Cyber-physical-social Systems Research Articles

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.

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 CO 2 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.

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.

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.

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.

Tensor-Based Baum–Welch Algorithms in Coupled Hidden Markov Model for Responsible Activity Prediction

The development and applications of artificial intelligence (AI) have brought unprecedented opportunities to humans, but also brought many challenges and concerns such as unfairness, immorality, distrust, illegality, and discrimination. Responsible AI provides a new solution to effectively address these AI potential threats by integrating social/physical rules into AI systems. However, these rules are high-level regulations and ethical principles, which are difficult to be formalized. To this end, we attempt to use the data generated in various AI systems such as cyber–physical–social systems (CPSS) to discover and reflect these rules to provide more responsible services for humans. In this article, we first propose a data-driven responsible CPSS framework. Its core idea is to mine valuable rules through perception, fusion, processing, and analysis of CPSS data, and then use these rules to adaptively optimize CPSS. Based on this framework, three tensor-based couple hidden Markov models (T-CHMMs) are constructed to integrate three responsible features (i.e., timing, periodicity, and correlation) for mining potential and valuable rules. Then, the corresponding tensor-based Baum–Welch (TBW) algorithms are designed to solve their learning problems. Finally, the predictive accuracy and computational efficiency of the proposed models and algorithms are verified on three open datasets. The experimental results show that proposed methods have the best performances for various scenarios, which reflects that our methods are more promising and responsible than existing methods.

Block-Chain based Trustful Data-Driven Cyber-Physical Social System Framework for Urban Air Mobility

This paper provides an overview of existing research on urban mobility issues and highlights the importance of incorporating smart technologies, particularly in the context of introducing air-based transportation, to enhance the efficiency of urban mobility systems. The integration of air mobility will bring about increased complexities in managing urban transportation due to the growing volume of data, services, and infrastructure that governing bodies will have to handle. Urban air mobility infrastructure consists of interconnected and intricate systems that demonstrate the seamless integration of physical and software components. To address these complexities, the Cyber-physical social system (CPSS) is proposed as an effective approach to minimize complications within and between these subsystems. Establishing public-private partnerships and ensuring secure transactions are vital prerequisites for embracing current technological advancements and shaping the future of urban air mobility services. In this regard, blockchain technology emerges as a reliable protocol that guarantees the integrity of data exchanged among the decentralized network of stakeholders, eliminating the need for a trusted intermediary.

Resilience of modern power distribution networks with active coordination of EVs and smart restoration

AbstractIn this modern era of cyber–physical–social systems, there is a need of dynamic coordination strategies for electric vehicles (EVs) to enhance the resilience of modern power distribution networks (MPDNs). This paper proposes a two‐stage EV coordination framework for MPDN smart restoration. The first stage is to introduce a novel proactive EV prepositioning model to optimize planning prior to a rare event, and thereby enhance the MPDN survivability in its immediate aftermath. The second stage involves creating an advanced spatial–temporal EV dispatch model to maximize the number of available EVs for discharging, thereby improving the MPDN recovery after a rare event. The proposed framework also includes an information system to further enhance MPDN resilience by effectively organizing data exchange among intelligent transportation system and smart charging system, and EV users. In addition, a novel bidirectional geographic graph is proposed to optimize travel plans, covering a large penetration of EVs and considering variations in traffic conditions. The effectiveness is assessed on a modified IEEE 123‐node test feeder with real‐world transportation and charging infrastructure. The results demonstrate a significant improvement in MPDN resilience with smart restoration strategies. The validation and sensitivity analyses evidence a significant superiority of the proposed framework.

QTT-DLSTM: A Cloud-Edge-Aided Distributed LSTM for Cyber-Physical-Social Big Data.

Cyber-physical-social systems (CPSS), an emerging cross-disciplinary research area, combines cyber-physical systems (CPS) with social networking for the purpose of providing personalized services for humans. CPSS big data, recording various aspects of human lives, should be processed to mine valuable information for CPSS services. To efficiently deal with CPSS big data, artificial intelligence (AI), an increasingly important technology, is used for CPSS data processing and analysis. Meanwhile, the rapid development of edge devices with fast processors and large memories allows local edge computing to be a powerful real-time complement to global cloud computing. Therefore, to facilitate the processing and analysis of CPSS big data from the perspective of multi-attributes, a cloud-edge-aided quantized tensor-train distributed long short-term memory (QTT-DLSTM) method is presented in this article. First, a tensor is used to represent the multi-attributes CPSS big data, which will be decomposed into the QTT form to facilitate distributed training and computing. Second, a distributed cloud-edge computing model is used to systematically process the CPSS data, including global large-scale data processing in the cloud, and local small-scale data processed at the edge. Third, a distributed computing strategy is used to improve the efficiency of training via partitioning the weight matrix and large amounts of input data in the QTT form. Finally, the performance of the proposed QTT-DLSTM method is evaluated using experiments on a public discrete manufacturing process dataset, the Li-ion battery dataset, and a public social dataset.

