Intelligent Cyber-physical Systems Research Articles

Transforming Transportation: Safe and Secure Vehicular Communication and Anomaly Detection With Intelligent Cyber–Physical System and Deep Learning

Transforming Transportation: Safe and Secure Vehicular Communication and Anomaly Detection With Intelligent Cyber–Physical System and Deep Learning

Enhancing Traffic Flow Prediction in Intelligent Cyber-Physical Systems: A Novel Bi-LSTM-Based Approach With Kalman Filter Integration

Enhancing Traffic Flow Prediction in Intelligent Cyber-Physical Systems: A Novel Bi-LSTM-Based Approach With Kalman Filter Integration

Hypergraph-Based Interference Avoidance Resource Management in Customer-Centric Communication for Intelligent Cyber-Physical Transportation Systems

Hypergraph-Based Interference Avoidance Resource Management in Customer-Centric Communication for Intelligent Cyber-Physical Transportation Systems

Guest Editorial of the Special section on Customers Centric Communication and Networked Control for Intelligent Cyber-Physical Transportation Systems

Guest Editorial of the Special section on Customers Centric Communication and Networked Control for Intelligent Cyber-Physical Transportation Systems

Artificial Intelligence for Traffic Prediction and Estimation in Intelligent Cyber-Physical Transportation Systems

Artificial Intelligence for Traffic Prediction and Estimation in Intelligent Cyber-Physical Transportation Systems

ETNAS: An energy consumption task-driven neural architecture search

Neural Architecture Search (NAS) is crucial in the field of sustainable computing as it facilitates the development of highly efficient and effective neural networks. However, it cannot automate the deployment of neural networks to accommodate specific hardware resources and task requirements. This paper introduces ETNAS, which is a hardware-aware multi-objective optimal neural network architecture search algorithm based on the differentiable neural network architecture search method (DARTS). The algorithm searches for a lower-power neural network architecture with guaranteed inference accuracy by modifying the loss function of the differentiable neural network architecture search. We modify the dense network in DARTS to simultaneously search for networks with a lower memory footprint, enabling them to run on memory-constrained edge-end devices. We collected data on the power consumption and time consumption of numerous common operators on FPGA and Domain-Specific Architectures (DSA). The experimental results demonstrate that ETNAS achieves comparable accuracy performance and time efficiency while consuming less power compared to state-of-the-art algorithms, thereby validating its effectiveness in practical applications and contributing to the reduction of carbon emissions in intelligent cyber–physical systems (ICPS) edge computing inference.

A quality evaluation method for intelligent traffic management system based on business data flow

Intelligent Traffic Management System (ITMS) is a complex and intelligent cyber-physical system (CPS) with multi-subsystem interaction, which plays a significant role in traffic safety. However, the quality evaluation requirements of ITMS, particularly its running quality, cannot be satisfied by the current quality evaluation metrics. Moreover, the present ITMS evaluation techniques are arbitrary. The effectiveness of road traffic is impacted because ITMS quality cannot be adequately assured. To fill this gap, this paper proposes a quality evaluation (QE) methodology based on the ITMS business data flow. First, the ITMS QE dimension extraction process was introduced to describe the ITMS architecture and activities; then the new evaluation indexes including intelligence, complexity and interactivity were proposed and an ITMS QE model was established; further through the measurement of metrics elements, the quality score of the indicators were calculated; finally a prototype tool was developed to verify the efficacy and practicability of the method. The results showed that the proposed method has the advantages of accurate problem tracking and decrease decision-making uncertainty. This is applicable to the ITMS QE in various operational scenarios.

