Cyber-physical-social Systems Research Articles

Fostering Cyber-Physical Social Systems through an Ontological Approach to Personality Classification Based on Social Media Posts.

The exponential increase in social networks has led to emergent convergence of cyber-physical systems (CPS) and social computing, accelerating the creation of smart communities and smart organizations and enabling the concept of cyber-physical social systems. Social media platforms have made a significant contribution to what we call human behavior modeling. This paper presents a novel approach to developing a users’ segmentation tool for the Romanian language, based on the four DISC personality types, based on social media statement analysis. We propose and design the ontological modeling approach of the specific vocabulary for each personality and its mapping with text from posts on social networks. This research proposal adds significant value both in terms of scientific and technological contributions (by developing semantic technologies and tools), as well as in terms of business, social and economic impact (by supporting the investigation of smart communities in the context of cyber-physical social systems). For the validation of the model developed we used a dataset of almost 2000 posts retrieved from 10 social medial accounts (Facebook and Twitter) and we have obtained an accuracy of over 90% in identifying the personality profile of the users.

A survey on data center cooling systems: Technology, power consumption modeling and control strategy optimization

Data center is a fundamental infrastructure of computers and networking equipment to collect, store, process, and distribute huge amounts of data for a variety of applications such as Cyber–Physical–Social Systems, business enterprises and social networking. As the demands of remote data services keep increasing, both the workload of the data center and its power consumption are rapidly rising. An indispensable part of a data center is the cooling system which provides a suitable operation environment, and accounts for around 30% of the power consumption of the data center. Therefore, optimized energy management of data center’s cooling system is a highly profitable research area. Generally, a cooling system is made up of a mechanical refrigeration sub-system and a terminal cooling sub-system. Heat generated during operation of the data center will be absorbed by the latter one, and transferred into the outdoor environment via the former one. Depending on the cooling principle, current cooling solutions can be classified into air-cooling, liquid-cooling or free cooling technology. Although air-cooling is widely used in most existing data centers, the other two solutions have attracted more interests due to their excellent cooling effectiveness and higher energy efficiencies. Among the different cooling equipment, the chillers and fans are the major power consumers of the entire cooling system. Therefore, modeling of their power consumption is important for energy management of the cooling system, which can be classified into mechanism-based methods and data-driven methods. Based on the aforementioned models, optimization strategies for the operation management of cooling equipment are proposed to reduce the power consumption of the cooling system, which mainly includes the model predictive control-based methods and reinforcement learning-based methods. This paper is an overview of the data center’s cooling system, which mainly includes the mainstream cooling solutions, the power consumption modeling methods and the optimization control strategies. In addition, several current challenges and future work in the data center’s cooling system are described.

A QoS-guaranteed online user data deployment method in edge cloud computing environment

App vendors now have a new way to deploy applications on edge servers thanks to the rise of edge cloud computing. App vendors will be able to deliver better services to users throughout this form. Compared to the traditional centralized system, the new distributed computing paradigm constructs a novel cyber–physical–social system. However, how to design a data deployment method to meet the requirement of the CPS system is a challenge. A QoS-guaranteed edge user data deployment method needs to maximize the reduction in service latency and minimize the overall system cost (SC) with available resources. The EDD (edge data deployment) problem is formulated as an online problem and we propose a convinced method to solving this problem. We analyze the performance of our approach theoretically and conduct experiments to compare the proposed method to three different baseline methods with a real-word dataset. Our method’s efficiency and efficacy are shown by the experimental results.

An Approach for Chart Description Generation in Cyber–Physical–Social System

There is an increasing use of charts generated by the social interaction environment in manufacturing enterprise applications. To transform these massive amounts of unstructured chart data into decision support knowledge for demand-capability matching in manufacturing enterprises, we propose a manufacturing enterprise chart description generation (MECDG) method, which is a two-phase automated solution: (1) extracting chart data based on optical character recognition and deep learning method; (2) generating chart description according to user input based on natural language generation method and matching the description with extracted chart data. We verified and compared the processing at each phase of the method, and at the same time applied the method to the interactive platform of the manufacturing enterprise. The ultimate goal of this paper is to promote the knowledge extraction and scientific analysis of chart data in the context of manufacturing enterprises, so as to improve the analysis and decision-making capabilities of enterprises.

