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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Stevens Institute Series on Complex Systems and Enterprises
- Sprache: Englisch
Offers a one-stop reference on the application of advanced modeling and simulation (M&S) in cyber physical systems (CPS) engineering This book provides the state-of-the-art in methods and technologies that aim to elaborate on the modeling and simulation support to cyber physical systems (CPS) engineering across many sectors such as healthcare, smart grid, or smart home. It presents a compilation of simulation-based methods, technologies, and approaches that encourage the reader to incorporate simulation technologies in their CPS engineering endeavors, supporting management of complexity challenges in such endeavors. Complexity Challenges in Cyber Physical Systems: Using Modeling and Simulation (M&S) to Support Intelligence, Adaptation and Autonomy is laid out in four sections. The first section provides an overview of complexities associated with the application of M&S to CPS Engineering. It discusses M&S in the context of autonomous systems involvement within the North Atlantic Treaty Organization (NATO). The second section provides a more detailed description of the challenges in applying modeling to the operation, risk and design of holistic CPS. The third section delves in details of simulation support to CPS engineering followed by the engineering practices to incorporate the cyber element to build resilient CPS sociotechnical systems. Finally, the fourth section presents a research agenda for handling complexity in application of M&S for CPS engineering. In addition, this text: * Introduces a unifying framework for hierarchical co-simulations of cyber physical systems (CPS) * Provides understanding of the cycle of macro-level behavior dynamically arising from spaciotemporal interactions between parts at the micro-level * Describes a simulation platform for characterizing resilience of CPS Complexity Challenges in Cyber Physical Systems has been written for researchers, practitioners, lecturers, and graduate students in computer engineering who want to learn all about M&S support to addressing complexity in CPS and its applications in today's and tomorrow's world.
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Table of Contents
Cover
Preface
Foreword
About the Editors
List of Contributors
Author Biography
Part I: Introduction
1 The Complexity in Application of Modeling and Simulation for Cyber Physical Systems Engineering
1.1 Introduction
1.2 Multimodal Nature of CPS
1.3 Why CPS Engineering Is Complex?
1.4 M&S Technology Available for CPS Engineering
1.5 Intelligence, Adaptation, and Autonomy Aspects
1.6 Conclusion
Acknowledgments
References
2 Challenges in the Operation and Design of Intelligent Cyber‐Physical Systems
2.1 Introduction
2.2 Connected Autonomous Vehicles
2.3 The Evolution of Physical and Cognitive Faculties in Humans
2.4 The Landscape of Intelligent Cyber‐Physical Systems
2.5 Challenges in System Operation
2.6 Challenges in System Design and Test
2.7 Conclusions
References
3 NATO Use of Modeling and Simulation to Evolve Autonomous Systems
3.1 Introduction
3.2 Autonomous Systems in NATO
3.3 Modeling and Simulation for Autonomous Systems Conference (MESAS)
3.4 Autonomous Systems: Future Challenges and Opportunities
3.5 Conclusion
References
Part II: Modeling Support to CPS Engineering
4 Multi‐Perspective Modeling and Holistic Simulation
4.1 Introduction
4.2 Related Works
4.3 Conceptual Foundations to MPM&HS
4.4 Multi‐Perspective Modeling
4.5 Holistic Simulation
4.6 MPM&HS Process
4.7 Application
4.8 Discussion
4.9 Conclusion
References
5 A Unifying Framework for the Hierarchical Co‐Simulation of Cyber‐Physical Systems
5.1 Introduction
5.2 Related Work
5.3 The HYFLOW Formalism
5.4 Numerical Integration
5.5 Fluid Stochastic Petri‐Nets
5.6 Conclusion
References
6 Model‐Based Systems of Systems Engineering Trade‐off Analytics
6.1 Introduction
6.2 Systems of Systems (SoS), Cyber Physical Systems (CPS), and Internet of Things (IoT)
6.3 Systems of Systems Challenges for Trade‐off Analysis
6.4 Model‐Based Architectures as Framework for SoS Trade‐off Analytics
6.5 Establishing SoS Objectives and Evaluation Criteria
6.6 Evaluating Alternatives
6.7 Summary and Conclusion
Disclaimer
References
7 Taming Complexity and Risk in Internet of Things (IoT) Ecosystem Using System Entity Structure (SES) Modeling
7.1 Introduction
7.2 IoT Definition and Device‐Centric World View
7.3 System Entity Structure (SES) Model
7.4 IoT Model
7.5 Case Study: MIRAI Attack
7.6 Risks in IoT
7.7 Conclusions and Future Work
Acknowledgments
Notice
References
Part III: Simulation-Based CPS Engineering
8 Simulation Model Continuity for Efficient Development of Embedded Controllers in Cyber‐Physical Systems
8.1 Introduction and Motivation
8.2 Background on Relevant Technologies
8.3 DEVS over ROS (DoveR): An Implementation of the Model Continuity‐Based Methodology
8.4 An Experimental Robotic Platform: Hardware and Models
8.5 Experimental Case Study: Developing a Controller with a Model Continuity‐Centered Methodology
8.6 Challenges of Implementing DoveR
8.7 Concluding Remarks and Future Work
Acknowledgments
References
9 Cyber‐Physical Systems Design Methodology for the Prediction of Symptomatic Events in Chronic Diseases
9.1 Introduction
9.2 General Architecture
9.3 Software Model and Physical Implementation
9.4 Energy Consumption and Scalability Issues
9.5 Conclusion
References
10 Model‐Based Engineering with Application to Autonomy
10.1 Introduction
10.2 Background
10.3 Approaches to Model‐Based Engineering
10.4 Modeling and Simulation in Model‐Based Engineering
10.5 Use Case: Velocity Control of Automotive CPS
10.6 Use Case: Domain Specific Modeling Language for CPS Design
10.7 Conclusion and Insight
Acknowledgments
References
Part IV: The Cyber Element
11 Perspectives on Securing Cyber Physical Systems
11.1 Cyber Physical Systems
11.2 CPS Security Challenges
11.3 Challenges and Opportunities for M&S in CPS Security
References
12 Cyber‐Physical System Resilience
12.1 Introduction
12.2 Cyber Resilience: A Glimpse on Related Works
12.3 Cyber‐Physical System Resilience
12.4 Resilience Metrics and Framework
12.5 Qualitative CPS Resilience Metrics
12.6 Quantitative Modeling of CPS Resilience
12.7 Simulation Platform for CPS Resilience Metrics
12.8 Complexities, Challenges, and Future Directions
12.9 Conclusion
Acknowledgment
Disclaimer
References
13 The Cyber Creation of Social Structures
13.1 Introduction
13.2 The Emergence of Cyber Physical Systems
13.3 Distributed Agency: A Language to Describe Multileveled Structures and Agencies
13.4 Social Adaptation: A Natural Extension of Human Adaptation to and Manipulation of Its Environment
13.5 Complexity and Society: Where CPS Fit in the Social Sciences
13.6 CPS Structures: Applications to the Human Realm
13.7 Conclusions
References
Part V: Way Forward
14 A Research Agenda for Complexity in Application of Modeling and Simulation for Cyber Physical Systems Engineering
14.1 Introduction
14.2 Research Challenges Identified Within the Compendium
14.3 Summary and Discussion
Acknowledgments
References
Cyber Physical Systems
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 CPS contributor and the associated M&S paradigm.
