Cyber-physical-social Systems Research Articles

Evaluating a Multidisciplinary Model for Managing Human Uncertainty in 5G Cyber–Physical–Social Systems

This paper presents a comprehensive evaluation of the previously introduced multidisciplinary model to quantify human uncertainty (MMtQHU) within a realistic 5G-enabled cyber–physical–social systems (CPSS) environment. The MMtQHU, which integrates human, social, and environmental factors into CPSS modeling, is applied to the Ingolstadt traffic scenario (InTAS), a detailed urban simulation reflecting high-traffic conditions. By modeling unpredictable driver behaviors, such as deviations from optimal routes, the study assesses the model’s effectiveness in managing human-induced uncertainties in vehicle-for-hire (VFH) applications. The evaluation shows that human uncertainty significantly impacts 5G network resource allocation and traffic dynamics. A comparative analysis of traditional resource allocation methods reveals their limitations in handling the dynamic nature of human behavior. These findings underscore the necessity for advanced, adaptive strategies, potentially leveraging artificial intelligence and machine learning to enhance the resilience and efficiency of 5G networks in CPSS environments. The study offers valuable insights for future advancements in robust and adaptive 5G infrastructure by highlighting the critical role of integrating human behavior into CPSS models.

SLACPSS: Secure Lightweight Authentication for Cyber–Physical–Social Systems

The concept of Cyber–Physical–Social Systems (CPSSs) has emerged as a response to the need to understand the interaction between Cyber–Physical Systems (CPSs) and humans. This shift from CPSs to CPSSs is primarily due to the widespread use of sensor-equipped smart devices that are closely connected to users. CPSSs have been a topic of interest for more than ten years, gaining increasing attention in recent years. The inclusion of human elements in CPS research has presented new challenges, particularly in understanding human dynamics, which adds complexity that has yet to be fully explored. CPSSs are a base class and consist of three basic components (cyberspace, physical space, and social space). We map the components of the metaverse with that of a CPSS, and we show that the metaverse is an implementation of a Cyber–Physical–Social System (CPSS). The metaverse is made up of computer systems with many elements, such as artificial intelligence, computer vision, image processing, mixed reality, augmented reality, and extended reality. It also comprises physical systems, controlled objects, and human interaction. The identification process in CPSSs suffers from weak security, and the authentication problem requires heavy computation. Therefore, we propose a new protocol for secure lightweight authentication in Cyber–Physical–Social Systems (SLACPSSs) to offer secure communication between platform servers and users as well as secure interactions between avatars. We perform a security analysis and compare the proposed protocol to the related previous ones. The analysis shows that the proposed protocol is lightweight and secure.

Parallel intelligence in three decades: a historical review and future perspective on ACP and cyber-physical-social systems

Recent advances in human-in-the-loop or human-centric research have sparked a new wave of scientific exploration. These studies have enhanced the understanding of complex social systems and contributed to more sustainable artificial intelligence (AI) ecosystems. However, the incorporation of human or social factors increases system complexity, making traditional approaches inadequate for managing these complex systems and necessitating a novel operational paradigm. Over decades of work, a mature and comprehensive theory of parallel intelligence (PI) has been established. Rooted in cyber-physical-social systems (CPSS), PI adapts flexibly to various situations within complex systems through the ACP framework (Artificial systems, Computational experiments, and Parallel execution), ensuring system reliability. This paper provides a detailed review and a novel perspective on PI, beginning with the historical and philosophical origins of CPSS and proceeding to present both the fundamental framework and technological implementations of PI. PI-based Industry 5.0 is highlighted, where three pillars are adopted to help realize the supposed vision. Additionally, the paper outlines applications of PI in multiple fields, such as transportation, healthcare, manufacturing, and agriculture, and discusses the opportunities and challenges for imaginative intelligence. The continuous exploration of PI is expected to eventually facilitate the realization of “6S”-based (safe, secure, sustainable, sensitive, service, and smart) parallel ecosystems.

