies, organizations can improve the cyber resilience of their CPS, reducing the risk of cyber attacks and promoting the safety and security of modern society.

Security strategy for autonomous vehicle cyber-physical systems using transfer learning

Cyber-physical systems (CPSs) are emergent systems that enable effective real-time communication and collaboration (C&C) of physical components such as control systems, sensors, actuators, and the surrounding environment through a cyber communication infrastructure. As such, autonomous vehicles (AVs) are one of the fields that have significantly adopted the CPS approach to improving people's lives in smart cities by reducing energy consumption and air pollution. Therefore, autonomous vehicle-cyber physical systems (AV-CPSs) have attracted enormous investments from major corporations and are projected to be widely used. However, AV-CPS is vulnerable to cyber and physical threat vectors due to the deep integration of information technology (IT), including cloud computing, with the communication process. Cloud computing is critical in providing the scalable infrastructure required for real-time data processing, storage, and analysis in AV-CPS, allowing these systems to work seamlessly in smart cities. CPS components such as sensors and control systems through network infrastructure are particularly vulnerable to cyber-attacks targeted by attackers using the communication system. This paper proposes an intelligent intrusion detection system (IIDS) for AV-CPS using transfer learning to identify cyberattacks launched against connected physical components of AVs through a network infrastructure. First, AV-CPS was developed by implementing the controller area network (CAN) and integrating it into the AV simulation model. Second, the dataset was generated from the AV-CPS. The collected dataset was then preprocessed to be trained and tested via pre-trained CNNs. Third, eight pre-trained networks were implemented, namely, InceptionV3, ResNet-50, ShuffleNet, MobileNetV2, GoogLeNet, ResNet-18, SqueezeNet, and AlexNet. The performance of the implemented models was evaluated. According to the experimental evaluation results, GoogLeNet outperformed all other pre-rained networks, scoring an F1- score of 99.47%.

Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach.

In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.

Modeling and Verification of Uncertain Cyber-Physical System Based on Decision Processes

Currently, there is uncertainty in the modeling techniques of cyber-physical systems (CPS) when faced with the multiple possibilities and distributions of complex system behavior. This uncertainty leads to the system’s inability to handle uncertain data correctly, resulting in lower reliability of the system model. Additionally, existing technologies struggle to verify the activity and safety of CPS after modeling, lacking a dynamic verification and analysis approach for uncertain CPS properties.This paper introduces a generalized possibility decision process as a system model. Firstly, the syntax and semantics of generalized possibility temporal logic with decision processes are defined. Uncertain CPS is extended by modeling it based on time-based differential equations and uncertainty hybrid time automaton. After that, model checking is performed on the properties of activity and safety using fuzzy linear time properties. Finally, a cold–hot hybrid constant-temperature system model is used for simulation experiments. By combining theory and experiments, this paper provides a new approach to the verification of uncertain CPS, effectively addressing the state explosion problem. It plays a crucial role in the design of uncertain CPS and offers a key solution for model checking in the presence of uncertainty.

Resilient neural network-based control of nonlinear heterogeneous multi-agent systems: a cyber-physical system approach

Resilient neural network-based control of nonlinear heterogeneous multi-agent systems: a cyber-physical system approach

Cyber-physical system assisted platooning method using dynamic correlation of destination vectors in urban roads

Economic losses due to delays in traffic congestion is serious social problems. Platooning has recently been attracting attentions from many researchers to alleviate traffic jams, especially in highway. On highways, platooning can have positive effects, such as running at a constant speed with multiple vehicles which leads to fuel-efficient driving, reducing air resistance and so on. However, the impacts of platooning in urban roads have not been addressed in detail since traffic lights may break the platoons. In this paper, we propose a platooning method using L2 norm and cosine similarity to form platoon with highly similar routes. Also, we investigate the sorting method within a platoon according to each vehicle’s straightness to eliminate Head of Line blocking at intersections and improve throughput at intersections. We propose a Cyber-Physical System approach to control platoon collaboratively in urban roads. Computer simulation results confirmed that the proposed method shortens the average travel time by 34.31%. This paper also shows the validity of forming platoon based on destination vectors and sorting vehicles within a platoon.