Chapter 2
Table 2.1 Behavior at various levels of cognition.
Table 2.2 A classification of behavior in operation.
Chapter 4
Table 4.1 Heterogeneous composition challenges.
Chapter 6
Table 6.1 SoS types.
Table 6.2 Robustness comparison results.
Table 6.3 Network metrics for assessing SoS architectures.
Chapter 7
Table 7.1 Fourteen IoT‐Inclusive System perspectives.
Chapter 9
Table 9.1 Five scenarios for the workload balancing policies.
Chapter 12
Table 12.1 Cyber resilience framework for CPS.
Table 12.2 Values of the random index for small problems (m < 10).
Table 12.3 Notations and parameter description for resilience graph.
Chapter 14
Table 14.1 Observations in traditional and complex systems engineering.
List of Illustrations
Chapter 1
Figure 1.1 CPS landscape.
Figure 1.2 Experimental LVC approach for generating emergence.
Figure 1.3 M&S supported (computational) CPS engineering test‐bed perspectiv...
Chapter 2
Figure 2.1 Paradigm shift from the traditional Model‐Based Design “V” to a M...
Figure 2.2 Intelligent CPS Ensemble Design‐Test‐Operation Prism.
Chapter 3
Figure 3.1 Initial MESAS Objective starting in 2014, Systems with Autonomous...
Figure 3.2 T‐Rex Simulator reproducing combined Cyber and Drone Attack to Cr...
Figure 3.3 IDRASS Simulation Solution within SPIDER Virtual Environment appl...
Chapter 4
Figure 4.1 Hybridization strategies in computational frameworks.
Figure 4.2 CPS from multiple engineering perspectives.
Figure 4.3 Basic entities in M&S and their relationships.
Figure 4.4 General principle of Multi‐Perspective Modeling.
Figure 4.5 General principle of Holistic Simulation.
Figure 4.6 Generic Ontology for Complex Systems M&S (O4CS).
Figure 4.7 Generic model base for complex systems simulation (MB4CS).
Figure 4.8 MPM&HS methodological process.
Figure 4.9 Ontology and conventional models for healthcare systems M&S.
Figure 4.10 Pruning of the ontology for healthcare systems.
Figure 4.11 CPS in healthcare as coordinating systems.
Chapter 5
Figure 5.1
HYFLOW
modular approach to M&S of cyber‐physical systems.
Figure 5.2 Basic
HYFLOW
component trajectories.
Figure 5.3 Pulse integrator input/output trajectories.
Figure 5.4 LC circuit.
Figure 5.5
HYFLOW
LC circuit network.
Figure 5.6 Capacitor voltage and current for a triangular input voltage.
Figure 5.7 FSPN with variable rate |
t
2
|·
c
.
Figure 5.8 FSPN representation of machines subjected to breakdowns.
Figure 5.9
HYFLOW
modular representation of a FSPN transition.
Chapter 6
Figure 6.1 Relationship between SoS, CPS, and IoT (de C Henshaw 2016).
Figure 6.2 Systems of Systems pain points (Dahmann 2014).
Figure 6.3 The SoS wave model (Dahmann et al. 2011).
Figure 6.4 Base Model applied to models of different size (Albro et al. 2017...
Figure 6.5 Illustrative workflow for using the CSV Importer (Albro et al. 20...
Figure 6.6 Notional SVN model for an SoS (Monahan et al. 2018).
Figure 6.7 SoS trade‐off analysis workflow with lightweight analytic tools a...
Figure 6.8 Notional scenario considered for network robustness analysis. The...
Figure 6.9 Considered SoS architectures, where (a) is the baseline, (b) the ...
Figure 6.10 Shared data and models are core goals of the DoD Digital Enginee...
Figure 6.11 SoS architecture model core for integrated SoS digital engineeri...
Chapter 7
Figure 7.1 Device Centric View of an IoT system.
Figure 7.2 SES Application.
Figure 7.3 Notional IoT‐Inclusive System.
Figure 7.4 PES generation through an iterative process.
Figure 7.5 PES for IoT‐Inclusive System.
Figure 7.6 Component‐Entity Structure (CES) from IoT‐Inclusive system PES.
Figure 7.7 IoT‐Inclusive System entity structure (sample depiction).
Figure 7.8 PES of IoT‐Inclusive‐System SES for Mirai System.
Figure 7.9 Information PES for Mirai System PES.
Figure 7.10 Resource PES for Mirai System PES.
Figure 7.11 Domain‐InfoTech PES for Mirai System PES.
Figure 7.12 Orgs PES within the Mirai System PES.
Figure 7.13 Users PES within the Mirai System PES.
Figure 7.14 Combined risk evaluation framework.
Chapter 8
Figure 8.1 Closed‐loop control system diagram. The system output is fed back...
Figure 8.2 Typical activities and stages in a control design pipeline for a ...
Figure 8.3 End‐to‐end model continuity.
Stage 1:
Standalone simulation‐based...
Figure 8.4 Abstraction layers to provide separation of concerns.
Figure 8.5 DoveR for End‐to‐end model continuity.
Stage 1:
Standalone simula...
Figure 8.6 Network message handling at the ROS/PowerDEVS interface. Delivery...
Figure 8.7 sndROS atomic model functions.
Figure 8.8 sndNET method (PowerDEVS Engine).
Figure 8.9 Excerpt of a ROS node for DoveR.
