Multi-dimensional Complex System Research Articles

Efficient data-driven event-triggered predictive control for cyber-physical systems

As a multi-dimensional complex system that integrates computing, network, and physical environments, cyber-physical system (CPS) involves a large amount of system data in its real-time control process. The efficiency of data processing and transmission in CPS plays an important role in its control performance, however, few results have addressed such problems. This paper proposes a novel data-driven event-trigger mechanism to alleviate the data processing and transmission load for the data-driven predictive control method in CPS. The design of the event-triggered law can be transformed into solving a linear matrix inequality (LMI) based on the measured system data, while ensuring the stability of the integrated system. The improved performances are illustrated by the explicit simulation results, which indicates its potential application in CPS.

Provably secure and lightweight three-factor authentication scheme for industrial medical CPS

Cyber-Physical System (CPS) is a multidimensional complex system that integrates computing, network, and physical environment, which is widely used to promote the upgrading of industrial production and technology. Recently, the application of industrial CPS in the medical field has attracted the attention of scholars and medical experts. Medical CPS can establish a perfect medical network to help doctors monitor patients’ conditions in real-time and make treatments. However, how to design a provably secure and lightweight authentication protocol for industrial medical CPS is a challenge. Very recently, Qi et al. proposed an authentication protocol for industrial medical CPS based on the chaotic map, the Artificial Intelligence (AI) biometric technique is used in the protocol to resist password guessing attack and smart card lost attack. However, we find that their protocol is still vulnerable to identity guessing attack, user impersonation attack, trace attack, desynchronization attack, and has no perfect forward secrecy. Therefore, we propose a security-enhanced and lightweight authentication protocol for industrial medical CPS. In the protocol, a dynamic temporary identity strategy is designed to protect anonymity and privacy, which enables the updating of temporary identities while resisting desynchronization attacks. The protocol is proved secure through formal security proof in random oracle model. Meanwhile, compared with the related protocols, our protocol is superior in security and cost to meet the lightweight requirements in medical scenarios.

Data-Correlation-Aware Unsupervised Deep-Learning Model for Anomaly Detection in Cyber–Physical Systems

A cyber–physical system (CPS) is a multidimensional complex system integrating computing, communication, and control technologies. Because of their key functionality within the system, CPS requires large robustness and security to ensure its reliable operation. Due to its importance in supporting overall system security, anomaly detection (AD) is likely to continue to play an important role in the CPS security. Moreover, unsupervised AD models based on deep learning have shown better performances in rule training, adaptive update, detection efficiency, and accuracy. Due to the nature of the CPS systems, CPS data is more likely to exhibit implicit correlative relationship among data, which would be vital to exploit for CPS security provisions in more complex data environments. In view of this observation, we propose the data-correlation-aware unsupervised deep learning model for AD in CPS, which uses an undigraph structure to store samples and implicit correlation among samples. We design a dual-autoencoder to train both original features and implicit correlation features among data, and we construct an estimation network using Gaussian mixture model (GMM) to evaluate the probability distribution of samples to complete the anomaly analysis. Experimental results compared the performance with relevant AD models based on deep learning which did not use data-correlation analysis. The results showed that, under some representative application scenarios of CPS considered, data-correlation-aware unsupervised deep-learning model achieved superior results in parameter sensitivity, ablation, relationship between correlation degree and detection performance, visualization, and detection effects.

Modeling of Satellite Constellation in Modelica and a PHM System Framework Driven by Model Data Hybrid

The new generation of low-earth-orbit (LEO) satellite constellation systems has the characteristics of low delay, strong signal and global coverage, and it is an important direction for the development of next-generation communication technology. A major disadvantage is that the constellation system is huge, often composed of hundreds or thousands of satellites, which puts forward high requirements for the design and health management of the constellation system, and the existing telemetry data monitoring system cannot meet the actual needs. CPS is a multidimensional complex system that integrates computation, communication and control (3C). Through the deep integration and cooperation of 3C, the real-time monitoring and dynamic control of large-scale engineering systems are realized, which is completely suitable for the operation and maintenance requirements of the satellite constellation system. This paper firstly establishes the entire satellite constellation system model, which is integrated from the satellite multidomain system, the constellation orbit environment system and the communication link system. Then, according to the technical concept of cyber-physical systems (CPS), an implementation framework of a prognostics and health (PHM) system driven by a model–data hybrid for satellite constellation systems is proposed. The framework is based on model simulation data and telemetry data and combines virtual and real data fusion, fault diagnosis, simulation prediction and other technologies to generate enhanced data to drive the effective operation of the PHM system. Finally, a verification case is designed to prove that the satellite constellation health management system implemented under this framework has a positive effect on the reliable operation and maintenance of the satellite constellation system.