A resource prediction method for air traffic cyber-physical-social system

Air traffic is exhibiting the characteristics of large flow, strong coupling, and high time variation. Therefore, the complex network of air traffic is more vulnerable to disturbances. When it is disturbed, the failure of some nodes spreads through dependency relationships in the network, resulting in cascade failure. In the event of a cascade failure, the network may quickly collapse until it is paralyzed, with widespread delays and flight cancellations. The current flow management and deployment methods still remain in the control-oriented stage, which is mainly completed by air traffic controls (ATCs), and lack of accurate flow adjustment and effective utilization of capacity. The whole air traffic system and its peripheral factors are intricate, so human and social factors must be integrated into the control and decision-making of the system. Considering engineering and social factors such as operation environment, social environment, personnel, rules, equipment, and information processing, we analyse the air traffic in a cyber-physical-social system (CPSS). To reflect the actual system behaviour rules, dynamic response, limit state, and so on, the corresponding computational experiment and comprehensive evaluation system are established. Based on neural networks and other technologies, a resource prediction scheme based on task demand is proposed for multi-dimensional resources such as airports, air routes, and ATC, to reduce the cost of system resource scheduling and improve resource utilization through resource prediction and adjustment. Finally, the accuracy of the proposed resource prediction algorithm is verified by theoretical analysis and simulation.

The Future of Process Industry: A Cyber-Physical-Social System Perspective.

The process industry is an industrial field of interdisciplinary nature involving electrical engineering, energy, petroleum, chemical, and metallurgy, which play a key role in the sustainable development. As a main source of CO2 emissions, the process industry will undertake a large part of the emission reduction task. In order to incorporate the impact of social factors, such as environment, society, and human to support the future process industry, the cyber-physical-social system (CPSS) framework should be considered as a promising way to enhance the transformation of the process industry. The development of CPSS technologies will fundamentally change the infrastructure of conventional industrial systems, offering a great opportunity for the greenization, high-value, and digitalization in the process industry. This article first presents the current status of the process industry. Through a CPSS framework, the current developments of the process industry as well as the main challenges and opportunities are discussed. A vision for the future process industry based on CPSS is described by focusing on three aspects, namely, the greenization and low carbon, high-value and high-end, digitalization, and intellectualization in process manufacturing. Finally, the advanced technologies and approaches in CPSS driven by artificial intelligence and industrial digitalization, which are important in achieving the sustainable development of the process industry, are outlined. The development of the comprehensive digital technologies, such as virtual reality, digital twin, blockchain, and big data, will stimulate the implementation of a ground-breaking concept formed in the CPSS framework called industrial metaverse.

A Cyber-Physical–Social System Approach for Peer-to-Peer Energy Sharing

A Cyber-Physical–Social System Approach for Peer-to-Peer Energy Sharing

Parameter Identification and Refinement for Parallel PCB Inspection in Cyber–Physical–Social Systems

Parameter Identification and Refinement for Parallel PCB Inspection in Cyber–Physical–Social Systems

CSCT: Charging Scheduling for Maximizing Coverage of Targets in WRSNs

In recent years, wireless rechargeable sensor networks (WRSNs), as a crucial technology in cyber-physical-social systems (CPSSs), have gradually become a hotspot of research, with the development of wireless energy transmission technology. In previous works, the objective is to maximize the survival rate of sensor nodes. However, in this article, we focus on maintaining more targets. First, it details the charging scheduling problem of maximizing coverage of targets (CoT) in on-demand charging architecture of WRSNs. Also, the problem is formalized as a multiple-objective optimization problem, which aims at maximizing the CoT and the energy efficiency simultaneously. After that, the charging scheduling for maximizing coverage of targets (CSCT) scheme is proposed to achieve the above objectives. Then, the problem is reformulated as a Deadline-TSP problem that is NP-hard. To address this problem, we design an energy predictive model and propose the CSCT with an n-path (n-CSCT) scheme that has an O(|N|ⁿ) computational complexity. In addition, the resurrection of sensor nodes is considered in this article. Thus, the n-CSCT with node resurrection (n-CSCT-R) scheme is proposed for this case. Finally, we validate the effectiveness of the proposed schemes via extensive simulations.