Figure 8.10 PowerDEVS screenshot. Dark gray (receive) and light gray (send) ...
Figure 8.11 TachoBot:
Test Assembly for the Control of Hybrid Objectives
roB...
Figure 8.12 Ideal closed‐loop, composed of: (a) plant (Robot or CPS), (b) co...
Figure 8.13 Closed‐loop response with the simulation‐based designed controll...
Figure 8.14 Closed‐loop response after introducing ROS and network in the lo...
Figure 8.15 Delay introduced by ROS measured as the difference between sent ...
Figure 8.16 Model refinement by parameter tuning (Stage 3): (a) measurements...
Figure 8.17 Embedded simulation (Stage 4): (a) closed‐loop response, and (b)...
Chapter 9
Figure 9.1 Example of a Mobile Cloud Computing network.
Figure 9.2 Design of the architecture of the system.
CERPS
is scalable and t...
Figure 9.3 Elements that compose the
Robust Prediction System
architecture.
Figure 9.4 Example of
Data Driver
architecture. It is shown how one measured...
Figure 9.5
Sensor Status Detector
architecture. It detects data errors and w...
Figure 9.6
Predictor
architecture. SDMS2 is the core of this module. It dete...
Figure 9.7
Decider
architecture. This model generates local alarms based on ...
Figure 9.8 Three examples of core functions for the Decider module. (a) Bina...
Figure 9.9 Hemodynamic variables after synchronization and preprocessing dur...
Figure 9.10 Modeling of subjective pain evolution curve using real data. The...
Figure 9.11 Overview of the implementation of the whole system architecture....
Figure 9.12 Root component of the migraine pruning system implemented in an ...
Figure 9.13 Example of signal repair using Gaussian process machine learning...
Figure 9.14 Root component of the migraine pruning system implemented in an ...
Figure 9.15 Test results: symptomatic periods for one of the trained patient...
Figure 9.16 RMSE of the predictions depending on the number of decimal fixed...
Figure 9.17 3D and 2D views of the Pareto Fronts, result of the optimization...
Figure 9.18 Utilization of the HPC Data Center and Cloud cluster. (a) HPC Da...
Chapter 10
Figure 10.1 Simplified V‐model
Figure 10.2 An abstracted system.
Figure 10.3 Traditional design process.
Figure 10.4 MBE design process.
Figure 10.5 The process of DSML and model design using a DSME (Sprinkle and ...
Figure 10.6 An example of environment modeling in Gazebo.
Figure 10.7 Computational model of an autonomous vehicles; left side shows s...
Figure 10.8 (a) Raytracing of simulated LiDAR sensor in Gazebo World (b) Poi...
Figure 10.9 A state machine with set specified inputs for ACC example, imple...
Figure 10.10 Test setup in simulation based on model based design. ACC input...
Figure 10.11 An example DSML of a simple hybrid control system with verifica...
Chapter 12
Figure 12.1 Generic CPS (ICS) architecture.
Figure 12.2 Categorization of CPS threat and attack types and mapping with r...
Figure 12.3 CPS cyber resilience metrics structural hierarchy.
Figure 12.4 Decomposition of robustness using analytical hierarchy process....
Figure 12.5 Critical assets in different system levels.
Figure 12.6 Attack graph‐based stepping stone modeling.
Figure 12.7 System performance graph in the event of cyber incident.
Figure 12.8 CPS attack scenario illustration using NIST defense‐in‐depth arc...
Figure 12.9 Process flowchart of cyber resilience tool for CPS.
Figure 12.10 Qualitative cyber resilience simulation engine for CPS.
Figure 12.11 Quantitative cyber resilience simulation engine for CPS.
Figure 12.12 A use case example using CPS application in oil and gas industr...
Figure 12.13 Sample qualitative metrics of (a) robustness and (b) resourcefu...
Chapter 13
Figure 13.1 Fuzzy Agents in a complex world.
Figure 13.2 The backwards induction methodology that Distributed Agency prop...
Figure 13.3 Agents use their unique information to position themselves in mu...
Figure 13.4 Overly dominant agents can suboptimize the system by constrainin...
Figure 13.5 Suboptimizing agents can reduce conflict using information from ...
Figure 13.6 Assimilating different information requires dispersing smaller a...
Figure 13.7 More range in subsystems increases a system's capacity to integr...
Figure 13.8 From suboptimization to distributed agency. (a) Depicts an overl...
Cyber Physical Systems
Figure E.1 Doctoral recipients by broad field of study.
Guide
Cover
Table of Contents
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Complexity Challenges in Cyber Physical Systems
Using Modeling and Simulation (M&S) to Support Intelligence, Adaptation and Autonomy
Saurabh Mittal
The MITRE CorporationFairbornOH, USA
Andreas Tolk
The MITRE CorporationHamptonVA, USA
This edition first published 2020© 2020 John Wiley & Sons, Inc.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Saurabh Mittal and Andreas Tolk to be identified as the authors has been asserted in accordance with law.
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Library of Congress Cataloging‐in‐Publication Data
Names: Mittal, Saurabh, author. | Tolk, Andreas, author.Title: Complexity challenges in cyber physical systems : using modeling and simulation (M&S) to support intelligence, adaptation and autonomy / Dr. Saurabh Mittal, The MITRE Corporation, McLean, OH, US, Dr. Andreas Tolk, The MITRE Corporation, Hampton, VA, US.Description: First edition. | Hoboken, NJ : Wiley, 2020. | Series: Stevens institute series on complex systems and enterprises | Includes bibliographical references and index.Identifiers: LCCN 2019027671 (print) | LCCN 2019027672 (ebook) | ISBN 9781119552390 (cloth) | ISBN 9781119552468 (adobe pdf) | ISBN 9781119552499 (epub)Subjects: LCSH: Cooperating objects (Computer systems) | Automatic control–Simulation methods.Classification: LCC TJ213 .M5335 2020 (print) | LCC TJ213 (ebook) | DDC 006.2/2–dc23LC record available at https://lccn.loc.gov/2019027671LC ebook record available at https://lccn.loc.gov/2019027672
Cover Design: WileyCover Images: © v_alex/Getty Images, © wayra/Getty Images
To the Infinite Intelligence that created things simple, just,and accommodating enough, which manifests itself incomplex universes, both within and without, that we allshare, enjoy, and strive to understand.
