Embedded real-time systems in cyber-physical applications: a frequency domain analysis methodology

ABSTRACT

Cyber-Physical Systems (CPSs) use sensors and actuators to interface between an embedded system and the physical world. The time-continuous domain of the physical world should be periodically sampled by real-time tasks in an embedded system to preserve its dynamic properties in the time-discrete domain.

Because the task execution pattern may vary during runtime, a jitter in the execution of a real-time task hinders the periodicity of its execution. The effects of jitters in CPSs are difficult to determine when the premises of the Nyquist-Shannon sampling theorem are not satisfied.

This paper proposes using frequency domain analysis to determine the perturbations that a real-time system produces on real-world applications; accordingly, the paper defines both a design and an evaluation criterion for real-time systems in CPS applications. The Fixed Priority discipline is analysed through simulations to conclude that no special design techniques are required when the utilization factors are less than 20%.     

CPS处理器中的时间指令扩展研究及实现

物理进程具有内在的并发及实时特性,因此发展信息—物理融合系统（CPS）需要计算进程能表达这类特性。而传统的计算模式为了方便用户逻辑描述,随着抽象程度的提高逐步丢弃了对时间特性的精确描述。在嵌入式Java处理器JPOR-32基础上面向CPS应用增加了时钟寄存器和时钟计数器,并根据程序员对时间特性的需求,结合异常机制扩展了四条时间指令,使得用户可根据不同需求对时间进行精确控制。最后通过采用时间指令后图像处理程序在该CPS处理器上的运行结果验证了该时间控制机制的可行性、正确性及精确性。     

A survey on the security of cyber-physical systems

Cyber-physical systems (CPSs) are integrations of computation, communication, control and physical processes. Typical examples where CPSs are deployed include smart grids, civil infrastructure, medical devices and manufacturing. Security is one of the most important issues that should be investigated in CPSs and hence has received much attention in recent years. This paper surveys recent results in this area and mainly focusses on three important categories: attack detection, attack design and secure estimation and control. We also discuss several future research directions including risk assessment, modeling of attacks and attacks design, counter-attack strategy and testbed and validation.     

关键基础设施网络安全技术研究进展

通过对关键基础设施的网络安全现状进行总结,对关键基础设施的安全威胁进行了分类,着重对信息物理融合系统在受到网络攻击时采用的数据词典、证据理论、博弈论等方法进行网络安全防护深入分析,着重分析了基于信任理论的信息物理融合系统的防御方案,阐述了关键基础设施的相关性与脆弱性之间的关系,总结了现有主要的关键基础设施网络安全的模拟技术,对比分析了现有的仿真技术的特点.最后提出了关键基础设施网络安全技术值得深入研究的发展方向.     

具有随机网络攻击的切换信息物理系统的事件触发控制（英文）

本文研究了随机网络攻击下切换信息物理系统的事件触发控制问题.将信息物理系统描述为一种切换线性系统形式.引入事件触发机制来节省系统资源和减轻网络负载,当误差超过给定阈值时传感器中的采样数据才通过通信网络传输到控制器中.考虑在传感器与控制器的通信网络中受到两种不同特征的随机网络攻击.在网络攻击和所设计的事件触发控制器下,建立了切换随机信息物理系统模型.利用模态依赖平均驻留时间方法构建了相应的切换信号.在设计的事件触发控制器和模态依赖平均驻留时间切换信号下实现了系统的均方指数稳定性,并给出了控制器增益.最后,通过实例验证了所得理论结果的有效性.     

AADL建模工具的设计

设计一种架构分析与设计语言(AADL)建模工具,便于用户建立AADL模型.基于模型驱动体系结构思想,在Eclipse插件开发平台上开发可扩展的插件集合AADL模型设计工具.用户根据该工具建立的嵌入式系统AADL架构模型,通过XML文档保存,分析与验证工具以第三方插件的形式集成到该工具中,提取系统的AADL架构模型.通过应用实例验证了该工具的有效性.     

Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing

Machine-to-machine (M2M) communication is a crucial technology for collaborative manufacturing automation in the Industrial Internet of Things (IIoT)-empowered industrial networks. The new decentralized manufacturing automation paradigm features ubiquitous communication and interoperable interactions between machines. However, peer-to-peer (P2P) interoperable communications at the semantic level between industrial machines is a challenge. To address this challenge, we introduce a concept of Semantic-aware Cyber-Physical Systems (SCPSs) based on which manufacturing devices can establish semantic M2M communications. In this work, we propose a generic system architecture of SCPS and its enabling technologies. Our proposed system architecture adds a semantic layer and a communication layer to the conventional cyber-physical system (CPS) in order to maximize compatibility with the diverse CPS implementation architecture. With Semantic Web technologies as the backbone of the semantic layer, SCPSs can exchange semantic messages with maximum interoperability following the same understanding of the manufacturing context. A pilot implementation of the presented work is illustrated with a proof-of-concept case study between two semantic-aware cyber-physical machine tools. The semantic communication provided by the SCPS architecture makes ubiquitous M2M communication in a network of manufacturing devices environment possible, laying the foundation for collaborative manufacturing automation for achieving smart manufacturing. Another case study focusing on decentralized production control between machines in a workshop also proved the merits of semantic-aware M2M communication technologies.     

A metamodel for cyber-physical systems

With the advent of the Internet of Things and Industry 4.0 concepts, cyber-physical systems in civil engineering experience an increasing impact on structural health monitoring (SHM) and control applications. Designing, optimizing, and documenting cyber-physical system on a formal basis require platform-independent and technology-independent metamodels. This study, with emphasis on communication in cyber-physical systems, presents a metamodel for describing cyber-physical systems. First, metamodeling concepts commonly used in computing in civil engineering are reviewed and possibilities and limitations of describing communication-related information are discussed. Next, communication-related properties and behavior of distributed cyber-physical systems applied for SHM and control are explained, and system components relevant to communication are specified. Then, the metamodel to formally describe cyber-physical systems is proposed and mapped into the Industry Foundation Classes (IFC), an open international standard for building information modeling (BIM). Finally, the IFC-based approach is verified using software of the official IFC certification program, and it is validated by BIM-based example modeling of a prototype cyber-physical system, which is physically implemented in the laboratory. As a result, cyber-physical systems applied for SHM and control are described and the information is stored, documented, and exchanged on the formal basis of IFC, facilitating design, optimization, and documentation of cyber-physical systems.     

分布式电网CPS 系统数据攻击下的状态估计

电力物理网络通过构建信息网络进行优化调控并构成信息物理融合系统, 实现大规模分布式系统的优化控制, 随之而来的问题是病毒、黑客入侵、拒绝服务等来自信息网络的威胁, 导致物理系统恶意破坏. 鉴于此, 以攻击可检测为前提, 建立攻击信号下的电力系统分布式动态模型, 设计动态状态估计器检测受攻击的信号, 并估计其原始信号. 最后通过3 机9 节点分布式电网系统仿真实验验证了所设计的状态估计器对于数据攻击检测的有效性.

     

基于模型的嵌入式系统软件工程语言AADL与UML的比较

随着嵌入式系统规模、复杂度和性能需求的提升,嵌入式系统开发的重点从代码级提前到模型级,模型驱动体系结构成为嵌入式系统开发的主流。统一建模语言(UML)和结构分析和设计语言(AADL)是模型驱动系统工程的标准,从方法、扩展机制和应用领域三个方面对两者进行研究和比较,讨论模型分析、模型转换等相关技术。最后,探讨了UML和AADL的发展与研究方向     

ARINC653航空电子系统AADL建模技术研究

随着ARINC653系统在我国航空领域的广泛应用,研究ARINC653系统建模方法将为进一步研究基于模型的ARINC653系统可靠性、可调度性分析奠定基础.详细研究了ARINC653系统AADL建模的方法,具体描述了ARINC653系统、分区、进程、进程通信和健康监控等核心元素的AADL建模映射机制,并进行实例说明.     