Driver emotion recognition based on attentional convolutional network

Unstable emotions, particularly anger, have been identified as significant contributors to traffic accidents. To address this issue, driver emotion recognition emerges as a promising solution within the realm of cyber-physical-social systems (CPSS). In this paper, we introduce SVGG, an emotion recognition model that leverages the attention mechanism. We validate our approach through comprehensive experiments on two distinct datasets, assessing the model’s performance using a range of evaluation metrics. The results suggest that the proposed model exhibits improved performance across both datasets.

Fabry-perot interferometers with resin scaffolders for high sensitivity temperature sensing

This study explores the development of an innovative Fabry-Perot Interferometer (FPI) designed for temperature sensing and environmental monitoring. The device is constructed by embedding optical fibers within a 3D-printed resin scaffold, forming a structure with an open Fabry-Perot cavity. Intended as an integral component of Cyber-Physical-Social Systems (CPSS), this FPI structure aims to enhance the system’s capacity to sense changes in external environmental conditions. Within the CPSS context, the FPI offers several advantages, including simple manufacturing processes, low production costs, and high sensitivity. These benefits contribute to providing precise environmental feedback to the system, which is essential in implementing effective security and privacy protection strategies. Experimental evaluations have shown that the FPI exhibits a high linear sensitivity of 14.330 nm/°C within a temperature range of 34.9°C–38.5°C, confirming its potential for application in CPSS for temperature monitoring and environmental sensing.

Computational Experiments for Complex Social Systems—Part III: The Docking of Domain Models

Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber–physical–social systems (CPSS). In consideration of the cost, legal, and institutional constraints on the study of CPSS in real world, computational experiments have emerged as a new method for quantitative analysis of CPSS. However, with the increase of application scenarios, how to map complex and diverse domain models to artificial society models has become a key challenge to hinder the wide use of computational experiments. In this article, the docking framework between the domain model and the artificial society model was proposed in this article, and the model docking specification is given from three aspects: the agent model, the environmental model, and the rules model. In addition, the effectiveness of the framework was verified by two classic cases: artificial stock market and epidemic prevention and control. The result showed that the proposed model docking framework can provide technical support for the multidisciplinary applications of computational experiments and significantly reduce the difficulty of using the method.

Mobility and energy efficient services composition algorithm with QoS guarantee for large scale Cyber–Physical–Social Systems

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user’s movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user’s mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user’s mobility traces, avoiding the random generation of users’ traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices’ batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider.

Comparing the Complexity and Efficiency of Composable Modeling Techniques for Multi-Scale and Multi-Domain Complex System Modeling and Simulation Applications: A Probabilistic Analysis

Modeling and simulation of complex systems frequently requires capturing probabilistic dynamics across multiple scales and/or multiple domains. Cyber–physical, cyber–social, socio–technical, and cyber–physical–social systems are common examples. Modeling and simulating such systems via a single, all-encompassing model is often infeasible, and thus composable modeling techniques are sought. Co-simulation and closure modeling are two prevalent composable modeling techniques that divide a multi-scale/multi-domain system into sub-systems, use smaller component models to capture each sub-system, and coordinate data transfer between component models. While the two techniques have similar goals, differences in their methods lead to differences in the complexity and computational efficiency of a simulation model built using one technique or the other. This paper presents a probabilistic analysis of the complexity and computational efficiency of these two composable modeling techniques for multi-scale/multi-domain complex system modeling and simulation applications. The aim is twofold: to promote awareness of these two composable modeling approaches and to facilitate complex system model design by identifying circumstances that are amenable to either approach.