Saurabh Mittal
To all scientists and researchers who dare to leave thecomfort of their home discipline and seek collaboration withlike‐minded partners to create transdisciplinary teamsinspiring progress in our complex work.
Andreas Tolk
Preface
The various definitions for Cyber Physical Systems (CPSes) all focus on their computational and physical components, integrating sensors, networks, motors, and more. But we often overlook that CPS will significantly change the way we access systems and our environment. They are ubiquitous: cars self‐park, recognize street signs and react accordingly, know the distance to other cars and keep the correct distance, and more. CPS allows a new family of medical devices, from surgical assisting tools to smart prostheses. Smart houses observe the comfort level of people and control the air conditioning accordingly. They are learning when people are home, can prepare their meals and keep the meals warm in case of a traffic jam. If the house were part of the smart city, sensors would have learned about the jam and diverted the traffic, automatically reconfiguring the traffic lights and communicating the news to the smart cars. First responders as well as soldiers are getting accustomed to their colleague CPS in human cyber teams, where the CPS can assess regions too dangerous or otherwise not reachable for the human team partners. However, all this support comes with a price: growing complexity! How can we either manage or govern such intelligent, adaptive, and autonomous systems? How can we take advantage of positive emergence, and avoid the major consequences of negative ones?
We went through a similar dramatic change before, namely when the Internet changed our view on searching for and gaining access to information. Many CPSes are using the Internet to gather and change information as well; and again, it comes with a price. Before the Internet era, many complex systems had both the software and hardware components, but they were shielded from cyberattacks due to a lack of network access. Due to the additional capability of connectedness of these components across varied networks (both within and without the organization that own the CPS), new challenges have emerged. Some of the challenges include cyber security, control, test, degree of connectivity, constant vigilance and operation, degree of autonomy, intelligence‐based behavior, resilience, and impact on the socioeconomic fabric.
As is the case in many current publications, CPS and Internet of Things (IoT) are used interchangeably but there are some subtle differences between the two. We understand CPS as domain‐specific versions of IoT, so the difference lies in terms of scale, societal impact and the propagation of effects. CPS are more focused towards a specific domain such as aviation, health, military, defense, manufacturing, etc. Due to the domain‐specific nature, CPS can be studied in more detail at both the operational technology and information technology levels. However, the resulting danger is that CPS within their domains share neither their insights nor benefit from insights of other CPS from other domains. A domain‐agnostic common theory providing common methods that lead to domain specific solutions would be advantageous, and some candidates exist and will be discussed, but no common formalism in support of this idea has been widely accepted yet.
Modeling and Simulation (M&S) has emerged as a mechanism by which various CPS challenges can be studied in a virtual environment. Model‐based engineering (MBE) and simulation‐based engineering are two distinct activities, even though the simulation activity subsumes the modeling activity. A model is an abstract representation of the system and is evaluated in an environment that may be a live (people using real systems), a virtual (people using simulated systems), or a constructive (simulated people and systems) environment. The simulation infrastructure ensures the model system is provided the right environment for evaluating the capabilities, which are essentially the system's capabilities that need to be tested and evaluated.
We started our journey in Fall 2017, when we gratefully received some internal MITRE research funding to research the challenges of hybrid simulation in support of CPS. We allocated part of the funding to bring experts to a panel discussion. The experts belonged to disparate domains who employ M&S to address the CPS challenges and conduct CPS engineering together. Interestingly, this spawned some collaboration, as we discovered similarities in our challenges and solutions, from which this book ultimately emerged. This book tries to organize the obtained insights and report the latest in the use of M&S for CPS engineering. We address the subject in five parts: Introduction, Modeling Support to CPS Engineering, Simulation‐Based CPS Engineering, The Cyber Element, and The Way Forward.
The Part I begins with a chapter from us and provides an overview of complexities associated with the application of M&S to CPS Engineering. Castro et al. in the second chapter, provide a more detailed description of the challenges in the operation and design of intelligent CPS. The third chapter by Mazal et al. discusses M&S in the context of autonomous systems involvement within the North Atlantic Treaty Organization (NATO). Part II begins with a chapter from Traoré on multi‐perspective modeling and holistic simulation for very complex systems analysis. The next chapter by Barros describes a unifying framework for hierarchical co‐simulation of CPS. This is followed by model‐based system of systems engineering tradeoff analytics by Markina‐Khusid et al. The next chapter by Mittal et al. considers a larger version of CPS, i.e. IoT, and the complexities associated with developing a risk assessment framework. Part III begins with a chapter from Castrol et al. on simulation model continuity for efficient development of embedded controllers in CPS. This is followed by another practical application by Henares et al. on CPS design methodology for prediction of symptomatic events in chronic diseases. In this chapter they present the entire lifecycle methodology for CPS engineering from concept to cloud deployment and execution. The next chapter by Bhadani et al. applies model‐based engineering to the subject of autonomy in CPS. Part IV begins with a chapter by Furness on providing various perspectives on securing CPS. This is supported by the next chapter by Haque et al. on CPS resilience and discusses frameworks, complexities, and future directions on resilient systems engineering. The next chapter by Suarez and Demareth discusses the creation of social structures employing CPS. The Part V incorporates another chapter by the editors on the way forward and provides a research agenda for addressing complexity in application of M&S for CPS engineering.
Editing this book was a rewarding journey that offered plenty of opportunities to learn and discover. We invite you to share the exciting journey of CPS engineering that offers a wealth of opportunities for advancement at various levels. CPS are going to shape our lives: observing the well‐being of the elderly, observing our health, observing and optimizing our production systems, and many more opportunities. Just as our children can hardly imagine finding information on certain topics of interest – mainly due to home work or college projects – before Internet and Google, the new generation may no longer imagine how often we had to practice parallel parking, or how parcels were delivered only once a day. We hope to contribute to the efficient development of future CPS solutions with this compendium, and hopefully generate some ideas for scholars and researchers as well.
Saurabh Mittal1, PhDThe MITRE Corporation,Fairborn, OH, USA
Andreas Tolk2, PhDThe MITRE Corporation,Hampton, VA, USA
Notes
1
,
2
The author's affiliation with the MITRE Corporation is provided for identification purposes only, and is not intended to convey or imply MITRE's concurrence with, or support for, the positions, opinions, or viewpoints expressed by the author. Approved for Public Release, Distribution Unlimited. Case: PR_18-2996-3.