Topology-informed information dynamics modeling in cyber–physical–social system networks

AbstractCyber–physical–social systems (CPSS) are physical devices that are embedded in human society and possess highly integrated functionalities of sensing, computing, communication, and control. CPSS rely on their intense collaboration and information sharing through networks to be functioning. In this paper, topology-informed network information dynamics models are proposed to characterize the evolution of information processing capabilities of CPSS nodes in networks. The models are based on a mesoscale probabilistic graph model, where the sensing and computing capabilities of the nodes are captured as the probabilities of correct predictions. A topology-informed vector autoregression model and a latent variable vector autoregression model are proposed to model the correlations between prediction capabilities of nodes as linear functional relationships. A hybrid Gaussian process regression model is also developed to capture both the nonlinear spatial and temporal correlations between nodes. The new information dynamics models are demonstrated and tested with a simulator of CPSS networks. The results show that the topological information of networks can improve the efficiency in constructing the time series models. The network topology also has influences on the prediction capabilities of CPSS.

The Cyber-Physical-Social Transition Tensor Service Framework

Cyber-Physical-Social Systems (CPSS) are the extension of Cyber-Physical Systems (CPS) which integrate social characteristics into Cyber-Physical Systems. The emergence of CPSS provides a promising solution for energy and sustainability challenges, at the same time, CPSS make sustainability computing space change from single space to multi-spaces. To cope with the problem, we propose Cyber-Physical-Social Transition Tensor service framework to provide sustainable services for multi-spaces. To the best of our knowledge, this is the first framework that can support multi-space services at one time. The framework solves various stationary probability distributions arising from the multi-relational data from CPSS, then provides services for multi-spaces based on the probability distributions. In order to solve the stationary probability distributions, we develop a novel iterative algorithm, named Intersect Tensor Power Method (ITPM). Furthermore, we prove the convergence of the iterative algorithm, the existence and uniqueness of the stationary probability distribution. This paper's highlight is the Cyber-Physical-Social Transition Tensor (CPST <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) model which leverages multivariate transition tensor to seamlessly integrate massive data from CPSS. A case study of multi-space prediction service illustrates our framework, the accuracy reaches 95 percent with three kinds of humans, two hundred and fifty traffic flow slices, and nineteen time slices.

Improved Multi-Order Distributed HOSVD with Its Incremental Computing for Smart City Services

Smart city, a focus of many researchers from academia and industry, is a successful example of Cyber-Physical-Social Systems (CPSS). Based on the rapid and efficient processing of large-scale data, Smart city, an example of CPSS, has revolutionized the service provision model by providing proactive services for humans. However, to operationalize the services provided in smart cities, a comprehensive analysis of heterogeneous and large-scale big data is required. Further, to speed up data processing and improve the adaptability and extensibility of big data, CPSS big data processing should be realized in the form of blocks and avoid redundant computing on historical data. In this paper, as an extension of multi-order distributed and incremental High-Order Singular Value Decomposition (HOSVD) computing, Ring-based Tree algorithm and Tree-based Tree algorithm are proposed for the problems of increasing scale of processable data and computational efficiency. The experimental and simulation results demonstrate that the proposed algorithms have high performance in terms of error, improvement factor, and improvement factor ratio. At last, to demonstrate the performance of our improved algorithms, a case study about CPSS big data processing is provided.

Blockchain-enabled Tensor-based Conditional Deep Convolutional GAN for Cyber-physical-Social Systems

Deep learning techniques have shown significant success in cyber-physical-social systems (CPSS). As an instance of deep learning models, generative adversarial nets (GAN) model enables powerful and flexible image augmentation, image generation, and classification, thus can be applied to real-world CPSS settings. GAN model training needs a large collection of cyber-physical-social data originating from various CPSS devices. Numerous prevailing GAN models depend on a tacit assumption that several cyber-physical-social data providers present a reliable source to collect training data, which is seldom the case in real CPSS. The existing GAN models also fail to consider multi-dimensional latent structure. In our work, we put forward a novel blockchain-enabled tensor-based conditional deep convolutional GAN (TCDC-GAN) model for cyber-physical-social systems. The blockchain is employed to develop a decentralized and reliable cyber-physical-social data-sharing platform between numerous cyber-physical-social data providers, such that the training data and the model are documented on a ledger that is distributed. Furthermore, a tensor-based generator and a tensor-based discriminator are well designed by employing the tensor model. The results of extensive simulation experiments show the efficacy of the proposed TCDC-GAN model. Compared with the state-of-the-art models, our model gains superior estimation performance.

