Simulation for Cyber-Physical Systems Engineering: A Cloud-Based Context

Preface
Contents
Editors and Contributors
Part IFoundations
1 Cloud-Based M&S for Cyber-Physical Systems Engineering
	1.1 Introduction
	1.2 Cloud-Based M&S
	1.3 M&S-Based CPS Engineering
		1.3.1 The Need for a Unified M&S Process
		1.3.2 Cloud Implications for CPS Engineering
	1.4 Book Overview
	1.5 Summary
	References
2 Composability Challenges for Effective Cyber Physical Systems Applications in the Domain of Cloud, Edge, and Fog Computing
	2.1 Introduction
	2.2 Cloud, Edge, and Fog Computing
		2.2.1 Cloud Computing
		2.2.2 Edge Computing
		2.2.3 Fog Computing
		2.2.4 Cyber Physical Systems and Cloud, Edge, and Fog Solutions
	2.3 Providing Computational Capability
		2.3.1 Models as the Reality of Computational Functions
		2.3.2 Interoperability Versus Composability
		2.3.3 Complementary and Competing Models
	2.4 Conceptual Consistency
		2.4.1 Data and Processes
		2.4.2 Ontological Representations
	2.5 Summary and Discussion
	References
3 Truth Management with Concept-Driven Agent Architecture in Distributed Modeling and Simulation for Cyber  Physical Systems Engineering
	3.1 Introduction
	3.2 SoS Nature of CPS
	3.3 CPS Modeling and Simulation
	3.4 Distributed Simulation Considerations for SoS
		3.4.1 Distributed Simulation Overview
		3.4.2 The SoS Nature of Distributed Simulation
		3.4.3 Distributed Simulation Challenges and Solution Approaches
		3.4.4 Truth Management Approaches in Distributed Simulation
	3.5 Conceptual Alignment and Reference Architecture for Truth Management
		3.5.1 Mobile Propertied Agents (MPAs) and Concept-Driven Agent Architecture (CDAA)
		3.5.2 Mathematical Considerations for a Parallel Distributed Cloud-Based M&S Infrastructure
		3.5.3 Reference Architecture Implementation
	3.6 Cloud Implications for Plausible Solution
	3.7 MPA/CDAA Applied to CPS M&S
	3.8 Conclusions and Future Work
	References
4 Implementing the Modelling and Simulation as a Service (MSaaS) Paradigm
	4.1 Introduction
		4.1.1 Terminology
		4.1.2 Allied Framework for MSaaS
		4.1.3 Chapter Overview
	4.2 Operational Concept
		4.2.1 MSaaS from the User Perspective
		4.2.2 Operational Concept Document
		4.2.3 Vision Statement and Goals
	4.3 Technical Concept
		4.3.1 MSaaS Reference Architecture
		4.3.2 MSaaS Discovery Service and Metadata
		4.3.3 MSaaS Engineering Process
	4.4 Governance Concept
		4.4.1 Governance and Roles
		4.4.2 General Policies
		4.4.3 Security Policies
		4.4.4 Compliance Policies
	4.5 Experimentation
		4.5.1 Explore and Test Enabling Technology
		4.5.2 Test Solutions for Simulation Services
	4.6 Evaluation
	4.7 Implementation Strategy and Next Steps
		4.7.1 Implementation Strategy
		4.7.2 Next Steps
	4.8 Summary and Conclusions
	References
5 Cyber-Physical System Engineering Oriented Intelligent High Performance Simulation Cloud
	5.1 Introduction
		5.1.1 Connotation of CPS
		5.1.2 Connotation of CPS Engineering
		5.1.3 Challenges of CPS Engineering for Modern Modeling and Simulation
	5.2 Cyber-Physical System Engineering Oriented Intelligent High Performance Simulation Cloud (CPSEO-IHPSC) for CPS Engineering
		5.2.1 Connotation of Cyber-Physical System Engineering Oriented Intelligent High Performance Simulation Cloud (CPSEO-IHPSC) for CPS Engineering:
		5.2.2 CPSEO-IHPSC Architecture