Foreword
Several important global trends are occurring with regards to the advancement of cyber physical systems. Worldwide, significant technology‐driven advances are being pursued that address increasing cyber physical system performance, safety, and security while achieving design, development, and operational efficiencies that reduce cost. These trends include:
Significant investment in higher levels of automation for physical systems, including autonomous systems.
Increasing research and early applications of Artificial Intelligence to physical systems (AI), including addressing “Dependable AI” for high assurance AI‐software design and development.
Development of advanced static and dynamic analysis tools by the Modeling and Simulation community. The resulting Model‐based Systems Engineering (MBSE) analysis tools and methods address considerations related to the growing complexity of highly integrated System‐of‐System architectures.
Development of cyberattack resilient system architectures that can restore acceptable system operation in response to a real‐time detection of a functionally disabling cyberattack.
These initiatives bring with them increased complexity of system designs, with a corresponding set of risks that need to be addressed when designing new or significantly upgraded systems. These risks include:
Cyberattacks that include supply chain and insider attacks that can directly impact the application layer of physical systems and, in the worst case, can potentially result in operator or user injuries or loss of life.
Safety‐related incidents due to undetected deficiencies in system design.
Operator errors due to uncertainties related to human–machine roles under anomalous circumstances.
But, perhaps the most concerning risk is the recognized shortage of engineers and scientists who can contribute to the development of these new technologies and tools, as well as the shortage of the workforce that can productively employ the analysis tools that are designed to enable high productivity in the development and evaluation of new cyber physical system designs. This book helps to address this risk by providing a well‐constructed, selective set of articles that together offer the reader an integrated view of the state‐of‐the‐art in addressing complex cyber physical system design and development. By integrating the diverse set of articles, the book serves to compliment the education curriculums at Universities, which tends to separate the subjects discussed above into the curriculums of different departments (e.g. Mechanical Engineering for physical systems, Computer Science for AI and cybersecurity, Systems Engineering for complex system design analysis, etc.). As a result, I believe that books of this kind can play a significant role in enabling engineers to build on their formal education and prior experience in a manner that supports the greatly needed enhanced design and evaluation skills that the trends in cyber physical systems are calling for.
Reading this book is something that I highly recommend for engineers and scientists who are interested in becoming important participants in the global trends related to advancing the automation levels of cyber physical systems!
Barry Martin HorowitzMember of the National Academy of EngineeringMunster Professor Systems and Information EngineeringUniversity of Virginia
Previously CEO of The MITRE Corporation
Previously Virginia Cybersecurity Commissioner
March 2019
About the Editors
SAURABH MITTAL is Chief Scientist for Simulation, Experimentation, and Gaming Department at The MITRE Corporation in Fairborn, OH, Vice President‐Memberships and member of Board of Directors for Society of Modeling and Simulation (SCS) International in San Diego, CA. He holds a PhD and MS in Electrical and Computer Engineering with dual minors in Systems and Industrial Engineering, and Management and Information Systems from the University of Arizona, Tucson. He has co‐authored over 100 publications as book chapters, journal articles, and conference proceedings including 3 books, covering topics in the areas of complex systems, system of systems, complex adaptive systems, emergent behavior, modeling and simulation (M&S), and M&S‐based systems engineering across many disciplines. He serves on many international conference program/technical committees, as a referee for prestigious scholastic journals and on the editorial boards of Transactions of SCS, Journal of Defense M&S and Enterprise Architecture Body of Knowledge. He is a recipient of Herculean Effort Leadership award from the University of Arizona, US DoD's highest civilian contractor recognition: Golden Eagle award, and Outstanding Service and Professional Contribution awards from SCS.
ANDREAS TOLK is a Senior Divisional Staff Member at The MITRE Corporation in Hampton, VA, and adjunct Full Professor at Old Dominion University in Norfolk, VA. He holds a PhD and MSc in Computer Science from the University of the Federal Armed Forces of Germany. His research interests include computational and epistemological foundations and constraints of modeling and simulation as well as mathematical foundations for the composition of model‐based solutions in computational sciences. He published more than 250 peer reviewed journal articles, book chapters, and conference papers, and edited 10 textbooks and compendia on Modeling and Simulation and Systems Engineering topics. He is a Fellow of the Society for Modeling and Simulation and Senior Member of IEEE and the Association for Computing Machinery.
List of Contributors
Jose L. AyalaComplutense University of MadridMadridSpain
Fernando J. BarrosDepartment of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
Rahul BhadaniDepartment of Electrical and Computer EngineeringUniversity of ArizonaTucsonAZUSA
Marco BiaginiNATO Modelling & Simulation Center of Excellence (M&S COE)Italy
Agostino BruzzoneGenoa UniversityGenoaItaly
Matt BuntingDepartment of Electrical and Computer EngineeringUniversity of ArizonaTucsonAZUSA
Sheila A. CaneQuinnipiac UniversityHamdenCTUSA
Sebastian CastroMathWorksNatickMAUSA
Rodrigo CastroDepartamento de Computación, FCEyNUniversidad de Buenos Aires and Instituto de Ciencias de la Computación, CONICETBuenos AiresArgentina
Fabio CoronaNATO Modelling & Simulation Center of Excellence (M&S COE)Italy
Judith DahmannThe MITRE CorporationMcLeanVAUSA
Loren DemerathDepartment of SociologyCentenary College of LouisianaShreveportLAUSA
Zach FurnessINOVA Health SystemsSterlingVAUSA
Juan I. GiribetDepartamento de Ingeniería Electrónica y Matemática, FIUBAUniversidad de Buenos Aires, and Instituto Argentino de Matemática Alberto Calderón, CONICETBuenos AiresArgentina
Md Ariful HaqueComputational Modeling and Simulation EngineeringOld Dominion UniversityNorfolkVAUSA
Richard B. HarrisThe MITRE CorporationMcLeanVAUSA
Kevin HenaresComplutense University of MadridMadridSpain
Ryan JacobsThe MITRE CorporationMcLeanVAUSA
Jason JonesNATO Modelling & Simulation Center of Excellence (M&S COE)Italy
Bheshaj KrishnappaRisk Analysis and MitigationReliabilityFirst CorporationClevelandOHUSA
Ezequiel Pecker MarcosigDepartamento de Ingeniería Electrónica, FIUBAUniversidad de Buenos Aires and Instituto de Ciencias de la Computación, CONICETBuenos AiresArgentina
Aleksandra Markina‐KhusidThe MITRE CorporationMcLeanVAUSA
Jan MazalNATO Modelling & Simulation Center of Excellence (M&S COE)Italy
Saurabh MittalThe MITRE CorporationFairbornOHUSA
Pieter J. MostermanMathWorksNatickMAUSA
Josué PagánTechnical University of MadridMadridSpain
Akshay H. RajhansMathWorksNatickMAUSA
José L. Risco‐MartínComplutense University of MadridMadridSpain
Charles SchmidtThe MITRE CorporationMcLeanVAUSA
Sachin ShettyComputational Modeling and Simulation EngineeringOld Dominion UniversityNorfolkVAUSA
Jonathan SprinkleDepartment of Electrical and Computer EngineeringThe University of ArizonaTucsonAZUSA
E. Dante SuarezSchool of Business, Department of Finance and Decision SciencesTrinity UniversitySan AntonioTXUSA
Andreas TolkThe MITRE CorporationHamptonVAUSA
Mamadou K. TraoréIMS UMR CNRSUniversity of BordeauxBordeauxFrance
John TufaroloResearch Innovations, Inc.AlexandriaVAUSA
Michele TuriNATO Modelling & Simulation Center of Excellence (M&S COE)Italy
Marina ZapaterSwiss Federal Institute of Technology LausanneLausanneSwitzerland
Author Biography
Aleksandra Markina‐Khusid is a Principal Systems Engineer in The MITRE Corporation Systems Engineering Technical Center, supporting several SoS modeling efforts for DoD and DHS. She is the MITRE Model Based Engineering Capability Area Team leader. Dr. Markina‐Khusid holds a BS degree in Physics, MS and PhD degrees in Electrical Engineering, and an MS in Engineering & Management, all from the Massachusetts Institute of Technology.