‘Un’-blocking the industry 4.0 value chain with cyber-physical social thinking

ABSTRACT Integration of diverse technologies fuels business-model transformation, evolving new applications. Industry 4.0 utilizes emerging-technologies like IoT, AI to make next-generation manufacturing-processes more efficient and agile. Cyber physical systems (CPS) augmented with social, thinking technologies effectuate cyber physical social thinking systems (CPST). Distributed Ledger Technologies (DLT) like Blockchain are synergistic with architectural-elements of such industry-blueprints. Extant literature explores contextual-roles of distinct technology-components as-well-as their limited-combinations; clearly necessitating consideration of integrated-applications. This contemporary conceptual paper: (i) concisely analyses potential-synergies amongst Blockchain, CPST technologies; (ii) proposes novel integrated architecture; (iii) outlines underlying challenges, opportunities and performance considerations; bearing benefits beyond traditional-ecosystems to societal-stakeholders.

A tensor-network-based big data fusion framework for Cyber–Physical–Social Systems (CPSS)

Big data has been continuously generated from the rapidly developing of cloud/fog/edge computing, Internet of Things (IoT) and 5G technology. This big data not only brings great benefits and opportunities to human beings but also brings many risks and challenges. One major challenge is how to represent and treat higher-order and heterogeneous data from multi-sources. Tensors are emerging as powerful tools for representation and modeling of this data. Tensor decomposition can be used to extract potentially useful information from this data. Thus, it has attracted much attention from the big data community. The main target of this paper is to propose a novel data fusion framework to solve several main challenges of CPSS data applications, including CPSS big data representation, fusion, efficient computing, storage, robustness, and security issues. In this paper, we use many graphics to visualize complex tensor decomposition and transformation processes. The visualization may help the readers better understand tensor and tensor decomposition. It also provides a general guideline and a good starting point for those who are interested in tensor and tensor decomposition. Specifically, we first introduce the most extensively used matrix and tensor decomposition methods. Second, the current popular data fusion methods are reviewed and summarized. Third, we propose a novel tensor-network-conversion-based data fusion approach which can simultaneously analyze multiple matrices and multiple tensors. To better understand this approach, we give a brief review of tensor network. Fourth, based on the proposed approach, a novel CPSS big data fusion framework is proposed in this paper. Meanwhile, we verify it by a concise case study. Finally, some challenges and open problems of the proposed framework are discussed. The discussion also includes some exciting future research directions in the big data fusion field.

Memory-efficient deep learning inference with incremental weight loading and data layout reorganization on edge systems

Pattern recognition applications such as face recognition and agricultural product detection have drawn a rapid interest on Cyber–Physical–Social-Systems (CPSS). These CPSS applications rely on the deep neural networks (DNN) to conduct the image classification. However, traditional DNN inference models in the cloud could suffer from network delay fluctuations and privacy leakage problems. In this regard, current real-time CPSS applications are preferred to be deployed on edge-end embedded devices. Constrained by the computing power and memory limitations of edge devices, improving the memory management efficacy is the key to improving the quality of service for model inference. First, this study explored the incremental loading strategy of model weights for the model inference. Second, the memory space at runtime is optimized through data layout reorganization from the spatial dimension. In particular, the proposed schemes are orthogonal to existing models. Experimental results demonstrate that the proposed approach reduced the memory consumption by 61.05% without additional inference time overhead.

A Many-Objective Optimization Based Intelligent High Performance Data Processing Model for Cyber-Physical-Social Systems

As an emerging paradigm, Cyber-Physical Social System (CPSS) can provide humans with efficient, convenient and personalized services. However, due to the explosive growth of user data, massive and high-dimensional data has become a key issue for CPSS services. In the past, researchers mainly focused on big data analysis methods, ignoring the processing of high-dimensional data before analysis. In response to above problem, this paper focus on the redundant attributes in high-dimensional data. By using intelligent optimization technology, the vision model is optimized for four conflicting goals at the same time, so as to better solve the problem. In order to support this model, a collaboration mechanism (MaOEA-CM) was designed. The algorithm uses the combination of sum of objectives and shift-based density estimation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${I}_{SDE} {+}$</tex-math></inline-formula> ) indicator as the criterion of gradual co-evolution, and guides individuals to search in the high convergence and multiple directions of CPSS. Experiments on four data sets show that the proposed algorithm and model can significantly improve the accuracy of data processing in CPSS and have higher performance than other feature selection models.