Review and Analysis of the Development of Artificial Neural Networks

Introduction. The creation of intelligent cyber-physical systems is impossible without knowledge of the analysis and process of development of scientific thought regarding artificial neural networks. The main task of this article is research and analysis of the concept of intelligent technologies based on artificial neural networks. Knowledge of the peculiarities of the creation, formation, and development of knowledge about artificial neural networks is of particular importance for scientists, developers, and design engineers. The article consists of the following parts: first, different approaches to the problem of building artificial functions of the brain are highlighted, the views of which are, in turn, divided into monotypic and genotypic models. The next part is the analysis of the development of artificial intelligence systems, some facts of the process of the development of the artificial intelligence system are also introduced and the peculiarities of scientific opinion on the issues of artificial neural networks are clarified. Various concepts and views are considered, with the help of which it is possible to reproduce the calculation process for a more detailed analysis and synthesis of algorithms of intelligent systems. In the part about the state of the theory, attention is focused on the fact that researchers who could not get accurate analytical answers add to the scientific toolkit methods of experimental modeling either on digital machines or on mechanical models. In addition, it is noted that the model is not the result of research, but only a starting point for analyzing its behavior. In the part of artificial neural networks, the author touches on the following concepts: the logic of McCulloch and Pitts calculations in neural networks, the problems of assigning confidence coefficients, the principle of self-organization, which were first illustrated with the help of computer simulations, the principle of competitive learning, the Kohonen Self-Organizing Maps, multilayer networks of direct propagation taking into account the radial basis functions, which became an alternative to the multilayer perceptron, the support vector machine. As conclusions and as a result, the author receives a complete picture of the genesis of intelligent systems and the technology of artificial neural networks. The processes of development of scientific thought give a clear understanding of the features of intellectual technologies built with the help of artificial neural networks, features of functioning and calculation. Keywords: artificial neural networks, perceptron, theories of McCulloch-Pitts, intelligent computer systems, cyber-physical agent, mobile robot, robotics.

A Novel Back-Propagation Neural Network for Intelligent Cyber-Physical Systems for Wireless Communications

Wireless sensor networks, which play a significant role in monitoring complex environments that change rapidly over time, were used in the Artificial Intelligence method. External factors or the device designers themselves are both responsible for this complex behavior. Sensor networks often use machine learning techniques to adapt to such conditions, eliminating the need for excessive redesign. Cyber-physical systems (CPS) appeared as the promising option for improving physical-virtual interactions. The quality of the system containing processing information is primarily determined by the system function. There are many benefits obtained while combining Artificial Intelligence (AI) and Cyber-Physical Systems (CPSs) in buildings. In CPS-based indoor environment has various design schemes containing measurement and intelligent buildings in the control system consisting of detection, tracking, execution, and communication modules. The Multi-Agent System (MAS) is the smallest control unit that simulates among neurons and it flexibly provides the information. To mimic the interactions between human neurons, multi-agents are used. In this paper, the CPS’s information world is built on the fundamental principle of granular formal concepts and the theory of granular computing is investigated. The calculation module is used by Back-Propagation Neural Network (BPNN) for pattern recognition and classification by environmental information. Various parameters namely the normalized root mean square error, peak signal-to-noise ratio, mean square error, and the mean absolute error are chosen as the objective assessment criteria to assess the benefits of the proposed method and the effectiveness of the proposed system is proven.

Securing Secrets in Cyber-Physical Systems: A Cutting-Edge Privacy Approach with Consortium Blockchain.

In the era of interconnected and intelligent cyber-physical systems, preserving privacy has become a paramount concern. This paper aims a groundbreaking proof-of-concept (PoC) design that leverages consortium blockchain technology to address privacy challenges in cyber-physical systems (CPSs). The proposed design introduces a novel approach to safeguarding sensitive information and ensuring data integrity while maintaining a high level of trust among stakeholders. By harnessing the power of consortium blockchain, the design establishes a decentralized and tamper-resistant framework for privacy preservation. However, ensuring the security and privacy of sensitive information within CPSs poses significant challenges. This paper proposes a cutting-edge privacy approach that leverages consortium blockchain technology to secure secrets in CPSs. Consortium blockchain, with its permissioned nature, provides a trusted framework for governing the network and validating transactions. By employing consortium blockchain, secrets in CPSs can be securely stored, shared, and accessed by authorized entities only, mitigating the risks of unauthorized access and data breaches. The proposed approach offers enhanced security, privacy preservation, increased trust and accountability, as well as interoperability and scalability. This paper aims to address the limitations of traditional security mechanisms in CPSs and harness the potential of consortium blockchain to revolutionize the management of secrets, contributing to the advancement of CPS security and privacy. The effectiveness of the design is demonstrated through extensive simulations and performance evaluations. The results indicate that the proposed approach offers significant advancements in privacy protection, paving the way for secure and trustworthy cyber-physical systems in various domains.

