Extending service model to build an effective service composition framework for cyber-physical systems

Huang, Jian; Bastani, Farokh B.; Yen, I-Ling; Dong, Jin; Zhang, W.; Wang, F.-J.; Hsu, H.-J.

doi:10.1109/soca.2009.5410453

Cited by 27 publications

(4 citation statements)

References 7 publications

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“…In the simulation study of the CPS system, Lin Jing et al from the Missouri University of Technology used agent-based modeling to construct a model of CPS [7]. Based on the service-oriented architecture (SOA), the University of Texas proposed Physical-entity service model for CPS modeling [8]. Frank Wawrzik et al proposed a SysML-based CPS modeling simulation method SICYPHOS CPS [4].…”

Section: Fig 1 C Typical Cyber-physical Systemmentioning

confidence: 99%

Modeling and Simulation of CPS based on SysML and Modelica (S)

Deng¹,

Yan²,

Gao³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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Cyber-Physical Systems (CPS) is usually associated with large numbers of domains. The domain relevance allows system engineers' knowledge to spread many domains. The efficiency and accuracy of modeling are not able to be guaranteed. Therefore, in this paper, a set of CPS modeling guideline is proposed based on SysML, and the system's 4-layer abstract hierarchy and the kind of modeling products obtained on each layer are defined. Based on this, a modeling specification containing seven sub-processes is designed. In order to verify the correctness of CPS function and the consistency of the business logic at the primary phase of the design, we summarize the mapping rules of SysML-Modelica and define the algorithm of model conversion. Finally, a simple CPS case is used to verify our task. The results show that this method can be effective in CPS's modeling and Simulation.

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Section: Fig 1 C Typical Cyber-physical Systemmentioning

confidence: 99%

Modeling and Simulation of CPS based on SysML and Modelica (S)

Deng¹,

Yan²,

Gao³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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“…Moreover, an early effort introducing the need for a modular architecture for the control system design, has been discussed in [11]. More recent efforts [12], though not focusing specifically on the control problem, address the modelling of cyber-physical systems as collections of services offered by specific cyber-physical tools. In the Computational Intelligence domain, a significant milestone was the approval of the IEEE Standard for Fuzzy Markup Language (IEEE Std 1855-2016), which specifies an interoperability framework for fuzzy logic controllers [13], [14].…”

Section: Introductionmentioning

confidence: 99%

A cognitive agent architecture for feedback control scheme design

Milis

Ηλιάδης

Panayiotou

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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We present a novel architecture for the design of feedback control schemes, aiming to automate the cognitive process performed by human experts when designing control schemes for certain systems. The work starts with the identification of types of cyber-physical components participating in a feedback control scheme. Each component is defined as a functional mapping between inputs-vectors and an output vector. The inputs and outputs are then mapped to a semantic space formed by linguistic variables that model the expert knowledge. These semantic annotations of the components are then exploited by a Cognitive Agent with semantic reasoning capabilities, to achieve the online configuration of a feedback control scheme, given a set of specifications and a database of available components' implementations. The adopted knowledge modelling and reasoning techniques are driven by past efforts of the World Wide Web Consortium on the semantic composition of Web services. The applicability of the method is tested via a paper-based enactment of a use-case from the smart buildings domain.

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“…An early effort towards introducing the need for a modular architecture for the control system design and the concept of structuring the data exchanged based on their basic semantic description, has been discussed in [21]. More recent efforts [22], though not focusing specifically on the control problem, address the modelling of cyber-physical systems as collections of services offered by specific physical devices and cyber tools within certain time and domain context. These services are then invoked, based on their standard descriptions for online composition of services, to perform specific tasks.…”

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confidence: 99%

Semantically Enhanced Online Configuration of Feedback Control Schemes

Milis

Panayiotou

2018

IEEE Trans. Cybern.

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Abstract-Recent progress towards the realization of the "Internet of Things" has improved the ability of physical and soft/cyber entities to operate effectively within large-scale, heterogeneous systems. It is important that such capacity be accompanied by feedback control capabilities sufficient to ensure that the overall systems behave according to their specifications and meet their functional objectives. To achieve this, such systems require new architectures that facilitate the online deployment, composition, interoperability and scalability of control system components. Most current control systems lack scalability and interoperability because their design is based on a fixed configuration of specific components, with knowledge of their individual characteristics only implicitly passed through the design. This work addresses the need for flexibility when replacing components or installing new components, which might occur when an existing component is upgraded or when a new application requires a new component, without the need to readjust or redesign the overall system. A semantically-enhanced feedback control architecture is introduced for a class of systems, aimed at accommodating new components into a closed-loop control framework by exploiting the semantic inference capabilities of an ontology-based knowledge model. This architecture supports continuous operation of the control system, a crucial property for large-scale systems for which interruptions have negative impact on key performance metrics that may include human comfort and welfare or economy costs. A case-study example from the smart buildings domain is used to illustrate the proposed architecture and semantic inference mechanisms.Index Terms-cyber-physical systems, feedback control, internet of things, semantic composition, semantic knowledge models. W E currently live in the "smart era" where people and machines intelligently interact in work and social ecosystems [1]. This interaction is facilitated by a variety of smart machines, from small personal devices to hardware and software-equipped smart buildings [2], [3], to even larger and more complex systems such as electric power grids [4]. The high penetration of smart portable and embedded devices connected to networks, suggest a future where machines will interact with each other and the environment, in a contextaware framework [5]. Thus, new sensing, actuation and control capabilities can be created.In this context, consider the case of a smart building, equipped with various sensors, actuators and controllers, designed to maintain the comfort and safety of the inhabitants, while reducing operating costs. A smart building may consist of various sub-systems that control the lighting, the temperature and the humidity, the condition of the air, fire alarm systems, sprinkler systems and many more. In most cases, the design of these systems is based on a fixed configuration of specific components, with certain knowledge of their specific characteristics. For example, specific temperature se...

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Extending service model to build an effective service composition framework for cyber-physical systems

Cited by 27 publications

References 7 publications

Modeling and Simulation of CPS based on SysML and Modelica (S)

Modeling and Simulation of CPS based on SysML and Modelica (S)

A cognitive agent architecture for feedback control scheme design

Semantically Enhanced Online Configuration of Feedback Control Schemes

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