Cyber-physical Systems Applications Research Articles

Brain Signals Classification Based on Fuzzy Lattice Reasoning

Cyber-Physical System (CPS) applications including human-robot interaction call for automated reasoning for rational decision-making. In the latter context, typically, audio-visual signals are employed. Τhis work considers brain signals for emotion recognition towards an effective human-robot interaction. An ElectroEncephaloGraphy (EEG) signal here is represented by an Intervals’ Number (IN). An IN-based, optimizable parametric k Nearest Neighbor (kNN) classifier scheme for decision-making by fuzzy lattice reasoning (FLR) is proposed, where the conventional distance between two points is replaced by a fuzzy order function (σ) for reasoning-by-analogy. A main advantage of the employment of INs is that no ad hoc feature extraction is required since an IN may represent all-order data statistics, the latter are the features considered implicitly. Four different fuzzy order functions are employed in this work. Experimental results demonstrate comparably the good performance of the proposed techniques.

Hybrid DeepGCL model for cyber-attacks detection on cyber-physical systems

The urgency of solving the problem of ensuring the security of cyber-physical systems is due to ensure their correct functioning. Cyber-physical system applications have a significant impact on different industrial sectors. The number and variety of cyber-attacks are growing, aimed not only at obtaining data from cyber-physical systems but also managing the production process itself. Detecting and preventing attacks on cyber-physical systems is critical because they can lead to financial losses, production interruptions, and therefore endanger national security. This paper proposes a deep hybrid model based on three parallel neural architectures: a one-dimensional convolutional neural network, a gated recurrent unit neural network, and a long short-term memory neural network. The SPOCU activation function is considered in hidden layers of the proposed model and improves its performance. Furthermore, to improve the classification accuracy, a modified version of Adam optimizer is considered. The experiments are conducted on two datasets: raw water treatment plant and gasoil heater loop process as the cyber-physical system applications. They contain information about the normal behavior of these systems and their failures caused by cyber-attacks. The results show that the proposed model outperforms the recent works using machine learning techniques.

In-network generalized trustworthy data collection for event detection in cyber-physical systems.

Sensors in Cyber-Physical Systems (CPS) are typically used to collect various aspects of the region of interest and transmit the data towards upstream nodes for further processing. However, data collection in CPS is often unreliable due to severe resource constraints (e.g., bandwidth and energy), environmental impacts (e.g., equipment faults and noises), and security concerns. Besides, detecting an event through the aggregation in CPS can be intricate and untrustworthy if the sensor's data is not validated during data acquisition, before transmission, and before aggregation. This paper introduces In-network Generalized Trustworthy Data Collection (IGTDC) framework for event detection in CPS. This framework facilitates reliable data for aggregation at the edge of CPS. The main idea of IGTDC is to enable a sensor's module to examine locally whether the event's acquired data is trustworthy before transmitting towards the upstream nodes. It further validates whether the received data can be trusted or not before data aggregation at the sink node. Additionally, IGTDC helps to identify faulty sensors. For reliable event detection, we use collaborative IoT tactics, gate-level modeling with Verilog User Defined Primitive (UDP), and Programmable Logic Device (PLD) to ensure that the event's acquired data is reliable before transmitting towards the upstream nodes. We employ Gray code in gate-level modeling. It helps to ensure that the received data is reliable. Gray code also helps to distinguish a faulty sensor. Through simulation and extensive performance analysis, we demonstrate that the collected data in the IGTDC framework is reliable and can be used in the majority of CPS applications.

Dynamic Edge and Cloud Service Integration for Industrial IoT and Production Monitoring Applications of Industrial Cyber-Physical Systems

Industrial cyber-physical systems rely increasingly on data from Internet-of-Things (IoT) devices and other systems as continuously emerging use cases implement new intelligent features. Edge computing can be seen as an extension of the cloud in close physical proximity, in which some of the typical cloud computing loads are beneficial to run. This article studies data analytics application development for integration of industrial IoT data and composition of application services executed on edge and cloud. A solution is designed to support heterogeneous hardware and run-time platforms, and focuses on the service layer that enables flexible orchestration of data flows and dynamic service compositions. The unified model and system architecture implemented, using the open Arrowhead framework model, is verified through two representative industrial use cases.