信息-物理融合系统若干关键问题综述

信息-物理融合系统(Cyber-Physical System,CPS)集成了计算系统与物理系统,并通过嵌入式计算机与网络实现了两者之间的协作和融合,将对人们的生产和生活方式产生重要影响.CPS是一个全新的研究领域,利用现有基础理论和技术设计CPS时面临着众多问题.介绍了CPS的概念、特点和体系结构,分析了与嵌入式系统、网络的关联,从计算系统、网络系统和控制系统3个方面概括了CPS设计面临的主要挑战,并着重探讨了当前一些可用于CPS设计的理论和技术以及CPS研究的最新进展,指出CPS当前的发展应以解决系统抽象层次设计、系统建模、体系结构设计、数据传输和管理、子系统集成方面的问题作为其下一步发展主要的研究方向,并提出了一些可行的解决办法,可为相关研究提供参考.     

基于AADL的综合航电分区系统可调度性判定

综合模块化航电(IMA)系统中的分区系统提高了其可靠性和安全性,但在系统设计和实现过程中,应采用各种分析和验证方法确保系统的时间需求得到满足。为此,针对符合ARINC653规范的IMA系统,根据分区系统层级调度的特性,提出一种基于仿真的分区任务集可调度性判定方法。借助Cheddar工具及其自定义调度策略功能,使架构分析和设计语言(AADL)具有对分区系统进行建模的能力,并利用该工具对AADL模型进行仿真以判定系统的可调度性。实例分析结果表明,该方法能自动、准确、快速地进行可调度性判定,并以甘特图的方式绘制任务调度过程,得到直观、详细的结果。     

复杂嵌入式实时系统体系结构设计与分析语言:AADL

首先归纳了AADL(architecture analysis and design language)的发展历程及其主要建模元素.其次,从模型 驱动设计与实现的角度综述了AADL 在不同阶段的研究与应用,总结了研究热点,分析了现有研究的不足,并对 AADL 的建模与分析工具、应用实践进行了概述.最后,探讨了AADL 的发展与研究方向.     

Adaptive output-feedback resilient tracking control using virtual closed-loop reference model for cyber-physical systems with false data injection attacks

This work studies output-feedback resilient tracking control problems in cyber-physical systems (CPSs) with false data injection attacks via closed-loop model-reference adaptive control (MRAC) techniques. The control input signals in CPSs, directly and indirectly influenced by sensor and actuator false data injection attacks, could degrade the system's control performance seriously. Then a virtual closed-loop reference model is used as a mediator between the open-loop reference and actual systems to improve the ability to suppress attacks, and by the MRAC techniques, a novel adaptive output-feedback resilient tracking control scheme is proposed to ensure the reliability of the attacked systems. Different from the existing results, (a) the new one is with an adaptive attack compensator in a time-varying gain output-feedback form, which can automatically eliminate the impact of matched attacks by online adjusting the gain; and (b) the $L_2$-gain rejection property of mismatched attacks is achieved by the virtual closed-loop reference. Finally, an illustrative example validates the developed method.     

Offloading mobile data traffic for QoS-aware service provision in vehicular cyber-physical systems

Owing to the increasing number of vehicles in vehicular cyber-physical systems (VCPSs) and the growing popularity of various services or applications for vehicles, cellular networks are being severely overloaded. Offloading mobile data traffic through Wi-Fi or a vehicular ad hoc network (VANET) is a promising solution for partially solving this problem because it involves almost no monetary cost. We propose combination optimization to facilitate mobile data traffic offloading in emerging VCPSs to reduce the amount of mobile data traffic for the QoS-aware service provision. We investigate mobile data traffic offloading models for Wi-Fi and VANET. In particular, we model mobile data traffic offloading as a multi-objective optimization problem for the simultaneous minimization of mobile data traffic and QoS-aware service provision; we use mixed-integer programming to obtain the optimal solutions with the global QoS guarantee. Our simulation results confirm that our scheme can offload mobile data traffic by up to 84.3% while satisfying the global QoS guarantee by more than 70% for cellular networks in VCPSs.     

AADL分级调度模型的分析与验证

针对嵌入式系统体系结构分析设计语言(architecture analysis and design language, AADL)分级调度模型的分析问题,提出了基于模型检验的可调度性分析和验证方法.基于时间自动机理论,将AADL分级调度模型转换为时间自动机网络,将待验证性质描述为时序逻辑公式,通过模型检验工具对可调度性进行分析和验证.研究结果表明,使用模型检验方法来分析AADL分级调度模型的可调度性是可行的.相对其他方法而言,该方法利用了形式化方法的穷举性来分析系统的性质,分析结果更加精确.     