A Novel Echo State Network Autoencoder for Anomaly Detection in Industrial IoT Systems

The Industrial Internet of Things (IIoT) technology had a very strong impact on the realization of smart frameworks for detecting anomalous behaviors that could be potentially dangerous to a system. In this regard, most of the existing solutions involve the use of Artificial Intelligence (AI) models running on Edge devices, such as Intelligent Cyber Physical Systems (ICPS) typically equipped with sensing and actuating capabilities. However, the hardware restrictions of these devices make the implementation of an effective anomaly detection algorithm quite challenging. Considering an industrial scenario, where signals in the form of multivariate time-series should be analyzed to perform a diagnosis, Echo State Networks (ESNs) are a valid solution to bring the power of neural networks into low complexity models meeting the resource constraints. On the other hand, the use of such a technique has some limitations when applied in unsupervised contexts. In this paper, we propose a novel model that combines ESNs and autoencoders (ESN-AE) for the detection of anomalies in industrial systems. Unlike the ESN-AE models presented in the literature, our approach decouples the encoding and decoding steps and allows the optimization of both the processes while performing the dimensionality reduction. Experiments demonstrate that our solution outperforms other machine learning approaches and techniques we found in the literature resulting also in the best trade-off in terms of memory footprint and inference time.

Fenceless Collision-Free Avoidance Driven by Visual Computation for an Intelligent Cyber–Physical System Employing Both Single- and Double-S Trajectory

Fenceless Collision-Free Avoidance Driven by Visual Computation for an Intelligent Cyber–Physical System Employing Both Single- and Double-S Trajectory

Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system.

The integration of image segmentation technology into packaging style design significantly amplifies both the aesthetic allure and practical utility of product packaging design. However, the conventional image segmentation algorithm necessitates a substantial amount of time for image analysis, rendering it susceptible to the loss of vital image features and yielding unsatisfactory segmentation results. Therefore, this study introduces a novel segmentation network, G-Lite-DeepLabV3+, which is seamlessly incorporated into cyber-physical systems (CPS) to enhance the accuracy and efficiency of product packaging image segmentation. In this research, the feature extraction network of DeepLabV3 is replaced with Mobilenetv2, integrating group convolution and attention mechanisms to proficiently process intricate semantic features and improve the network's responsiveness to valuable characteristics. These adaptations are then deployed within CPS, allowing the G-Lite-DeepLabV3+ network to be seamlessly integrated into the image processing module within CPS. This integration facilitates remote and real-time segmentation of product packaging images in a virtual environment.Experimental findings demonstrate that the G-Lite-DeepLabV3+ network excels at segmenting diverse graphical elements within product packaging images. Compared to the original DeepLabV3+ network, the intersection over union (IoU) metric shows a remarkable increase of 3.1%, while the mean pixel accuracy (mPA) exhibits an impressive improvement of 6.2%. Additionally, the frames per second (FPS) metric experiences a significant boost of 22.1%. When deployed within CPS, the network successfully accomplishes product packaging image segmentation tasks with enhanced efficiency, while maintaining high levels of segmentation accuracy.

How automation level influences moral decisions of humans collaborating with industrial robots in different scenarios.

Digitalization in intelligent manufacturing leads to the development of Industry 4.0/5.0 and human-cyber-physical systems. As many production technologies rely on teaming of human workers and intelligent cyber-physical systems such as industrial robots, human-robot collaboration is an intensively investigated topic in this transdisciplinary research area. To design industrial robots in a human-centered way, psychological knowledge concerning judgment and decision-making needs to be gained and integrated. This paper reports results from an experimental study (N = 222, 2 × 4 within-subjects design) using eight moral dilemmas framed in the context of human-robot-collaboration to examine the influence of spatial distance of an industrial robot and humans (no direct contact, different tasks vs. no direct contact, same task vs. handing-over contact, same task vs. direct contact, same task) on moral decisions. Additionally, the type of dilemma was varied, with every four dilemmas depicting a life-or-death and an injury scenario. Participants responded on a four-point-response scale which actions they would take indicating deontological or utilitarian moral decision-making. Results show a large effect of the proximity of the cooperation between robots and humans. The closer the collaboration the more a human tends to choose utilitarian moral choices. It is argued that this effect might stem from an adaptation of human rationality to the robot or overreliance and shift of responsibility to the robot team partner.