B Smart Technologies in Engineering Applications of Cyber Physical System in Healthcare: Sensing, Imaging, Computing and Networking

B Smart Technologies in Engineering Applications of Cyber Physical System in Healthcare: Sensing, Imaging, Computing and Networking

Temperature-Constrained Reliability Optimization of Industrial Cyber-Physical Systems Using Machine Learning and Feedback Control

As the backbone of Industry 4.0, industrial cyber-physical systems (ICPSs) that are geographically dispersed, federated, cooperative, and security-critical systems become the center of interest from both industry and academia. In ICPS, there are huge amounts of devices, such as sensors and actuators, which are embedded and networked together to improve the performance of real-time monitoring and control. Reliability and temperature are two important concerns of these embedded and networked devices in ICPS due to their stringent requirement of reliable execution and long lifespan. In this article, we study the problem of maximizing soft-error reliability of CPU- and GPU-integrated embedded platforms deployed in ICPS under the temperature constraint. To speed up the estimation of soft-error rate (SER) and temperature, we train an artificial neural network (ANN) that is able to quickly and accurately derive the system’s SER and temperature. To solve the temperature-constrained reliability optimization problem, we propose a feedback control-based task scheduling scheme that adaptively determines the number of tasks admitted in the system and the number of replicas for the admitted tasks. We perform a series of simulation experiments to verify the efficacy of our scheme. The experimental results demonstrate that: 1) the estimated SER and temperature derived by our ANN-based method are very close to the ground-truth data and 2) our proposed feedback control-based task scheduling method can improve system reliability by up to 184.2% with a lower peak temperature when compared with one baseline and two state-of-the-art methods. Note to Practitioners—This article is motivated by the safety-critical industrial cyber-physical system (ICPS) applications necessitating reliable execution and long lifespan, which could be realized by increasing reliability and controlling operating temperature. Our goal is to improve the system reliability of CPU- and GPU-integrated multiprocessor systems-on-chip (MPSoCs) deployed in ICPS under the temperature constraint. Most of the existing papers target either reliability or temperature. A few recent papers have focused on reliability and temperature optimization simultaneously. However, they are not designed for ICPS and do not consider the widely accepted CPU- and GPU-integrated MPSoC platforms. This article proposes a machine learning-based approach that trains an artificial neural network (ANN) to facilitate the online estimation of system SER and temperature. Compared to the offline estimation using simulation tools, the online approach is more applicable to real-time ICPS applications. This article also designs a feedback control-based approach for improving system reliability and reducing peak temperature of the CPU- and GPU-integrated MPSoCs by determining the number of tasks to be admitted and the number of replicas for tasks.

Towards improving detection performance for malware with a correntropy-based deep learning method

With the rapid development of Internet of Things (IoT) technologies, the detection and analysis of malware have become a matter of concern in the industrial application of Cyber-Physical System (CPS) that provides various services using the IoT paradigm. Currently, many advanced machine learning methods such as deep learning are popular in the research of malware detection and analysis, and some achievements have been made so far. However, there are also some problems. For example, considering the noise and outliers in the existing datasets of malware, some methods are not robust enough. Therefore, the accuracy of malware classification still needs to be improved. Aiming at this issue, we propose a novel method that combines the correntropy and the deep learning model. In our proposed method for malware detection and analysis, given the success of the mixture correntropy as an effective similarity measure in addressing complex datasets with noise, it is therefore incorporated into a popular deep learning model, i.e., Convolutional Neural Network (CNN), to reconstruct its loss function, with the purpose of further detecting the features of outliers. We present the detailed design process of our method. Furthermore, the proposed method is tested both on a real-world malware dataset and a popular benchmark dataset to verify its learning performance.

Optimal control and learning for cyber‐physical systems

Optimal control and learning for <scp>cyber‐physical</scp> systems

A Review of Current Research Trends in Power-Electronic Innovations in Cyber–Physical Systems

In this article, a broad overview of the current research trends in power-electronic innovations in cyber–physical systems (CPSs) is presented. The recent advances in semiconductor device technologies, control architectures, and communication methodologies have enabled researchers to develop integrated smart CPSs that can cater to the emerging requirements of smart grids, renewable energy, electric vehicles, trains, ships, the Internet of Things (IoT), and so on. The topics presented in this article include novel power-distribution architectures, protection techniques considering large renewable integration in smart grids, wireless charging in electric vehicles, simultaneous power and information transmission, multihop network-based coordination, power technologies for renewable energy and smart transformer, CPS reliability, transactive smart railway grid, and real-time simulation of shipboard power systems. It is anticipated that the research trends presented in this article will provide a timely and useful overview to the power-electronics researchers with broad applications in CPSs.