Conceptualizing data-driven closed loop production systems for lean manufacturing of complex biomedical devices—a cyber-physical system approach

A model is presented for shifting the manual intensive manufacturing process of complex biomedical devices towards more lean and efficient production process via application of concepts of cyber physical systems in combination with big data and analytics in a closed loop manner. The concept model is capable of handling high product volumes and variety, has ability for self-adaptation and correction in various operating conditions, and offers real-time quality control. The approach acknowledges the challenge of these industries operating in a strict regulated environment and the higher standards of built-in quality required by developing a closed loop process, proposed to be built in accordance to the requirements of regulatory bodies and current Industry 4.0 practices. The proposed model illustrates that modern manufacturing methodologies and concepts can be integrated and adopted in such highly regulated manufacturing environments and that the model can be deployed to different production scenarios.

Security Risk Assessment Approach for Distribution Network Cyber Physical Systems Considering Cyber Attack Vulnerabilities.

With the increasing digitalization and informatization of distribution network systems, distribution networks have gradually developed into distribution network cyber physical systems (CPS) which are deeply integrated with traditional power systems and cyber systems. However, at the same time, the network risk problems that the cyber systems face have also increased. Considering the possible cyber attack vulnerabilities in the distribution network CPS, a dynamic Bayesian network approach is proposed in this paper to quantitatively assess the security risk of the distribution network CPS. First, the Bayesian network model is constructed based on the structure of the distribution network and common vulnerability scoring system (CVSS). Second, a combination of the fuzzy analytic hierarchy process (FAHP) and entropy weight method is used to correct the selectivity of the attacker to strike the target when cyber attack vulnerabilities occur, and then after considering the defense resources of the system, the risk probability of the target nodes is obtained. Finally, the node loads and node risk rates are used to quantitatively assess the risk values that are applied to determine the risk level of the distribution network CPS, so that defense strategies can be given in advance to counter the adverse effects of cyber attack vulnerabilities.

Cognitive Grasping and Manipulation of Unknown Object with Control Grip Force using Cyber Physical System Approach

Service robots working in human environments must have the ability to grasp a wide variety of unseen objects with ability to grasp a wide variety of unseen objects with appropriate grip force without knowing their properties and its response to action in a human environment. This research uses a novel Cyber Physical System framework to estimate and apply minimum grasping force for unknown objects under a task motion using only tactile motion data captured from sensor-less acceleration-based controlled common gripper. The goal is to use this novel framework for developing functions for position-controlled robots to perform real-time cognitive grasping and enable quick implementation across multiple robots. This paper uses soft sensing inputs from the earlier research to characterize unknown object properties like stiffness, mass and surface interaction and show how a data-based analytics algorithm learns a wide range of object properties and slip status along with task action dynamics features, to propose the grasping action with minimum applied force. We validated that the minimum grasping force is able to hold the object firmly during any task motion. In order to improve the learning, the paper proposes a part of the framework to use virtual simulation to gather more learning data and show results that compare well between simulated results with experimental data. We use the simulated data to train Reinforcement Learning approach and show that small variation in object width can be learnt to identify optimum gripping position.

Contribution to the Design and Implementation of a Reflexive Cyber-Physical System: Application to Air Quality Prediction in the Vallees des Gaves

This thesis aims to set up a scientific approach to monitor and take preventive actions on the air quality for the actors of a territory not covered by conventional measuring stations. Thus, a Cyber-Physical System (CPS) approach combined with Prognostics Health Management (PHM) methodologies is chosen to move toward a self-monitoring and self-reconfiguration system. To collect data in an inexpensive manner, measurement stations with low-cost sensors (LCS) are developed. LCS have drawbacks and the first part of this thesis is the use of redundancy and a proposed algorithm to increase their hardware and data reliability. A first station is deployed as proof of concept and the region is already receiving real-time data. The next phase is to build forecasting models to help authorities make decisions.