Verification and Fault Analysis based on Combination of AADL and Modelica

CPS is a multidimensional complex system that can realize the interaction between computing process and physical process. Aiming at the problems of fault occurrence and uncertain behavior, this paper proposes the fault analysis stochastic hybrid automata as a formal model, the attributes of randomness and fault analysis are added through AADL behavior attachment to expand the attributes of hybrid automata, and applies the extended automata to the embedded system for system description and fault analysis. The model is used to model the fire control system, and AADL, Modelica and fault tree are combined to form a new model. The behavior is analyzed, and the conversion algorithm and conversion example are given.

Secure and intelligent slice resource allocation in vehicles-assisted cyber physical systems

Cyber physical systems (CPSs) are multi-dimensional complex systems integrated with computing, networking and physical modules, enabling to realize complex computing and communication tasks. Vehicles are composed of many electrical and electronic hardware modules. With integrating mobile vehicles, CPSs can be extended to realize more remote tasks. Network slicing (NS) is recognized as the key enabler towards the agile enabler of vehicles-assisted CPSs. In NS, one crucial technical issue is the slice resource allocation. Intelligent approaches, such as deep learning (DP), emerge and can be adopted to allocate slice resource allocation. In addition, due to the heterogeneity nature of physical elements in CPSs, security is strongly required to be addressed. On these basis, secure and intelligent slice resource allocation in vehicles-assisted CPSs is researched in this paper. At first, system models of vehicles-assisted CPSs with NS scheme and slices are described. Then, the intelligent resource allocation framework, abbreviated as Sec-Slice-Intell, is proposed and described. The goal of Sec-Slice-Intell framework is to achieve secure resource allocation per slice for NS-enabled CPSs. In order to validate the merits of the proposed framework, evaluation work is conducted and illustrated, in the form of simulation.

A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems

Cyber-Physical Systems (CPS), as a multi-dimensional complex system that connects the physical world and the cyber world, has a strong demand for processing large amounts of heterogeneous data. These tasks also include Natural Language Inference (NLI) tasks based on text from different sources. However, the current research on natural language processing in CPS does not involve exploration in this field. Therefore, this study proposes a Siamese Network structure that combines Stacked Residual Long Short-Term Memory (bidirectional) with the Attention mechanism and Capsule Network for the NLI module in CPS, which is used to infer the relationship between text/language data from different sources. This model is mainly used to implement NLI tasks and conduct a detailed evaluation in three main NLI benchmarks as the basic semantic understanding module in CPS. Comparative experiments prove that the proposed method achieves competitive performance, has a certain generalization ability, and can balance the performance and the number of trained parameters.

Modeling and Decision-Making Methods for a Class of Cyber–Physical Systems Based on Modified Hybrid Stochastic Timed Petri Net

Cyber-physical system (CPS) is a multidimensional complex system that integrates computation, communication, and physical environment with essential and broad application prospects. However, there is a kind of CPS containing discrete events, continuous processes, stochastic phenomena, time-delay, and decision. It suffers from several sophisticated modeling and decision-making challenges, including deep integration of cyber and physical world, intensive temporal properties, model uncertainty, concurrency, and behavioral control. In this article, we deal with the above problems from the perspective of hybrid systems. We propose the modeling methods of CPS based on the modified hybrid stochastic timed Petri net (M-HSTPN) with three-tier architecture and introduce the decision place to strengthen the decision-making ability of CPS. Its advantages lie in describing multiple problems in CPS at the same time by adopting a unified open architecture. By integrating the various modeling and decision-making challenges under a unified framework, it is easy to analyze the problem systematically. Taking the humanoid soccer robot CPSs (HSR-CPS) as an example, by analyzing factors that affect the action cycle and interception success rate of the HSRs, we show the powerful modeling and decision-making capabilities of the M-HSTPN for CPS.