Model design as a new socio-cyber-physical systems creating mechanism

The purpose of this work is to consider the methodology of designing intellectual models of cyber-physical social systems that take into account the cybernetic properties of the hardware, software and the human factor specifics of the system developers and users. To ensure customizable solutions, model design process is based on systems analysis. This methodology can be implemented by optimizing the objective tree, which determines the development vector of the model being designed based on their importance; the information tree, which determines the structure of the knowledge base optimized by the cost of resources used; the task tree, which allows optimizing the development of the cyber-physical social systems based on the results of analyzing the criteria of the two trees. Intellectual properties of the model being designed are determined by automating the management of its development vector in accordance with the dynamics of the utility of its specific goals. Due to practical limitations on the amount of resources available, the task is to optimize the plan for creating a cyber-physical social system that maximizes the solution value with limited cost. The choice between more precise algorithms that are not applicable given fuzzy limitations caused by human factor allows solving the problems faster than when using deterministic or heuristic methods.

Differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems: State-of-the-art and perspectives

The modern technological advancement influences the growth of the cyber–physical system and cyber–social system to a more advanced computing system cyber–physical–social system (CPSS). Therefore, CPSS leads the data science revolution by promoting tri-space information resource from a single space. The establishment of CPSSs increases the related privacy concerns. To provide privacy on CPSSs data, various privacy-preserving schemes have been introduced in the recent past. However, technological advancement in CPSSs requires the modifications of previous techniques to suit its dynamics. Meanwhile, differential privacy has emerged as an effective method to safeguard CPSSs data privacy. To completely comprehend the state-of-the-art developments and learn the field’s research directions, this article provides a comprehensive review of differentially private data fusion and deep learning in CPSSs. Additionally, we present a novel differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems , and various future research directions for CPSSs.

C2QoS: Network QoS guarantee in vSwitch through CPU-cycle management

Cyber–Physical–Social–System (CPSS) relies on cloud and edge computing for service deployment and acceleration. As an enabling technology of CPSS, network virtualization allows different services to be deployed on a single physical server and provides them with differentiated network performance based on service-level-agreement (SLA). Unfortunately, current virtual switches (vSwitches) cannot guarantee this functionality due to the common resource competition and unpredictable processing capacity. As a software process on the server, vSwitch is allocated limited dedicated CPU cores to implement traffic forwarding for all services. But the QoS strategy inheriting from the hardware switch, does not consider the forwarding tasks’ competition for CPU cores in terms of utilization and timing, thus cannot guarantee the SLA. To solve this critical issue, we propose a CPU-cycle based QoS (C2QoS) strategy to realize the isolation and guarantee of service network performance by managing the usage and scheduling of the IO-dedicated CPU cores in vSwitch. C2QoS includes a CPU-cycle based token bucket mechanism to strictly limit service’s network bandwidth and a hierarchical batch scheduling mechanism to achieve hierarchical latency. Experimental results show that, compared with existing strategies, C2QoS can strictly guarantee the network bandwidth of the services and reduce the service latency by up to 80%.

Probabilistic Modeling of Information Dynamics in Networked Cyber–Physical–Social Systems

Cyber–physical–social systems (CPSS) are physical devices with highly integrated functions of sensing, computing, communication, and control, and are seamlessly embedded in human society. The levels of intelligence and functions that CPSS can perform rely on their extensive collaboration and information sharing through networks. In this article, information diffusion within CPSS networks is studied. Information dynamic models are proposed to characterize the evolution of information processing and decision-making capabilities of individual CPSS nodes. The data-driven statistical models are based on a mesoscale probabilistic graph model, where the individual nodes’ sensing and computing functions are represented as the probabilities of correct predictions; whereas the communication functions are represented as the probabilities of mutual influences between nodes. A copula dynamics model is proposed to explicitly capture the information dependency among individuals with joint prediction probabilities and estimated from extremal probabilities. A topology-informed vector autoregression model is proposed to represent the mutual influence of prediction capabilities. A spatial–temporal hybrid Gaussian process regression model is also proposed to simultaneously capture correlations between nodes and temporal correlation in the time series.