		5.2.3 CPSEO-IHPSC Technical System
	5.3 The Research of Cyber-Physical System Engineering Oriented Intelligent High Performance Simulation Cloud (Prototype)
		5.3.1 System Architecture of Cyber-Physical System Engineering Oriented Intelligent High Performance Simulation Cloud Prototype
		5.3.2 Key Technical Achievements of Cyber-Physical System Engineering Oriented Intelligent High Performance Simulation Cloud Prototype
		5.3.3 Technical Novelty of Cyber-Physical System Engineering Oriented Intelligent High Performance Simulation Cloud Prototype
	5.4 Case Study of Cyber-Physical System Engineering Oriented Intelligent High Performance Simulation Cloud for CPS Engineering (Prototype)
		5.4.1 Based on CPSEO-IHPSC Digital Twins Technology of Intelligent Manufacturing System Application
		5.4.2 Based on CPSEO-IHPSC Digital Twins Technology of Smart City System Case
	5.5 Suggestions on Developing Cyber-Physical System Engineering Oriented Intelligent High Performance Simulation Cloud in the New Era
		5.5.1 Interpretation of “New Internet + Cloud Computing + Big Data + New Artificial Intelligence+”
		5.5.2 Focusing on the Coordinated Development of Technology, Industry, and Application
	References
Part IIMethodology
6 Service Composition and Scheduling in Cloud-Based Simulation
	6.1 Introduction
		6.1.1 The Characteristics of Cloud-Based Simulation
		6.1.2 Related Works
	6.2 A Service Network Model for Simulation Entity Service Composition and Scheduling
		6.2.1 Service and Task Description Models
		6.2.2 Service Network-Based Service Composition and Scheduling Model
		6.2.3 Case Study
	6.3 DDDS-Based Dynamic Service Scheduling in a Cloud Environment
		6.3.1 System Framework
		6.3.2 Scheduling Rules
		6.3.3 DEVS Modeling
		6.3.4 DDDS Strategies
	6.4 Conclusions
	References
7 Agent-Directed Simulation and Nature-Inspired Modeling for Cyber-Physical Systems Engineering
	7.1 Introduction to Cyber-Physical Systems (CPS)
	7.2 Simulation and Its Increasing Importance
		7.2.1 Inputs
		7.2.2 Coupling
	7.3 Agent-Directed Simulation (ADS)
	7.4 Nature-Inspired Modeling and Computing
		7.4.1 Sources of Information
		7.4.2 Categories of Nature-Inspired Models
	7.5 Systems Engineering and Cyber-Physical Systems Engineering
	7.6 Conclusions
	References
8 Composing Cyber-Physical Simulation Services in the Cloud via the DEVS Distributed Modeling Framework
	8.1 Introduction
	8.2 Systems Engineering for Multi-Domain Operations
	8.3 Emerging Technologies to Support Cloud-Based Modeling and Simulation
		8.3.1 Cloud-Based Modeling and Simulation
		8.3.2 Modeling and Simulation as a Service
		8.3.3 Microservices and Domain-Driven Design
		8.3.4 Discrete Event System (DEVS) Distributed Modeling Framework (DMF)
	8.4 Simulation for Multi-Domain Operations
		8.4.1 DSEEP Phase 1—Define Composed Simulation Service Objectives
		8.4.2 DSEEP Phase 2—Perform Conceptual Analysis
		8.4.3 DSEEP Phase 3—Design Composed Simulation Service
		8.4.4 DSEEP Phase 4—Develop Composed Simulation Service
		8.4.5 DSEEP Phase 5—Integrate and Test Composed Simulation Service
		8.4.6 DSEEP Phase 6—Execute Composed Simulation Service
		8.4.7 DSEEP Phase 7—Analyze Data and Evaluate Results
	8.5 Conclusions and Follow-On Research
	References
9 Anticipative, Incursive and Hyperincursive Discrete Equations for Simulation-Based Cyber-Physical System Studies