Algorithmic Complexity of Multiplex Networks

Multilayer networks preserve full information about the different interactions among the constituents of a complex system, and have recently proven quite useful in modelling transportation networks, social circles, and the human brain. A fundamental and still open problem is to assess if and when the multilayer representation of a system provides a qualitatively better model than the classical single-layer aggregated network. Here we tackle this problem from an algorithmic information theory perspective. We propose an intuitive way to encode a multilayer network into a bit string, and we define the complexity of a multilayer network as the ratio of the Kolmogorov complexity of the bit strings associated to the multilayer and to the corresponding aggregated graph. We find that there exists a maximum amount of additional information that a multilayer model can encode with respect to the equivalent single-layer graph. We show how our complexity measure can be used to obtain low-dimensional representations of multidimensional systems, to cluster multilayer networks into a small set of meaningful super-families, and to detect tipping points in the evolution of different time-varying multilayer graphs. Interestingly, the low-dimensional multiplex networks obtained with the proposed method also retain most of the dynamical properties of the original systems, as demonstrated for instance by the preservation of the epidemic threshold in the multiplex SIS model. These results suggest that information-theoretic approaches can be effectively employed for a more systematic analysis of static and time-varying multidimensional complex systems.

From the Difference of Structures to the Structure of the Difference

When dealing with evolving or multidimensional complex systems, network theory provides us with elegant ways of describing their constituting components, through, respectively, time-varying and multilayer complex networks. Nevertheless, the analysis of how these components are related is still an open problem. We here propose a general framework for analysing the evolution of a (complex) system, by describing the structure created by the difference between multiple networks by means of the Information Content metric. Differently from other approaches, which focus on assessing the magnitude of the change, the proposed one allows understanding if the observed changes are due to random noise or to structural (targeted) modifications; in other words, it allows describing the nature of the force driving the changes and discriminating between stochastic fluctuations and intentional modifications. We validate the framework by means of sets of synthetic networks, as well as networks representing real technological, social, and biological evolving systems. We further propose a way of reconstructing network correlograms, which allow converting the system’s evolution to the frequency domain.

Exploring the multidimensional complex systems structure of the stress response and its relation to health and sleep outcomes

To gain a comprehensive understanding of the multidimensional complex systems structure of the stress response and related health outcomes, we utilized network analysis in a sample of 328 healthy participants in two steps. In a first step, we focused on associations between measures of basal hypothalamic-pituitary-adrenal axis functioning and subjective stress perceptions. In a second step, we linked these diverse stress-related measures to biomarkers and self-reports of health and sleep. Overall, measures clustered depending on their method of assessment, with high correlations between different saliva-based indices of diurnal cortisol regulation, between cortisol and cortisone levels in hair, between different biological health indicators (systemic inflammatory activity and body mass index), between state (experience sampling) and trait (questionnaire-based) self-reports of stress and wellbeing, and between different self-reports of sleep. Bridges between clusters suggested that if individuals perceive stress throughout their daily lives this is reflected in their total salivary cortisol output possibly contributing to long-term cortisol accumulation in hair. Likewise, earlier awakening time may contribute to cortisol accumulation in hair via an influence on awakening cortisol processes. Our results show that while meaningful connections between measures exist, stress is a highly complex construct composed of numerous aspects. We argue that network analysis is an integrative statistical approach to address the multidimensionality of the stress response and its effects on the brain and body. This may help uncover pathways to stress-related disease and serve to identify starting points for prevention and therapeutic intervention.

Universal resilience patterns in complex networks.

Resilience, a system's ability to adjust its activity to retain its basic functionality when errors, failures and environmental changes occur, is a defining property of many complex systems. Despite widespread consequences for human health, the economy and the environment, events leading to loss of resilience--from cascading failures in technological systems to mass extinctions in ecological networks--are rarely predictable and are often irreversible. These limitations are rooted in a theoretical gap: the current analytical framework of resilience is designed to treat low-dimensional models with a few interacting components, and is unsuitable for multi-dimensional systems consisting of a large number of components that interact through a complex network. Here we bridge this theoretical gap by developing a set of analytical tools with which to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive effective one-dimensional dynamics that accurately predict the system's resilience. The proposed analytical framework allows us systematically to separate the roles of the system's dynamics and topology, collapsing the behaviour of different networks onto a single universal resilience function. The analytical results unveil the network characteristics that can enhance or diminish resilience, offering ways to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems resilient to both internal failures and environmental changes.