Dimensionality reduction and machine learning based model of software cost estimation

Software Cost Estimation (SCE) is one of the research priorities and challenges in the construction of cyber-physical-social systems (CPSSs). In CPSS, it is urge to process environmental and social information accurately and use it to guide social practice. Thus, in response to the problems of low prediction accuracy, poor robustness, and poor interpretability in SCE, this paper proposes a SCE model based on Autoencoder and Random Forest. First, preprocess the project data, remove outliers, and build regression trees to fill in missing attributes in the data. Second, construct a Autoencoder to reduce the dimensionality of factors that affect software cost. Subsequently, the performance of the model was trained and validated using the XGBoost framework on three datasets: COCOMO81, Albrecht, and Desharnais, and compared with common cost prediction models. The experimental results show that the MMRE, MdMRE, and PRED (0.25) values of the proposed model on the COCOMO81 dataset reached 0.21, 0.16, and 0.71, respectively. Compared with other models, the proposed model achieved significant improvements in accuracy and robustness.

A Cyber-physical-social systems approach to the semantic segmentation of pulmonary embolism

Cyber-Physical-Social Systems (CPSS) epitomize the modern era’s intelligent connectivity. They integrate physical devices, computer networks, and social networks, forming an innovative paradigm for intelligent systems. Utilizing CPSS to enhance intelligence, automation, and remote services in healthcare represents a primary research focus. Pulmonary embolism, a prevalent condition resulting from the blockage of the pulmonary artery and its branches by emboli, leads to a spectrum of clinical syndromes marked by impaired pulmonary circulation and right heart dysfunction, contributing to sudden and unpredictable fatalities. Nevertheless, the diagnosis of pulmonary embolism remains challenging due to non-specific clinical presentations, constrained diagnostic capabilities, delayed diagnoses, insufficient physician knowledge, and suboptimal diagnostic techniques. Consequently, we introduce the innovative LSCU-Net architecture within the CPSS framework, designed to develop an automated segmentation and intelligent assessment system for pulmonary embolism, facilitating its automated and intelligent detection. The experimental findings demonstrate that the model accurately segments pulmonary embolism, evidenced by a Jaccard index of 0.6958, a Dice coefficient of 0.8193, a Mean Pixel Accuracy (mPA) of 0.8519, and an accuracy of 0.9993. Empirical studies reveal that our proposed model substantially surpasses existing models in performance. Consequently, this model can aid physicians in the diagnosis of pulmonary embolism during clinical practice. The established pulmonary embolism automatic segmentation and assessment system also showcases the application successes of CPSS in intelligent remote healthcare. The system’s development and deployment not only streamline physicians’ diagnostic processes but also elevate public health standards and advance CPSS research within the medical domain.

A New Perspective for Computational Social Systems: Fuzzy Modeling and Reasoning for Social Computing in CPSS

The evolution of modern mobile terminals, social networks, and other intelligent services makes everyone become a ubiquitous information perceiver, producer, and propagator. Also known as “social sensor” and “social IoT”, these individuals and communities generate a huge volume of social signals, which has shown prominent value for mining. These unstructured social signals provide a new perspective in the research of complex systems, which makes the traditional cyber–physical system (CPS)-oriented information computing sublimate to the cyber–physical–social system (CPSS)-oriented knowledge computing. However, there still exist great uncertainties, ambiguities, and complexities in modeling behaviors of social individuals or groups. Especially when we apply big-data-driven learning-based models in specific fields and scenarios, the lack of domain expert knowledge and characteristics of system uncertainty severely limits the performance and accuracy of these models. The introduction of fuzzy system modeling integrates data and knowledge in the social computing area, which has shown its unique advantages in solving the above issues and has drawn more attention to this topic. In this article, we conduct a review of recent advances in social computing with fuzzy technologies in CPSS. First, we briefly review the development of social computing, and analyze the characteristics and advantages of social computing through fuzzy methods. Second, we refine core fuzzy system methods for social computing and elaborate on existing fuzzy-technology-empowered social computing methodologies. As in a range of social spaces, we also review and analyze related advances in human-in-the-loop systems. We also reveal the trend of decentralized, autonomous, and organized computing in cyber–physical–social space with fuzzy-based methods and proposed a framework to categorize related studies in CPSS. Finally, we conclude the research trends and hotspots based on current studies, and discuss the challenges for future research directions.