Virtual Prototyping for Modern Internet-of-Things Applications: A Survey

Modern technological industries fused with the Internet-of-Things (IoT) have been advancing rapidly. The joint usage of several technologies has led to the reshaping of the modeling and simulation techniques into the virtualization of physical systems. Thus, the concept of virtual prototyping has emerged as a significant development in distributed IoT applications that includes early exploration, optimization, and security assessments. Several industries have been employing various types of prototyping e.g., virtual platforms, digital twins, and application-specific virtualization techniques, to achieve individual needs for development. In this survey, we clarify some of these concepts and the distinctions between them, provide a comprehensive overview of various prototyping technologies, and discuss how several virtualization technologies play a transformative role in the design and operation of intelligent cyber-physical systems.

Emerging biotransduction strategies on soft interfaces for biosensing.

As a lab-on-soft biochip providing accurate and timely biomarker information, wearable biosensors can satisfy the increasing demand for intelligent e-health services, active disease diagnosis/therapy, and huge bioinformation data. As biomolecules generally could not directly produce detectable signals, biotransducers that specifically convert biomolecules to electrical or optical signals are involved, which determines the pivotal sensing performance including 3S (sensitivity, selectivity, and stability), reversibility, etc. The soft interface poses new requirements for biotransducers, especially equipment-free, facile operation, mechanical tolerance, and high sensing performance. In this review, we discussed the emerging electrochemical and optical biotransduction strategies on wearables from the aspects of the transduction mechanism, amplification strategies, biomaterial selection, and device fabrication procedures. Challenges and perspectives regarding future biotransducers for monitoring trace amounts of biomolecules with high fidelity, sensitivity, and multifunctionality are also discussed. It is expected that through fusion with functional electronics, wearable biosensors can provide possibilities to further decentralize the healthcare system and even build biomolecule-based intelligent cyber-physical systems and new modalities of cyborgs.

An Intelligent Blockchain-Assisted Cooperative Framework for Industry 4.0 Service Management

The shift towards Industry 4.0 has seen significant steps forward with the advancements in processing, communication, and storage capabilities of Internet of Things (IoT) devices. Cyber-physical systems (CPS) have become more intelligent and withhold advanced processing, storage, and communication capabilities. Rejuvenated network and service management architectures must incorporate the capabilities of intelligent CPS. With that said, this article introduces a cooperative blockchain (BC)-assisted resource and capability sharing approach to fulfill CPS tasks. The solution uses Federated Learning (FL)-enabled Intelligent IoT (IIoT) devices to support Next-Generation Networks (NGNs). A clustering multi-stage blockchain and FL algorithm is used to create local and global models for CPS tasks. Local models are created for each cluster during the first stage. At the second stage, Federated Averaging is used by fog devices to create fog models. A global deep model is then created on the cloud using Federated Aggregation. Blockchain is used to record and validate the added models and ensure that records are not altered under cyber-attacks. Simulation results have shown that the proposed solution outperforms conventional FL and blockchain approaches in terms of accuracy and delay tolerance.

Guest Editorial Special Issue on Security, Privacy, and Trustworthiness in Intelligent Cyber–Physical Systems and Internet of Things

Recent advances in computation, communication, and control technologies have revolutionized the way that humans, smart things, and intelligent systems interact and exchange information. Intelligent cyber–physical systems (ICPSs), characterized by the deep complex intertwining process among cyber components for computation and control with intelligent technologies and the dynamic physical components, will fuel this revolution. Examples of ICPS include intelligent automotive and transportation systems, intelligent avionics systems, smart home, smart building, smart community, smart grid, smart healthcare systems, intelligent wearable systems, intelligent energy systems, robotic systems, etc. Bringing machine/deep-learning-based intelligence techniques into CPSs can improve the performance in many aspects. However, this also imposes new security, privacy, and trust challenges, which highlights the need to develop novel methodologies to tackle these challenges. In this special issue, we will publish the following articles.