Semiparallel Service Systems in CPSS: Theory and Application

In this article, we study the design and innovation of smarter service systems considering the deep integration of real service systems and virtual service elements. Specifically, we consider the perspective of life-cycle service system design and innovation that must consider the “social space” factors, such as stakeholders’ behaviors, which increases the complexity of service cyber–physical–social systems (CPSSs). We propose a new methodology framework, the semiparallel service system (SPSS) framework, following the CPSS architecture and artificial societies, computational experiments, and parallel execution (ACP) methodologies emerging in complex system modeling. We design two decomposition-then-aggregation loops to reduce the complexity of service systems innovation. In the first loop, we expand the Vee model of systems engineering to the N model to show how to develop an innovative service system step by step. In the second loop, the SPSS, in which the hybrid service system is parallel with the real service system, maintains the continuous innovations of the system. Following our framework, we can design prototypes, perform experiments, and run the hybrid service system. We use two cases, an Internet hospital platform design and a new service design in a customs bonded warehouse, to demonstrate the advantages and potential of our proposed methodology. Finally, we discuss the future research challenges and directions.

Multi-objective task scheduling method for cyber–physical–social systems in fog computing

Cyber–physical–social systems refer to new systems that involve a large number of Internet of Things (IoT) devices connected through an interconnected network. These systems gather data for various services such as transportation, energy, healthcare, etc. This data is then transmitted to the cloud layer for storage, processing, and access. To handle the significant workload offloaded from resource-constrained IoT devices without latency or bandwidth issues, a technology called fog computing has emerged as an intermediary layer between the cloud and IoT devices. The quality of service (QoS) provided by fog computing relies on efficient task scheduling to optimize energy consumption and maximum execution time. This task scheduling problem is known to be NP-hard and cannot be solved with high precision in a reasonable time using traditional techniques or existing metaheuristic-based task schedulers. Therefore, this study proposes a new multi-objective task scheduling approach based on a modified marine predators algorithm (MMPA) to simultaneously minimize energy consumption and make-span under the Pareto optimality theory. This variant is named the multi-objective MMPA (M2MPA). M2MPA enhances the performance of the MPA by incorporating the polynomial crossover operator to improve its exploration operator and the adaptive CF parameter to enhance its exploitation operator. The effectiveness of M2MPA is evaluated using eighteen tasks with different scales (small, large, and medium) and heterogeneous workloads assigned to two hundred fog devices with varying processing speeds. Finally, M2MPA is compared with six well-established techniques using various performance indicators, including carbon dioxide emission rate, flowtime, make-span, and energy consumption. The experimental findings demonstrate the substantial superiority of M2MPA over all competing algorithms across different performance indicators and the Wilcoxon rank-sum test. Quantitatively, M2MPA achieves an average make-span of 10.095, average energy consumption of 3.77E+06, average flowtime of 8.39E+03, and average carbon dioxide emission rate of 8.88E+06.

A Holistic Review of Cyber-Physical-Social Systems: New Directions and Opportunities.

A Cyber-Physical-Social System (CPSS) is an evolving subset of Cyber-Physical Systems (CPS), which involve the interlinking of the cyber, physical, and social domains within a system-of-systems mindset. CPSS is in a growing state, which combines secure digital technologies with physical systems (e.g., sensors and actuators) and incorporates social aspects (e.g., human interactions and behaviors, and societal norms) to facilitate automated and secure services to end-users and organisations. This paper reviews the field of CPSS, especially in the scope of complexity theory and cyber security to determine its impact on CPS and social media's influence activities. The significance of CPSS lies in its potential to provide solutions to complex societal problems that are difficult to address through traditional approaches. With the integration of physical, social, and cyber components, CPSS can realize the full potential of IoT, big data analytics, and machine learning, leading to increased efficiency, improved sustainability and better decision making. CPSS presents exciting opportunities for innovation and advancement in multiple domains, improving the quality of life for people around the world. Research challenges to CPSS include the integration of hard and soft system components within all three domains, in addition to sociological metrics, data security, processing optimization and ethical implications. The findings of this paper note key research trends in the fields of CPSS, and recent novel contributions, followed by identified research gaps and future work.