The Complexity in Application of Modeling and Simulation for Cyber Physical Systems Engineering

Mittal, Saurabh; Tolk, Andreas

doi:10.1002/9781119552482.ch1

Cited by 9 publications

(13 citation statements)

References 22 publications

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“…34,35 A system is resilient if the occurrence of undesired incidents is minimized, and, once an incident occurs, its undesirable consequences are reduced, and the system returns to normal operation within a short time. 34,36,37 From the perspective of the resilience of engineered Systems of Systems (SoS), resilience can be viewed as an inherent property of the system that is influenced by the SoS components and their behaviors, with an understanding that the resilience of a system is dynamic, and changes as the environment changes. 30 In a similar approach to the NAS definition above, 38 recognize that quality, robustness and agility are components of resilience, and identify repairability, extensibility, flexibility, adaptability, and versatility as how the system achieves robustness after a shock.…”

Section: Resilience In Dynamic and Contested Environmentsmentioning

confidence: 99%

“…Complex systems are ubiquitous, and, as their operations increasingly take place in contested and dynamic environments, so the need for their resilience becomes critical. 36,38 Modeling and simulation approaches by their nature have become increasingly popular in the analysis of the resilience of complex systems under various attacks and environmental changes, 4,40 with application domains such as energy infrastructures, 41 emergency department planning and analysis, 42 landscape ecology, 43 sociotechnical systems, 44 supply chain management, [45][46][47] and cyber-physical systems (CPS) 48 employing modeling and simulation to study various aspects of resilience. Keane et al 43 proposed the use of discrete event simulation to understand the resilience of emergency departments within hospitals that are facing earthquakes.…”

Section: Modeling Simulation Experimentation and Validationmentioning

confidence: 99%

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A simulation and experimentation architecture for resilient cooperative multiagent reinforcement learning models operating in contested and dynamic environments

Honhaga,

Szabo

2024

SIMULATION

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Cooperative multiagent reinforcement learning approaches are increasingly being used to make decisions in contested and dynamic environments, which tend to be wildly different from the environments used to train them. As such, there is a need for a more in-depth understanding of their resilience and robustness in conditions such as network partitions, node failures, or attacks. In this article, we propose a modeling and simulation framework that explores the resilience of four c-MARL models when faced with different types of attacks, and the impact that training with different perturbations has on the effectiveness of these attacks. We show that c-MARL approaches are highly vulnerable to perturbations of observation, action reward, and communication, showing more than 80% drop in the performance from the baseline. We also show that appropriate training with perturbations can dramatically improve performance in some cases, however, can also result in overfitting, making the models less resilient against other attacks. This is a first step toward a more in-depth understanding of the resilience c-MARL models and the effect that contested environments can have on their behavior and toward resilience of complex systems in general.

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Section: Resilience In Dynamic and Contested Environmentsmentioning

confidence: 99%

Section: Modeling Simulation Experimentation and Validationmentioning

confidence: 99%

A simulation and experimentation architecture for resilient cooperative multiagent reinforcement learning models operating in contested and dynamic environments

Honhaga,

Szabo

2024

SIMULATION

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“…The application of M&S will involve multiple levels of concept, specification, and operation in CPS engineering (CPSE) -to use formal methods to express basic concepts (such as structure, state, events, concurrency, etc.) and their relationships, to represent the system studies (problem) in the real world as a model, and to verify the implementation of system behaviors and functions (solution) by executing various simulation instructions through a simulation engine [8]. Therefore, CPS is an advanced hybrid form of system and be modeled in some very distinctive ways, such as computational elements for discrete modeling; physical elements for continuous modeling; communication networking for probabilistic; and even a game theoretic for operations research to support planning and decision making [9].…”

Section: Model-based Paradigmmentioning

confidence: 99%

“…The subject of agent-based development and testing platforms makes the clear connection between intelligent, adaptive, and autonomous CPS and its "digital twin" (DT) in form of intelligent software agents within a situated virtual context that replicates the nature of the physical environment. [8] Through the application of the digital twin of a system, we will have possibilities to analyze and test various operational scenarios on the complex swarm before its physical implementation. A digital twin is a virtual representation which is based on the digitalization of physical systems to allows modeling the state of a physical entity or system, and it is created by digitalizing data collected from physical entities through sensors, so various predictions could be made by understanding the behavior of the physical entity [17].…”

Section: Digital Twins In L-v-c Platformmentioning

confidence: 99%

The Framework of Modeling and Simulation based-on Swarm Ontology for Autonomous Unmanned Systems

Gao¹,

Xiao²,

Xie³

et al. 2023

Preprint

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For the emerging autonomous swarm technology, from the perspective of systems science and systems engineering, there must be some novel elements and methods to aggregate multiple systems into a group, which distinguish with the general components with specific functions, and we expect to provide the presentation of their existence in the swarm development processes. The inspiration of our methodology origins from the integration of swarm ontology, multi-paradigm modeling, multi-agent system, cyber-physical system, etc. Therefore, we choose the model-driven technology as a framework to acquire an approach of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism is defined and formed in parallel with ConOps analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by the nonlinear causation among individual and whole behaviors. This approach highlights to use the MBSE processes and their artifacts to embed the meso mechanism to integrate the operational and the functional, and which means to connect the macro and micro aspects in formalism and so to become a whole with its expected goals, and then to verify and validate within a L-V-C simulation environment.

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“…There are many challenges that face the vision of CPS in I4.0 such as security (Mahmoud, Hamdan and Baroudi, 2019; Habibzadeh et al , 2019), high complexity (Mittal and Tolk, 2019; Cao et al , 2021) and interoperability issues (Lu and Asghar, 2020; Fatima et al , 2020; Weichhart et al , 2021), which is the main focus of this research.…”

Section: Introductionmentioning

confidence: 99%

Semantic web and machine learning techniques addressing semantic interoperability in Industry 4.0

Hafidi,

Djezzar,

Hemam

et al. 2023

IJWIS

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Purpose This paper aims to offer a comprehensive examination of the various solutions currently accessible for addressing the challenge of semantic interoperability in cyber physical systems (CPS). CPS is a new generation of systems composed of physical assets with computation capabilities, connected with software systems in a network, exchanging data collected from the physical asset, models (physics-based, data-driven, . . .) and services (reconfiguration, monitoring, . . .). The physical asset and its software system are connected, and they exchange data to be interpreted in a certain context. The heterogeneous nature of the collected data together with different types of models rise interoperability problems. Modeling the digital space of the CPS and integrating information models that support cyber physical interoperability together are required. Design/methodology/approach This paper aims to identify the most relevant points in the development of semantic models and machine learning solutions to the interoperability problem, and how these solutions are implemented in CPS. The research analyzes recent papers related to the topic of semantic interoperability in Industry 4.0 (I4.0) systems. Findings Semantic models are key enabler technologies that provide a common understanding of data, and they can be used to solve interoperability problems in Industry by using a common vocabulary when defining these models. Originality/value This paper provides an overview of the different available solutions to the semantic interoperability problem in CPS.

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The Complexity in Application of Modeling and Simulation for Cyber Physical Systems Engineering

Cited by 9 publications

References 22 publications

A simulation and experimentation architecture for resilient cooperative multiagent reinforcement learning models operating in contested and dynamic environments

A simulation and experimentation architecture for resilient cooperative multiagent reinforcement learning models operating in contested and dynamic environments

The Framework of Modeling and Simulation based-on Swarm Ontology for Autonomous Unmanned Systems

Semantic web and machine learning techniques addressing semantic interoperability in Industry 4.0

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