Exploring reliable edge‐cloud computing for service latency optimization in sustainable cyber‐physical systems

AbstractIn recent years, the advance in information technology has promoted a wide span of emerging cyber‐physical systems (CPS) applications such as autonomous automobile systems, healthcare monitoring, and process control systems. For these CPS applications, service latency management is extraordinarily important for the sake of providing high quality‐of‐experience to terminal users. Edge‐cloud computing, integrating both edge computing and cloud computing, is regarded as a promising computation paradigm to achieve low service latency for terminal users in CPS. However, existing latency‐aware edge‐cloud computing methods dedicated for CPS fail to jointly consider energy budgets and reliability requirements, which may greatly degrade the sustainability of CPS applications. In this article, we explore the problem of minimizing service latency of edge‐cloud computing coupled CPS under the constraints of energy budgets and reliability requirements. We propose a two‐stage approach composed of static and dynamic service latency optimization. At static stage, Monte‐Carlo simulation with integer‐linear‐programming technique is adopted to find the optimal computation offloading mapping and task backup number. At dynamic stage, a backup‐adaptive dynamic mechanism is developed to avoid redundant data transmissions and executions for achieving additional energy savings and service latency enhancement. Experimental results show that our solution is able to reduce system service latency by up to 18.3% compared with representative baseline solutions.

Hybrid Modeling and Model Transformation of AADL for Verifying the Properties of CPS Space-Time Compositions

The wide application of Cyber Physical System (CPS) makes the security of CPS more and more concerned. As the key factors affecting the safety of CPS, space and time have also become the current research hotspot. The space and time safety of CPS requires that CPS arrives at the specified place at the specified time, time and space should meet the safety requirements of the CPS in the current CPS environment. We call the behavior space-time compositions. In order to solve the problem that CPS lacks the method of modeling and verification of space-time compositions, a hybrid Architecture Analysis & Design Language (AADL) modeling and model transformation method for CPS space-time compositions verification is proposed. Firstly, space-time description capability is extended in the AADL behavior annex and Hybrid AADL (HAADL) is proposed. Secondly, differential equations and space-time compositions vector are introduced in Process Algebra to propose Hybrid Space-Time Communication Sequential Processes (HS-TCSP). Furthermore, the Hybrid AADL is transformed to HS-TCSP. Finally, an example of an aircraft collision avoidance system is used to verify the effectiveness of the method.

Special issue on data processing techniques and applications for Cyber-Physical Systems (DPTA 2019)

Special issue on data processing techniques and applications for Cyber-Physical Systems (DPTA 2019)

RaPId - A Parameter Estimation Toolbox for Modelica/FMI-Based Models Exploiting Global Optimization Methods

Abstract This paper describes new additions to the Rapid Parameter Identification Toolbox (RaPId), which is an open-source MATLAB toolbox for parameter estimation using models developed with the Modelica language and exported with the functional mock-up interface (FMI) Standard. These additions include an updated graphical user interface (GUI), an optimization method utilizing multiple starting points for a gradient descent optimization, and examples for different cyber-physical system applications such as the Duffing-Holmes equation modeling in a form of electrical circuit and a hydroelectric power plant modeling.

A Survey of Specification-based Intrusion Detection Techniques for Cyber-Physical Systems

Cyber-physical systems (CPS) integrate computa-tion and communication capabilities to monitor and control physical systems. Even though this integration improves the performance of the overall system and facilitates the application of CPS in several domains, it also introduces security challenges. Over the years, intrusion detection systems (IDS) have been de-ployed as one of the security controls for addressing these security challenges. Traditionally, there are three main approaches to IDS, namely: anomaly detection, misuse detection and specification-based detection. However, due to the unique attributes of CPS, the traditional IDS need to be modified or completely replaced before it can be deployed for CPS. In this paper, we present a survey of specification-based intrusion detection techniques for CPS. We classify the existing specification-based intrusion detection techniques in the literature according to the following attributes: specification source, specification extraction, specifi-cation modelling, detection mechanism, detector placement and validation strategy. We also discuss the details of each attribute and describe our observations, concerns and future research directions. We argue that reducing the efforts and time needed to extract the system specification of specification-based intrusion detection techniques for CPS and verifying the correctness of the extracted system specification are open issues that must be addressed in the future.