Agostino G. Bruzzone is Full Professor at DIME University of Genoa, Director of M&S Net (International Network involving 34 Centers) Director of the MISS McLeod Institute of Simulation Science – Genoa Center (over 28 Centers distributed worldwide) founder member and president of the Liophant Simulation, he served as Vice President and Member of the Board of MIMOS (Movimento Italiano di Simulazione), member of the NATO MSG, Executive VP of the Society for Modeling and Simulation International. He works on innovative modeling, AI techniques, application of Neural Networks, GAs and Fuzzy Logic to industrial plant problems using Simulation and Chaos Theory. He is member of several International Technical and Organization Committees (i.e. AI Application of IASTED, AI Conference, ESS, AMS) and General Coordinator of Scientific Initiatives (i.e. General Chair of SCSC and I3M). He teaches “M&S” for the DIMS PhD Program (Doctorship in Integrated Mathematical M&S). He is Director of the Master Program in Industrial Plants & Technologies for the University of Genoa and founder and chair of STRATEGOS International MSc in Engineering Technologies for Strategy and Security (http://www.itim.unige.it/strategos). He served as Project Leader for the NATO Science & Technology Organization at the Centre for Maritime Research and Experimentation (CMRE) founding the new research track on Modeling and Simulation. He has been the 10th scientist worldwide to enter into the Modeling and Simulation Hall of Fame as top Lifetime Achievement Awards of the Society for Modeling and Simulation International.
Akshay H. Rajhans is Principal Cyber‐Physical Systems Research Scientist at MathWorks in the Advanced Research & Technology Office, where his research focuses on technical computing for and model‐based design and analysis of cyber‐physical systems (CPS). Previously, he worked on research and development and application engineering of electronic control systems for diesel‐engine applications at Cummins, and invented a model‐based approach to non‐intrusive load monitoring at Bosch Research and Technology Center. Dr. Rajhans has been involved in leadership capacities in top research conferences in CPS and modeling and simulation communities, including as the inaugural CPS Track Chair at both the Winter Simulation Conference (2017) and the Spring Simulation Conference (2019), and as a Co‐Chair of the International Workshop on Monitoring and Testing of CPS (2019). He is a recipient of the 2011 IEEE/ACM William J. McCalla Best Paper Award and his work has been recognized as a Research Highlight in “Communications of the ACM,” ACM's flagship magazine. Dr. Rajhans has a PhD in Electrical and Computer Engineering from Carnegie Mellon University and an MS in Electrical Engineering from the University of Pennsylvania. He is a member of IEEE and ACM.
Bheshaj Krishnappa is currently working as a Principal at ReliabilityFirst Corporation. Mr. Krishnappa is responsible for risk analysis and mitigation of threats to bulk power system reliability and security across a large geographic area in the United States. He has over 22 years of professional experience working for large‐ and mid‐sized companies in senior roles implementing and managing information technology, security, and business solutions to achieve organizational objectives. He is a business graduate knowledgeable in sustainable business practices that contribute to the triple bottom line of social, environmental, and economic performance. He is motivated to apply his vast knowledge to achieve individual and organizational goals in creating a sustainable positive impact.
Charles Schmidt is a Group Lead at The MITRE Corporation. He has over 17 years of experience in cybersecurity, security automation, and standards development. He holds a BS in Mathematics and Computer Science from Carleton College and an MS in Computer Science from the University of Utah.
Ezequiel Pecker Marcosig received a degree in Electronic Engineering from the Faculty of Engineering (FIUBA) of the University of Buenos Aires, Argentina. He is currently a PhD student at FIUBA and ICC‐CONICET working on modeling and simulation based design of hybrid controllers for cyber‐physical systems. His work is supported with a PhD Fellowship from the Peruilh Foundation. Since 2013, he is a Teaching Assistant in the Department of Electronic Engineering at FIUBA in the area of Automatic Control. His academic interests include automatic control, cyber‐physical systems, modeling and simulation, and hybrid systems.
E. Dante Suarez is Associate Professor for Finance and Decision Sciences at Trinity University, USA. He holds PhD and MS in Economics from Arizona State University, USA. Suarez's main research field is international finance, where he studies the integration of international financial markets, such as the relationship between American depositary receipts and their corresponding underlying stocks. This research is aimed at understanding how markets around the world interact with each other in this age of globalization. Other research areas include econometrics, European studies and Latin American business practices.