	9.1 Introduction
	9.2 Presentation Step by Step of the Second-Order Hyperincursive Discrete Harmonic Oscillator
	9.3 The 4 Dimensionless Incursive Discrete Equations of the Harmonic Oscillator
	9.4 The Constants of Motion of the Two Incursive Discrete Equations of the Harmonic Oscillator [33]
	9.5 Numerical Simulations of the Two Incursive Discrete Harmonic Oscillators
	9.6 The Dimensionless Hyperincursive Discrete Harmonic Oscillator Is Separable into Two Incursive Discrete Harmonic Oscillators
	9.7 Numerical Simulations of the Hyperincursive Discrete Equations of the Harmonic Oscillator
	9.8 Rotation of the Incursive Harmonic Oscillators to Recursive Discrete Harmonic Oscillators
	9.9 The Space and Time-Symmetric Second-Order Hyperincursive Discrete Klein–Gordon Equation
	9.10 The Hyperincursive Discrete Majorana Equations and Continuous Majorana Real 4-Spinors
	9.11 The Bifurcation of the Majorana Real 4-Spinors to the Dirac Real 8-Spinors
	9.12 The 4 Hyperincursive Discrete Dirac 4-Spinors Equations
	9.13 The Hyperincursive Discrete Klein–Gordon Equation Bifurcates to the 16 Proca Equations
	9.14 Simulation of the Hyperincursive Discrete Quantum Majorana and Dirac Wave Equations
	9.15 Conclusion
	References
Part IIIApplications
10 Offering Simulation Services Using a Hybrid Cloud/HPC Architecture
	10.1 Introduction
		10.1.1 Web-Based Computing
		10.1.2 Cloud Computing
	10.2 Desired Functionality
	10.3 The HPC Solution
		10.3.1 Security and Resource Management
		10.3.2 Identity Mapping
		10.3.3 Market Support
		10.3.4 Developer Effort
		10.3.5 User Storage
		10.3.6 Resource Usage
		10.3.7 Access to the Command Line and Desktop
		10.3.8 Seamless System Integration
	10.4 OnDemand in a Cloud
		10.4.1 HPC as a Cloud
		10.4.2 Using a Cloud as a Resource Provider
	10.5 Evolving OnDemand
		10.5.1 Building a Front End at OSC
		10.5.2 Working with AweSim
		10.5.3 OSC OnDemand
		10.5.4 The Future of OnDemand
	References
11 Cyber-Physical Systems Design Flow  to Manage Multi-channel Acquisition System for Real-Time Migraine Monitoring and Prediction
	11.1 Introduction
	11.2 Technologies Involved in the Design Flow
		11.2.1 FPGAs and Healthcare Monitoring Systems
		11.2.2 Discrete Event System Specification (DEVS)
		11.2.3 Predictive Models
	11.3 System I: Migraine Predictive Device
		11.3.1 FPGA Implementation
		11.3.2 HW Setup
		11.3.3 Validation
	11.4 System II: DEVS-based Framework to Deploy Cyber-Physical Systems Over IoT Environments
		11.4.1 Framework Design
		11.4.2 From Sensors to the Cloud: Scalability Issues
	11.5 Conclusions
	References
12 Significance of Virtual Battery in Modern Industrial Cyber-Physical Systems
	12.1 Introduction
	12.2 Related Work
	12.3 History of Cell and Batteries
	12.4 Need for Virtual Battery
	12.5 Parameters Impacting a Virtual Battery
	12.6 Designing Virtual Battery
	12.7 Conclusion and Future Work
	References
13 An Architecture for Low Overhead Grid, HPC, and Cloud-Based Simulation as a Service
	13.1 Introduction
	13.2 Applications in Experimental Design and Simulation Optimization
		13.2.1 Classical Design of Experiments in MEG
		13.2.2 Simulation Optimization in MEG
		13.2.3 Response Surface Approximations in MEG
	13.3 MEG Design and Architecture
		13.3.1 Architectural Enhancement