信息物理融合系统网络安全综述

信息物理融合系统（cyber-physical systems， CPS）是集计算、通信和控制于一体的智能系统，实现网络和物理的深度协作和有机融合.目前CPS在关键的基础设施、政府机构等领域发挥着越来越重要的作用.由于物理限制，计算机和网络产生的安全漏洞会导致CPS遭受巨大的破坏，同时还会引起经济损失、社会动乱等连锁反应，所以研究CPS的安全问题对于确保系统安全运行具有重要意义.本文结合国内外的研究现状，概述了CPS安全控制和攻击检测的最新进展.首先本文总结了CPS典型的系统建模以满足对系统性能分析的需要.然后介绍了3种典型的网络攻击，即拒绝服务攻击、重放攻击和欺骗攻击.根据检测方法的类别，对CPS攻击检测的发展进行的概述.此外还讨论了系统的安全控制和状态估计.最后总结和展望了CPS网络安全面临的挑战和未来的研究方向.     

A novel approach for the detection of anomalous energy consumption patterns in industrial cyber-physical systems

Most scenarios emerging from the Industry 4.0 paradigm rely on the concept of cyber-physical production systems (CPPS), which allow them to synergistically connect physical to digital setups so as to integrate them over all stages of product development. Unfortunately, endowing CPPS with AI-based functionalities poses its own challenges: although advances in the performance of AI models keep blossoming in the community, their penetration in real-world industrial solutions has not so far developed at the same pace. Currently, 90% of AI-based models never reach production due to a manifold of assorted reasons not only related to complexity and performance: decisions issued by AI-based systems must be explained, understood and trusted by their end users. This study elaborates on a novel tool designed to characterize, in a non-supervised, human-understandable fashion, the nominal performance of a factory in terms of production and energy consumption. The traceability and analysis of energy consumption data traces and the monitoring of the factory's production permit to detect anomalies and inefficiencies in the working regime of the overall factory. By virtue of the transparency of the detection process, the proposed approach elicits understandable information about the root cause from the perspective of the production line, process and/or machine that generates the identified inefficiency. This methodology allows for the identification of the machines and/or processes that cause energy inefficiencies in the manufacturing system, and enables significant energy consumption savings by acting on these elements. We assess the performance of our designed method over a real-world case study from the automotive sector, comparing it to an extensive benchmark comprising state-of-the-art unsupervised and semi-supervised anomaly detection algorithms, from classical algorithms to modern generative neural counterparts. The superior quantitative results attained by our proposal complements its better interpretability with respect to the rest of algorithms in the comparison, which emphasizes the utmost relevance of considering the available domain knowledge and the target audience when design AI-based industrial solutions of practical value. Finally, the work described in this paper has been successfully deployed on a large scale in several industrial factories with significant international projection.     

Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook

The recent advances in sensor and communication technologies can provide the foundations for linking the physical manufacturing facility and machine world to the cyber world of Internet applications. The coupled manufacturing cyber-physical system is envisioned to handle the actual operations in the physical world while simultaneously monitor them in the cyber world with the help of advanced data processing and simulation models at both the manufacturing process and system operational levels. Moreover, a sensor-packed manufacturing system in which each process or piece of equipment makes available event and status information, coupled with market research for true advanced Big Data analytics, seem to be the right ingredients for event response selection and operation virtualization. As a drawback, the resulting manufacturing cyber-physical system will be vulnerable to the inevitable cyber-attacks, unfortunately, so common for the software and Internet-based systems. This reality makes cybersecurity penetration within the manufacturing domain a need that goes uncontested across researchers and practitioners. This work provides a review of the current status of virtualization and cloud-based services for manufacturing systems and of the use of Big Data analytics for planning and control of manufacturing operations. Building on already developed cloud business solutions, cloud manufacturing is expected to offer improved enterprise manufacturing and business decision support. Based on the current state-of-the-art cloud manufacturing solutions and Big Data applications, this work also proposes a framework for the development of predictive manufacturing cyber-physical systems that include capabilities for attaching to the Internet of Things, and capabilities for complex event processing and Big Data algorithmic analytics.