Water Diplomacy: Perspectives from a Group of Interdisciplinary Graduate Students

Water management challenges are multifaceted, often involving issues of water availability, quality, access and equity. It is increasingly recognized that professionals trained to work in traditional disciplinary roles are ill-equipped to address complex issues such as water that transcend both geographic and disciplinary boundaries. A growing number of interdisciplinary doctoral programs, including the Water Diplomacy Integrative Graduate Education and Research Traineeship (IGERT) at Tufts University, have formed to respond to demand for progress in this area. This paper presents a Water Diplomacy IGERT student perspective on the benefits and challenges of interdisciplinary water education. First, we find that articulating a set of shared principles helps to build a strong research community, which in turn shapes the research approach and perspectives of participating students. Second, we recommend students clearly state uncertainties and assumptions to tackle the challenge of working with multidimensional complex systems. Next, we advise that students collaborate with researchers and partner organizations to gain exposure to different approaches outside of their discipline, and to increase breadth of knowledge while maintaining sufficient depth. Finally, we recommend that students take an active role in program leadership to ensure balance between program and student development. These difficulties are not unique to water education, and therefore we hope this discussion can help inform future interdisciplinary education and research efforts.

Multilayer network representation of membrane potential and cytosolic calcium concentration dynamics in beta cells

Modern theory of networks has been recognized as a very successful methodological concept for the description and analysis of complex systems. However, some complex systems are more complex than others. For instance, several real-life systems are constituted by interdependent subsystems and their elements are subjected to different types of interactions that can also change with time. Recently, the multilayer network formalism has been proposed as a general theoretical framework for the description and analysis of such multi-dimensional complex systems and is acquiring more and more prominence in terms of a new research direction. In the present study, we use this methodology for the description of functional connectivity patterns and signal propagation between pancreatic beta cells in an islet of Langerhans at the levels of membrane potential (MP) and cytosolic calcium concentration ([Ca2+]c) dynamics to study the extent of overlap in the two networks and to clarify whether time lags between the two signals in individual cells are in any way dependent on the role these cells play in the functional networks. The two corresponding network layers are constructed on the basis of signal directions and pairwise correlations, whereas the interlayer connections represent the time lag between both measured signals. Our results confirm our previous finding that both MP and [Ca2+]c change spread across an islet in the form of a depolarization and a [Ca2+]c wave, respectively. Both types of waves follow nearly the same path and the networks in both layers have a similar but not entirely the same structure. We show that the observed discrepancies are attributed to variability in delays between the depolarization and rise in [Ca2+]c. In particular, high-degree nodes in both layers are found to exhibit a larger time lag between the MP and the [Ca2+]c signal than nodes with less connections. We speculate that this finding reflects a higher activity of endoplasmic reticulum calcium pumps in the most connected cells. Our findings indicate that visualizing and studying the temporal information flow and interaction patterns between beta cells as a multiplex network can provide valuable new insights into the physiology of the complex signaling processes in islets of Langerhans.

Modeling and Analysis for CPS Physical Entities Based on Spatio-Temporal Petri Net

CPS is a kind of multidimensional complex system fused with computing system, physical environment and network environment. CPS achieves the target of real-time perception and dynamic control of physical environment by the organic integration of 3C operations and depth of their cooperation. However, CPS also faces enormous challenges. Since CPS is a kind of large, heterogeneous, distributed and real-time feedback system which is integrated with multiple heterogeneous subsystem via the network, its system complexity is above the general information system. And the introduction of physical entities control system brings tremendous difficulties to development as well as its system complexity. This paper conducts a detailed analysis of the characteristics of the physical aspects of CPS and studies CPS’s physical entities of the properties and their location changes process in-depth. Then we propose a formal modeling method of physical entities in CPS by constructing a Spatial-Temporal Petri net model introducing space factor into the traditional Timed Petri net, which not only able to describe logical and time-level the behavior of physical entities, but also the changes of state caused by the position change of the physical entity. At last an example of robot control system proves the effectiveness of spatial and temporal Petri net models.