Tensor-Based Viterbi Algorithms for Collaborative Cloud-Edge Cyber-Physical-Social Activity Prediction

With the rapid development and application of smart city, Cyber-Physical-Social Systems (CPSS) as its superset is becoming increasingly important, and attracts extensive attentions. For satisfying the smart requirements of CPSS design, a cloud-edge collaborative CPSS framework is first proposed in this paper. Then Coupled-Hidden-Markov-Model (CHMM) and tensor algebra are used to improve existing activity prediction methods for providing CPSS with more intelligent decision support. There are three key features (timing, periodicity and correlation) implied in CPSS data from multi-edge, which affects the accuracy of activity prediction. Thus, these features are synthetically integrated into improved Tensor-based CHMMs (T-CHMMs) to enhance the prediction accuracy. Based on the multi-edge CPSS data, three Tensor-based Viterbi Algorithms (TVA) are correspondingly proposed to solve the prediction problem for T-CHMMs. Compared with traditional matrix-based methods, the proposed TVA could more accurately compute the optimal hidden state sequences under given observation sequences. Finally, the comprehensive performances of proposed models and algorithms are validated on three open datasets by self-comparison and other-comparison. The experimental results show that the proposed methods is superior to the compared three classical methods in terms of F1 measure, average precision and average recall.

MissII: Missing Information Imputation for Traffic Data

Cyber-Physical-Social Systems (CPSS) offer a new perspective for applying advanced information technology to improve urban transportation. However, real-world traffic datasets collected from sensing devices like loop sensors often contain corrupted or missing values. The incompleteness of traffic data poses great challenges to downstream data analysis tasks and applications. Most existing data-driven methods only impute missing values based on observed data or hypothetical models, thus ignoring the incorporation of social world information into traffic data imputation. The connection between real-world social activities and CPSS is crucial. In this paper, a novel theory-guided traffic data imputation framework, namely MissII, is proposed. In MissII, we first estimate the traffic flow between two PoIs (Points of Interest) according to spatial interaction theory by considering the physical environment information (e.g., population distributions) and human social interactions (e.g., destination choice game). Moreover, we further refine the estimated traffic flow by considering the effects of road interactions and PoIs. Then, the estimated traffic flow is input into the non-parametric GAN model as real samples to guide the training process. Extensive experiments are conducted on real-world traffic dataset to demonstrate the effectiveness of the proposed framework.

A Double-Direction Cognitive Interaction Security Architecture for CPSS: A Case Study

With the development of big data analytics, new generation artificial intelligence, and other technologies, Cyber-Physical-Social Systems (CPSSs) have gradually come into people’s view. However, the complex integration brings about more complex security issues, especially the risks and problems in double-directional cognition triggered by the interactive integration of humans and Artificial Intelligence (AI). Therefore, based on the security analysis architecture and methodology for CPSSs, this paper carries out a case study with the help of the power operation and maintenance (O&M) system, which is a typical CPSS, to verify the validity of the relevant architecture and methodology and to propose reasonable security protection measures.

A cyber-physical social system for autonomous drone trajectory planning in last-mile superchilling delivery

Logistics drones are theoretically advantageous in automating last-mile delivery activities, but practically challenging in urban environments, including public concerns about risk of crashes and privacy. In view of the above concerns existing in the trajectory planning of logistics drones, this paper presents a parallel automatic delivery model following the cyber-physical social system (CPSS) for the last mile delivery of superchilling products, revealing the social value of logistics drone operations. Considering the operational constraints of logistics drone, a multi-objective optimisation model is established to balance the social value, energy efficiency and productivity of using logistics drones in the last-mile superchilling delivery. To effectively achieve the above optimisation, improved strategies, including the material exchange mechanism based on the random proportion rule and the Universe-Particle search strategy, are developed, resulting in an improved two-stage heuristic algorithm. Finally, simulation experiments are carried out in a simulated environment with 154 buildings, and compared with other frontier algorithms used for unmanned aerial vehicle (UAV) trajectory planning. The results show that the proposed framework can effectively reduce the threats to public life safety and improve the energy efficiency of logistics drones, while ensuring the productivity in the delivery process.