Systemic formalisation of Cyber-Physical-Social System (CPSS): A systematic literature review

The notion of Cyber-Physical-Social System (CPSS) is an emerging concept developed as a result of the need to understand the impact of Cyber-Physical Systems (CPS) on humans and vice versa. This paradigm shift from CPS to CPSS was mainly attributed to the increasing use of sensor enabled smart devices and the tight link with the users. The concept of CPSS has been around for over a decade and it has gained an increasing attention over the past few years. The evolution to incorporate human aspects in the CPS research has unlocked a number of research challenges. Particularly human dynamics brings additional complexity that is yet to be explored. The exploration to conceptualise the notion of CPSS has been partially addressed in few scientific literatures. Although its conceptualisation has always been use-case dependent. Thus, there is a lack of generic view as most works focus on specific domains. Furthermore the systemic core and design principles linking it with the theory of systems are loose. This work aims at addressing these issues by first exploring and analysing scientific literatures to understand the complete spectrum of CPSS through a Systematic Literature Review (SLR). Thereby identifying the state-of-the-art perspectives on CPSS regarding definitions, underlining principles and application areas. Subsequently, based on the findings of the SLR, we propose a domain-independent definition and a meta-model for CPSS, grounded in the Theory of Systems. Finally a discussion on feasible future research directions is presented based on the systemic notion and the proposed meta-models.

A deep residual computation model for heterogeneous data learning in smart Internet of Things

Smart Internet of Things (smart IoT) have emerged as a transformative computing paradigm recently. This new approach has made great contributions in the area of cyber–physical–social systems by employing various computational intelligence techniques like deep learning, for analyzing data, especially heterogeneous data from sensing and wireless communication. As a representative example of deep learning, deep residual networks have achieved excellent performance for big data feature learning since they can avoid gradient vanishing issues in deep learning models effectively. Unfortunately, they could not learn features for heterogeneous data, especially multi-modal data, in smart IoT. This paper proposes a deep residual computation model by generalizing the deep residual network in the tensor space. Especially, each multi-modal data object is represented as a tensor, while all hidden layers are also represented as tensors. Furthermore, we propose a tensor back-propagation algorithm to train the parameters of the deep residual computation model. Finally, we conduct extensive experiments to evaluate the presented deep residual model by comparing with the existing models such as multi-modal deep learning models, 3D deep residual models, deep computation models, and deep convolutional computation models. Results show that the proposed model produces more accurate classification results than other models for heterogeneous data feature learning in cyber–physical–social systems.

Artificial Identification, Blockchain, Cyberphysical Social Systems, Digital Twins, and Parallel Intelligence: Opportunities and Synergies Between the IEEE Council on Radio-Frequency Identification and Systems, Man, and Cybernetics Society [Essay

This article describes several opportunities and synergies between the IEEE Council on Radio-Frequency Identification (CRFID) and IEEE Systems, Man, and Cybernetics Society (SMCS) to initiate a roadmap study and working plan for a new model of support and collaboration among IEEE Societies and Councils in the future. We hope this will stimulate more communication and discussion for deep and effective coordination and collaboration among IEEE Councils and Societies.

Commercial hypervisor-based task sandboxing mechanisms are unsecured? But we can fix it!

Cyber–Physical–Social Systems are frequently prescribed for providing valuable information on personalized services. The foundation of these services is big data which must be trustily collected and efficiently processed. Though High Performance Computing and Communication technique makes great contributions to addressing the issue of data processing, its effectiveness still relies on the veracity of data generated from Internet of Things (IoT) devices. Nevertheless, IoT devices, as basic production facilities to ensure data’s security, are unable to deploy expensive security extensions. Consequently, it causes the implementation of the task sandboxing, the fundamental security mechanism in Real-Time Operating Systems (RTOSs), much simpler and more vulnerable. In this paper, we take ARM Mbed uVisor as an example system, utilizing hypervisor-based task sandboxing mechanisms, and presents three new findings: First, we discover vulnerabilities against Mbed task sandboxing, which can be exploited to compromise system-maintained data structure to manipulate any tasks’ data. Second, we present LIPS (Lightweight Intra-Mode Privilege Separation), building a special protection domain to isolate particular system-maintained data structures. Finally, thorough evaluation and experimental tests show the efficiency of LIPS to defeat these attacks, with small runtime overheads and good portability.