		13.3.2 Experiment Service
		13.3.3 Ouroboros Service
		13.3.4 Job Manager Service
		13.3.5 User Interface [UI] Service and Data Visualization
		13.3.6 Data Services
		13.3.7 MEG Experiment Primer—Ackley Function
	13.4 MEG and Cyber-Physical Systems
	13.5 Summary and Future Work
		13.5.1 MEG Summary and Current Status
	References
Part IVReliability Issues
14 Cloud-Based Simulation Platform for Quantifying Cyber-Physical Systems Resilience
	14.1 Introduction
	14.2 Cyber-Physical Systems (CPS)
		14.2.1 CPS and Other Related Fields
	14.3 Cloud Computing Environment
	14.4 CPS and Cloud Security Concerns
		14.4.1 CPS Security Threats
		14.4.2 Cloud Security Issues
	14.5 Modeling CPS Cyber Resilience Metrics
		14.5.1 Cyber Resilience: Definition and Characteristics
		14.5.2 Common Vulnerability Scoring System (CVSS)
		14.5.3 Vulnerability Graph Model
		14.5.4 Resilience Metrics Formulation
	14.6 Cloud-Based Simulation Platform
		14.6.1 Simulation Platform Architecture
		14.6.2 Simulation Platform Deployment Plan
		14.6.3 Use Case: An AWS-Based Qualitative Simulation Platform for Resilience Assessment
	14.7 Challenges of Cloud-Based CPS Simulation Platform and Way Forward
	14.8 Conclusion
	References
15 Reliability Analysis of Cyber-Physical Systems
	15.1 Introduction
	15.2 On Traditional Reliability in the Context of CPS
	15.3 Holistic Reliability of Cyber-Physical Systems
		15.3.1 Hardware Reliability
		15.3.2 Software Reliability
		15.3.3 Reliability Related to Human Interaction
	15.4 Combined Reliability of CPS
		15.4.1 CPS Reliability Approaches
		15.4.2 Challenges and Opportunities Associated with Reliability of CPS
	15.5 Data-Driven Reliability Analysis of CPS
	15.6 Illustrative Examples (Case Studies)
		15.6.1 Cyber-Physical Production Systems (Smart Factories)
		15.6.2 Smart Buildings
	15.7 Conclusions
	References
16 Dimensions of Trust in Cyber Physical Systems
	16.1 Introduction
		16.1.1 Internet of Things and Cyber Physical Systems
		16.1.2 Internet of Things and Smart Cities
	16.2 Trust
		16.2.1 Definitions
		16.2.2 Types of Trust in IoT Systems
		16.2.3 Trust Architecture
	16.3 Trust Research Strategy
		16.3.1 Physical and Perception Layer
		16.3.2 Network Layer
		16.3.3 Application Layer
	16.4 Simulation Trust
		16.4.1 LVC and IoT
		16.4.2 Internet of Simulation Things
	16.5 Internet of Trusted Simulations
	16.6 Conclusions
	References
17 Ethical and Other Highly Desirable Reliability Requirements for Cyber-Physical Systems Engineering
	17.1 Introduction
	17.2 Cyber-Physical Systems and Cyber-Physical Systems Engineering
	17.3 Systems Engineering Approach for Reliability Issues
	17.4 Reliability of and Failure Avoidance in Computation and in Simulation Studies
		17.4.1 Validity and Verification Issues of Modeling and Simulation
		17.4.2 A Frame of Reference for the Assessment of the Acceptability of Simulation Studies
		17.4.3 Failure Avoidance: Artificial Intelligence (AI)
		17.4.4 Failure Avoidance in or Due to Simulation Studies
	17.5 Aspects of Sources of Failures
		17.5.1 Ethics and Value Systems
		17.5.2 Decision-Making Biases
		17.5.3 Improper Use of Information: Misinformation, Disinformation, and Mal-information
		17.5.4 Attacks (by Humans and Autonomous AI Systems)
		17.5.5 Flaws
		17.5.6 Accidents
		17.5.7 Natural Disasters
	17.6 Conclusion
	References
Index