Fabio Corona is employed in the Concept Development and Experimentation Branch (CD&E) at NATO Modelling & Simulation Centre of Excellence in Rome. His primary interests at the Centre are emerging technologies and concepts regarding Autonomous System and M&S as a Service. He earned a PhD degree in Electrical Engineering from “Politecnico di Torino” and a MSc degree in Electronics Engineering from “Roma Tre” University. After joining the Italian Army, his employment ranged from the internetworking field under the Italian Army Signal Headquarter to the maintenance and procurement of optoelectronic and communication systems under the Italian Army Logistics Headquarter. During his PhD study, the main research field was in efficiency and power quality of photovoltaic systems under mismatching conditions.
Fernando Barros is a Professor in the Department of Informatics Engineering at the University of Coimbra. He holds a PhD in Electrical Engineering from the University of Coimbra. His research interests include theory of modeling and simulation, hybrid systems and dynamic topology models. He has published more than 80 contributions to journals, book chapters, and conference proceedings. Fernando Barros is a member of IEEE.
Jan Mazal is graduate of the Faculty of Military Systems Management of the Military College of Ground Forces in Vyskov. In 2003 he graduated the Academic Course of Military Intelligence in Fort Huachuca, Arizona, USA. Since 2005 he is a doctor in the field of the theory of the defence management and since 2013 he is Associate professor in the problematic of military management and C4ISR systems. He is former deputy chief of the Department of Military Management and Tactics at the University of Defence in Brno, currently he works as Doctrine Education and Training Branch Chief at NATO Modelling & Simulation Centre of Excellence in ROME. He is focused on the issue of military intelligence and reconnaissance, C4ISR systems, and Operational Decision Support. He is the author and co‐author of more than 70 professional publications, he solved more than 10 scientific projects, and he is the author of a number of functional samples and application software. In his previous military practice, he held command and staff functions at the tactical level and also he took part in the foreign missions as EUFOR (2006) and ISAF (2010).
Jason M. Jones is the Deputy Director for the NATO Modelling & Simulation Centre of Excellence. He has been a U.S. Army Functional Area 57, Simulations Operations Officer, since 2003 and has worked in all aspects of simulations: training, planning, world‐wide simulation distribution, testing, and experimentation. Areas of expertise include: live and constructive training; missile defense and logistics simulation; and knowledge management. He has a Master's Degree in Modeling, Virtual Environments, and Simulations from the Naval Postgraduate School in Monterey, CA, where his thesis examined the use of commercial gaming software for training infantry squads.
John Tufarolo is the Technical Director for Systems Engineering at Research Innovations. He holds a BS in Electrical Engineering from Drexel University, and an MS in Systems Engineering from George Mason University, and has more than 32 years of experience providing systems engineering project work, planning, and leadership in complex distributed systems.
Jonathan Sprinkle is the Litton Industries John M. Leonis Distinguished Associate Professor of Electrical and Computer Engineering at the University of Arizona. In 2013 he received the NSF CAREER award, and in 2009, he received the UA's Ed and Joan Biggers Faculty Support Grant for work in autonomous systems. His work has an emphasis for industry impact, and he was recognized with the UA “Catapult Award” by Tech Launch Arizona in 2014, and in 2012 his team won the NSF I‐Corps Best Team award. His research interests and experience are in systems control and engineering, and he teaches courses ranging from systems modeling and control to mobile application development and software engineering.
José L. Ayala got his PhD in Electronic Engineering from Technical University of Madrid and is currently an Associate Professor in the Department of Computer Architecture and Automation at Complutense University of Madrid. During his career, he has collaborated and performed research stays in University of California in Irvine, University of California in Los Angeles, EPFL, and University of Bologna. He is currently the VP New Initiatives of the IEEE Council of Electronic Design Automation; CEDA representative in IEEE IoT initiative and IEEE Smart Cities initiative; and steering committee of several international conferences (IEEE Smart Cities Conference, IEEE GLSVLSI, VLSI‐SoC, PATMOS, IEEE ASAP, etc). His research interests focus on IoT and edge solutions for personalized medicine approaches, including health monitoring, wireless sensor networks, and disease modeling.
José L. Risco‐Martín is Associate Professor at Complutense University of Madrid. He is head of the Department of Computer Architecture and Automation. Previously, he was Assistant Professor at Colegio Universitario de Segovia and Assistant Professor at C.E.S. Felipe II de Aranjuez. Dr. Risco‐Martín served as General Chair for SummerSim'17, Program Chair for SummerSim'15, General Chair for Summer Computer Simulation Conference 2016 and Vice General Chair for SummerSim'16. He has co‐authored more than 100 articles in various international conferences and journals. His research interests focus on design methodologies for integrated systems and high‐performance embedded systems, including new modeling frameworks to explore thermal management techniques for Multi‐Processor System‐on‐Chip, dynamic memory management and memory hierarchy optimizations for embedded systems, Networks‐on‐Chip interconnection design, low‐power design of embedded systems and more generally Computer‐Aided Design in M&S of Complex System, with emphasis on DEVS‐based methodologies and tools.
Josué Pagán is a Teaching Assistant Professor in the Universidad Politécnica de Madrid. He got his PhD with honors in Computer Science at Complutense University of Madrid in 2018. His work focuses on develop robust methodologies for information acquisition in biophysical and critical scenarios. He has worked developing models for prompt prediction and classification of neurological diseases. On summer 2016 he did a 12‐week research stay at the Embedded Pervasive Systems Lab at Washington State University under the supervision of Prof. Hassan Ghasemzadeh. Previously, on fall 2015 he did a 16‐week research stay at the Pattern Recognition Lab. at Friedrich Alexander University under the supervision of Prof. Bjoern Eskofier. He achieved his MSc at Universidad Politécnica de Madrid in September 2013 with Honor Mention. He also achieved a Bachelor in Telecommunication Engineering by the Universidad Publica de Navarra in 2010.
Juan I. Giribet is a Researcher in the Instituto Argentino de Matemática (IAM‐CONICET), Argentina. He is also Associate Professor in the Faculty of Engineering of the University of Buenos Aires, Argentina. He holds an MS and PhD in Electronic Engineering from the University of Buenos Aires. He published more than 70 contributions to journals and conference proceedings in topics covering electronic engineering and applied mathematics. He is Director of the master's program in Engineering mathematics at University of Buenos Aires. He is a senior member of IEEE.