Deep Neural Network Security Collaborative Filtering Scheme for Service Recommendation in Intelligent Cyber–Physical Systems

Cyber–physical systems (CPSs) is a security real-time embedded system. CPS integrates the information sensed by the current physical sensors, through high-speed real-time transmission, and then carries out powerful information processing to effectively interact and integrate the physical and the information worlds. With the aim to improve the quality of service, optimize the existing physical space, and increase security, collaborative filtering algorithms have also been widely used in various recommendation models for Internet of Things (IoT) services. However, general collaborative filtering algorithms cannot capture complex interactive information in the sparse Mashup–Web service call matrix, which leads to lower recommendation performance. Based on the artificial intelligence technology, this study proposes a recommendation algorithm for a security collaborative filtering service that integrates content similarity. A security collaborative filtering module is used to capture the complex interaction information between Mashup and Web services. By applying the content similarity module to extract the semantic similarity information between the Mashup and Web services, the two modules are seamlessly integrated into a deep neural network to accurately and quickly predict the rating information of Mashup for the Web services. Real data set on the intelligent CPS is captured and then compared with mainstream service recommendation algorithms. Experimental results show that the proposed algorithm not only efficiently completes the Web service recommendation task under the premise of sparse data but also shows better accuracy, effectivity, and privacy. Thus, the proposed method is highly suitable for the application of intelligence CPS.

Intelligent Cyber-Physical System for Shaping the Needs of the Internet of Things

The purpose of the study. The article discusses the research and further development of modern cyber-physical systems of the Internet of Things (CPS IoT). Researchers in the field of psychology, logic, lin- guistics, philosophy, for the study of artificial intelligence, used many different models of this phenomenon of “man”. Currently, the search for new approaches continues. One of the interesting approaches lies in the development of the application of cognitive mechanisms that a person uses in solving everyday tasks. The application of methods of gestalt processing of states of the CPS IoT worldview is considered to solve an ever-expanding range of tasks. Hybrid or, as they are also called, combined models can be used for gestalt processing to solve various tasks of cyber-physical systems of the Internet of Things related to forecasting indexes of the functioning of socio-economic systems. To do this, you can use a long-known approach to hybrid- ization, the essence of which is to split the entire set of initial data into subsets of initial data. The main stages of gestalt processing and one of the initial stages – the formation of a need are shown. The significant moments of the organization of interaction of CPS IoT modules with the environment are presented. In the demo example, the CPS IoT of the estate museum is considered. The main elements of the formation of the need for CPS IoT in organizing and conducting excursions are described.Materials and methods of research. To solve problems within the framework of gestalt processing of CPS IoT, new methods and developments of specialists in the field of intelligent systems are re- quired. Hybrid, combined models can be used for gestalt processing for various tasks of cyber-physical systems of the Internet of Things related to forecasting indexes of the functioning of socio-economic systems. You can use a long-known approach to hybridization, the essence of which is to split the entire set of initial data into subsets of initial data. It is possible to use gestalt processing within the framework of a holistic approach when splitting into subsets of the initial data - clusters. The cognitive approach uses sensory images, concept-representations, concept-scenarios and gestalts. The CPS IoT gestalt can be considered as one of the approaches to solving modern problems within the framework of Industry 4.0 technology. The article describes intelligent cyber-physical systems using methods of gestalt processing of their states - a picture of the world. Approaches to the use of needs and one of the many approaches to the formation of the needs of cyber-physical systems of the Internet of Things are considered.Results. Within the framework of the cognitive approach, con- cepts-representations of CPS IoT for arising (emerging) needs and their fixation are proposed. After fixing the need, as the main stages of its formation, the following are highlighted: concretization of the event, collection of relevant initial data for the need; formation (folding of perceived information) of the gestalt: checking the state of the necessary elements, searching for resources in the environment; organization of contact with the environment. These stages of the formation of the need relate to CPS IoT of any nature and are fo- cused on any tasks of the Internet of Things. It is advisable to save scenarios for the formation of needs for possible reuse in the future. The demo example shows an example of the formation of a need for CPS IoT during an excursion.Conclusion. The article considers a cognitive approach related to the use and development of intelligent cyber-physical systems for the Internet of Things and the Internet of everything. The main stages of the formation of the need for CPS IoT are considered. The implementation of the proposed approach, the development and use of gestalt concepts will allow reflecting CPS IoT with new emergent properties.