A Domain-Specific, Model Based Systems Engineering Approach for Cyber-Physical Systems

Model Based Systems Engineering as a scientific discipline tries to address the increasing complexity of today’s cyber-physical systems by utilizing different kinds of models. In practical application, however, this approach is often constrained to SysML-based object modeling. Even though this appears to be a suitable approach for dealing with complexity, various restrictions limit stakeholder acceptance. Considering scientific discussions in the context of modeling shows two different schools of thought. On the one hand, arguments for more formalized and rigorous concepts can be found, where on the other hand, the need for more stakeholder-oriented and easier-to-understand concepts is postulated. As both are reasonable, the question of integration arises. To address this aspect, we developed the concept of Domain Specific Systems Engineering. Our research in this field lasted for nearly a decade, and different aspects have been investigated. This paper contributes a summary of the overall approach that integrates the various aspects investigated so far. Thus, the underlying concepts are explained, and the corresponding modeling stack and tool-chain are described in more detail. Further, the practical experiences from various case studies are summarized, and identified shortcomings are discussed.

Stability of Cyber-Physical Systems of Numerical Methods for Stochastic Differential Equations: Integrating the Cyber and the Physical of Stochastic Systems

This paper presents the cyber-physical system (CPS) of a numerical method (the widely-used Euler- Maruyama method) and establishes a foundational theory of the CPSs of numerical methods for stochastic differential equations (SDEs), which transforms the way we understand the relationship between the numerical method and the underlying dynamical system. Unlike in the literature where they are treated as separate systems linked by inequalities, the CPS is a seamless integration of the SDE and the numerical method and we construct a new and general class of stochastic impulsive differential equations (SiDEs) that can serve as a canonic form of the CPSs of numerical methods. By the CPS approach, we show the equivalence and intrinsic relationship between the stability of the SDE and the stability of the numerical method using the Lyapunov stability theory we develop for our class of SiDEs. Applying our established theory, we present the CPS Lyapunov inequality that is the necessary and sufficient condition for meansquare stability of the CPS of the Euler-Maruyama method for linear SDEs. The proposed CPS and theory initiate the study of systems numerics and provoke many open and interesting problems for future work.

A cyber-physical system approach to zero-defect manufacturing in light-gauge steel frame assemblies

Recent advances in manufacturing research have set the stage for the industrial integration of zero-defect manufacturing strategies, aiming towards a more sustainable production paradigm. A systematically deployment guideline to implement zero-defect manufacturing is needed to transform the future cyber-physical factory floor. This paper describes a novel framework based on a well-known cyber-physical architecture that provides advanced information analytics, robust information flows, and data acquisition systems that support defect detection and prediction introduces repair technologies and sets up procedures for preventive maintenance. Through continuous inspection of product and equipment status during the manufacturing process, raw quality and tool health data from different sources is used to obtain key performance indicators. Statistical tools such as cross-correlation are then applied to quantify underlying relationships between product quality specifications and equipment health. The resulting correlations are then used to reduce non-conformance of products manufactured by implementation of preventive maintenance. A unified implementation for zero-defect manufacturing cyber-physical processes eases their integration in future Industry 4.0 facilities and validated in the context of offsite construction manufacturing of steel frame assemblies.

A Cyber-Physical System and Graph-Based Approach for Transportation Management in Smart Cities

In the last decade, technological advancements in the cyber-physical system have set the basis for real-time and context-aware services to ease human lives. The citizens, especially travelers, want to experience a safe, healthy, and timely journey to their destination. Smart and on-ground real-time traffic analysis helps authorities further improve decision-making to ensure safe and convenient traveling. In this paper, we proposed a transport-control model that exploits cyber-physical systems (CPS) and sensor-technology to continuously monitor and mine the big city data for smart decision-making. The system makes use of travel-time, traffic intensity, vehicle’s speed, and current road conditions to construct a weighted city graph representing the road network. Traditional graph algorithms with efficient implementation technologies are employed to respond to commuters’ and authorities’ needs in order to achieve a smart and optimum transportation system. To efficiently process the incoming big data streams, the proposed architecture uses the Apache GraphX tool with several parallel processing nodes, along with Spark and Hadoop that ultimately provide better performance against various state-of-the-art solutions. The system is thoroughly evaluated in terms of system throughput and processing time, revealing that the proposed system is efficient, robust, and scalable.