Adaptive self-learning distributed and centralized control approaches for smart factories

The increasing application of cyber-physical systems creates a manufacturing environment in which the technical requirements for distributed control approaches, self-learning systems and analytics of previously untapped data are given. While distributed control approaches are capable to evaluate this information locally and react immediately, centralized approaches react inertly to analyzed machine performance data. In this paper, we study the performance and ability to address the ever increasing challenges in industry of both types of control approaches within an established multi-agent based discrete event simulation.

An Asset Administration Shell Method for Data Exchange Between Manufacturing Software Applications

The multifaceted industrial application of cyber-physical systems (CPSs) has increased, enabling tight interactions between physical and computational elements. Industry 4.0 aims to promote industrial application of CPSs. The asset administration shell (AAS) represents the practical embodiment of the CPS, and can be obtained by integrating information and communication technologies. Most prior studies concentrated on AASs for hardware assets in the manufacturing domain; they seldom considered AASs for software applications. To fill this gap, we present a method for implementing AASs for software, i.e., a manufacturing execution system (MES) combined with an enterprise resource planning (ERP) system. This method allows runtime data exchange between an MES and ERP; this is better than the conventional file-based method. Finally, a data exchange case (a manufacturing order) is presented to validate our AAS method.

Cyber-Physical System (CPS) Application- A REVIEW

Due to its vast number of suitable applications, Cyber-Physical Systems (CPS) has been one of the emerging and rapidly-developed technologies in modern life. Its applications can be integrated and deployed in numerous sectors, for instance the automobile, home appliances, medical and industrial sectors. This field has seen immense development and research taking place, including the architecture model, security, communications mode, control systems model and also signal processing. CPS plays an important role in Industrial Revolution 4.0, where it is an integration of urbane network with intelligent production or process system. CPS main objective is to control the global networks with high credibility of data secured. This paper is basically a review of the CPS architectures, characteristics and its applications in daily basis. This review paper discusses how CPS are implemented in various industries and how it improves the efficiency of a system or application. Additionally, security enhancements in certain aspects of CPS application will also be reviewed and addressed.

Управление информационными процессами в системах моделирования природных объектов

The paper proposes a methodological approach and software for managing information processes in systems for modeling natural objects on the example of a system for operational forecasting of fl oods. The proposed approach is based on application of cyber-physical systems, the industrial Internet and fog computing concepts. The results of testing the technologies for managing information processes demonstrate increasing both reliability of modeling and effi ciency of using the fl ood forecasting system computing resources.

Millimeter-Wave Radars-on-Chip Enabling Next-Generation Cyberphysical Infrastructures

This article provides an overview of current and future implementations of high-resolution millimeter-wave active sensor technologies for applications in cyber-physical systems. The two major groups of active sensors, that is, pulsed and frequency modulated continuous-wave radars, are studied, and their performance for benchmark applications are compared. The specific design challenges of these radar technologies are discussed, and system/block level solutions to mitigate them are proposed. Finally, system architectures to co-integrate the proposed radar-on-chip and 5G communication transmitters are introduced.

On Physical-Social-Aware Localness Inference by Exploring Big Data from Location-Based Services

A user's localness (i.e., whether a user is a local resident in a city or not) and a venue's local attractiveness (i.e., the likelihood of a venue to attract local people) are important information for many location-based applications related with Cyber-Physical Systems (CPS), such as participatory sensing, urban planning, traffic control and localized travel recommendations. Previous effort has been devoted to geo-locating users in a city using supervised learning approaches, which depend on the availability of high quality training datasets. However, it is difficult to obtain such training datasets in the real-world CPS applications due to the issue of privacy. In this work, we develop an unsupervised approach, called a Physical-Social-Aware Inference (PSAI) scheme, to jointly infer a user's localness and a venue's local attractiveness by exploring both the physical and social information embedded in the location-based social networks (LBSN). We further implement a parallel PSAI framework on the platform of a Graphic Processing Unit (GPU) to enhance its ability to process large-scale data. Our extensive experiments on the real-world LBSN datasets demonstrate the effectiveness and efficiency of the PSAI scheme compared to the state-of-the-art baselines.