Automation of Laser Welding Process by Cyber-Physical System (CPS) Approach

Automation of Laser Welding Process by Cyber-Physical System (CPS) Approach

A Cyber-Physical Systems Approach to Collaborative Intersection Management and Control

As urban population increases steadily, current mobility and transportation services become inefficient, needing an urgent improvement since they represent a major source of discomfort and economic losses. In this context, the concept of intelligent transportation systems takes relevance as a way of optimizing those services with the use of technology. This work proposes a cyber-physical systems approach to collaborative urban traffic control. Specifically, a three-layer architecture is proposed to address the intersection management problem, making use of modular cyber-physical object abstractions and timed Petri nets. Preliminary implementations in a pseudo-real environment show that the proposed system is capable of handling the communication and processing loads, while improving traffic performance with respect to classical solutions for traffic signals, outperforming timed, Webster and coordinated control methods in pilot-scale tests.

A cyber-physical system approach for enabling ventilation on-demand in an underground mining site

Creating awareness on the gas concentration along with the timely provision of fresh air is of major importance for preserving the normal operating condition of mining equipment as well as for the well-being of people working underground. However, the deployment and application of a data acquisition and control system in underground sites remain challenging.In order to safeguard the mining personnel, a cyber-physical system for monitoring the concentration of gases enabling adaptive control of the ventilation system is discussed in this study. Long-range communication modules are used to collect field data that are processed to enable awareness over underground conditions. A ventilation on-demand module and an energy consumption evaluation component enable dynamic control of the ventilation system based on the measured gases concentration. A prototype implementation has been tested on a simulated use case with real-world sensor data.

Sense–Analyze–Respond–Actuate (SARA) Paradigm: Proof of Concept System Spanning Nanoscale and Macroscale Actuation for Detection of Escherichia coli in Aqueous Media

Foodborne pathogens are a major concern for public health. We demonstrate for the first time a partially automated sensing system for rapid (~17 min), label-free impedimetric detection of Escherichia coli spp. in food samples (vegetable broth) and hydroponic media (aeroponic lettuce system) based on temperature-responsive poly(N-isopropylacrylamide) (PNIPAAm) nanobrushes. This proof of concept (PoC) for the Sense-Analyze-Respond-Actuate (SARA) paradigm uses a biomimetic nanostructure that is analyzed and actuated with a smartphone. The bio-inspired soft material and sensing mechanism is inspired by binary symbiotic systems found in nature, where low concentrations of bacteria are captured from complex matrices by brush actuation driven by concentration gradients at the tissue surface. To mimic this natural actuation system, carbon-metal nanohybrid sensors were fabricated as the transducer layer, and coated with PNIPAAm nanobrushes. The most effective coating and actuation protocol for E. coli detection at various temperatures above/below the critical solution temperature of PNIPAAm was determined using a series of electrochemical experiments. After analyzing nanobrush actuation in stagnant media, we developed a flow through system using a series of pumps that are triggered by electrochemical events at the surface of the biosensor. SARA PoC may be viewed as a cyber-physical system that actuates nanomaterials using smartphone-based electroanalytical testing of samples. This study demonstrates thermal actuation of polymer nanobrushes to detect (sense) bacteria using a cyber-physical systems (CPS) approach. This PoC may catalyze the development of smart sensors capable of actuation at the nanoscale (stimulus-response polymer) and macroscale (non-microfluidic pumping).