Judith Dahmann is a Principal Senior Scientist in the MITRE Corporation Center for The MITRE Systems Engineering Technical Center and the Capability Action Team leader for Systems of Systems (SoS). Dr. Dahmann holds a Bachelor’s Degree from Chatham College in Pittsburgh, PA (1972), spent a year as a special student at Dartmouth College (1971–1972), a Master's Degree from The University of Chicago (1973), and a Doctorate from Johns Hopkins University (1984). Dr. Dahmann is an INCOSE Fellow and the co‐chair of the INCOSE Systems of Systems Working Group and the DoD liaison and co‐chair of the National Defense Industry Association SE Division SoS SE Committee.
Kevin Henares is a PhD student at the Complutense University of Madrid (UCM). He received an MSc in Computer Engineering in the same university (Spain, 2018) and a University Degree in Computer Engineering at the University of Vigo (Spain, 2016). His work focuses on the development of robust modeling and simulation methodologies to study the behavior of complex systems, and the generation of models to classify and predict critical events in neurological diseases. His email address is [email protected].
Loren Demarath is Professor and Chair of the Sociology Department at Centenary College of Louisiana. He is currently working on information processing theory and model of emergence and complexity. He is the author of numerous publications examining how the evolution of meaning is guided by aesthetic responses to order. His book, Explaining Culture: The Social Pursuit of Subjective Order, describes the emergent nature of culture.
Marina Zapater is a Post‐Doctoral researcher in the Embedded Systems Laboratory (ESL) at Ecole Polytechinique Federale de Lausanne (EPFL), Switzerland, since 2016. She was non‐tenure‐track Assistant Professor in the Computer Architecture Department of Universidad Complutense de Madrid (UCM), Spain, in the academic year 2015–2016. She received her PhD degree in Electronic Engineering from Universidad Politécnica de Madrid, Spain, in 2015, and an MSc in Telecommunication Engineering and a MSc in Electronic Engineering, both from Universitat Politècnica de Catalunya (UPC), Spain, in 2010. Her research interests include thermal and power optimization of heterogeneous architectures, and energy efficiency in data centers. In this area, she has co‐authored over 50 publications in top‐notch international conferences and journals, and she has participated in several international research projects, including five European H2020 projects. She is IEEE member, and the current Young Professionals representative of IEEE CEDA. She has served as TPC of several conferences, including DATE, ISLPED, and VLSI‐SoC.
Mamadou K. Traoré is Full Professor at University of Bordeaux in France. He holds an MS and PhD in Computer Science from the Blaise Pascal University in Clermont‐Ferrand, France. His contributions are in formal specifications, symbolic manipulation and automated code synthesis of simulation models. He received the International DEVS M&S Award in 2011. He is a member of ACM and SCS.
Marco Biagini is the Concept Development and Experimentation (CD&E) Branch Chief at NATO Modelling & Simulation Centre of Excellence. He has a PhD in Mathematics, Engineering, and Simulation and master degrees in strategic studies, peace keeping and security studies, and new media and communication. He has more than 15 years of experience in the M&S field. He was Battalion Commander at the Italian Army Unit for Digitization Experimentation (USD) and Section Chief at the Italian Army Simulation and Validation Centre. He is chairing the NATO M&S Group (NMSG) 150, and member of NMSG 145, NMSG 136, and NMSG 147.
Matt Bunting is a PhD student in the Department of Electrical and Computer Engineering at the University of Arizona. He earned his BS (2010) in Electrical Engineering from the University of Arizona. His research interests include the study of modeling techniques for domain‐specific modeling languages for development of Cyber‐Physical Systems. Mr. Bunting is also a co‐founder of the Safkan Health medical device company.
Md Ariful Haque is a PhD student in the Department of Modeling Simulation and Visualization Engineering at Old Dominion University. He is currently working as a graduate research assistant in the Virginia Modeling Analysis and Simulation Center. He has earned Master of Science (MS) in Modeling Simulation and Visualization Engineering from Old Dominion University (ODU) in 2018. He has received Master of Business Administration (MBA) degree from the Institute of Business Administration (IBA), University of Dhaka in 2016. He holds a BS degree in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology in 2006. Before joining Old Dominion University as a graduate student, he has worked in the Telecommunication industry for around seven years. His research interests include but not limited to cyber‐physical system security, cloud computing, machine learning, and Big data analytics.
Michele Turi is a Colonel of Italian Army and he is currently in charge as Director of the NATO M&S Center of Excellence. He has a PhD from Genoa University on Engineering, Mathematics, and Simulation PhD program and long experience in using M&S in operational environment. He started his career as mechanized Artillery Officer, covering roles in the ranks of Captain and Major also as Chief of Brigade Intelligence Section, Security Officer, Training Officer and C3I‐Computer & ICT Section. He gained further experience in the Command & Control and Military Decision Making Process when he was on duty at the IT – Army Staff College as C4 Military Teacher. He participated to the Project Management Working Group dedicated to the training simulation both for Live and Constructive Simulation systems to develop the Italian Army Constructive Simulation Centers and Live MOUTs & CTCs. He was involved in the working group for studies, projects, training programs, and simulation systems evaluation related to the Training Simulation, M&S, C2 systems interoperability and the VV&A process for concepts and systems in use and also for the future development. During his permanence at the IT Army M&S Section, he has been responsible to manage, perform, and apply the Army M&S policy; managing, assembling, and testing the first experimental Integrated Test Bed cell; coordinate the resources allocated for the International Working Group responsible to define deployable functional solutions, designing, adopting, and implementing system interoperability test plans' for the Italian Army side's of Afghan Mission Network; use project management methodologies, procedures and techniques to develop projects, plans, and define resources and management R&D projects' in the M&S branch. His areas of expertise include among the others Information Technology, C3I, M&S, VV&A, CD&E, Training Simulation, and Intelligence.
Pieter J. Mosterman is Chief Research Scientist and Director of the Advanced Research & Technology Office at MathWorks in Natick, Massachusetts, where he works on computational methodologies and technologies for technical computing and model‐based design tools. He also held an Adjunct Professor position at the School of Computer Science of McGill University. Prior to this, he was a research associate at the German Aerospace Center (DLR) in Oberpfaffenhofen. He earned his PhD in Electrical and Computer Engineering from Vanderbilt University in Nashville, Tennessee, and his MSc in Electrical Engineering from the University of Twente, the Netherlands. Dr. Mosterman developed the Electronics Laboratory Simulator that was nominated for The Computerworld Smithsonian Award by Microsoft Corporation in 1994. In 2003, he was awarded the IMechE Donald Julius Groen Prize for his paper on the hybrid bond graph modeling and simulation environment {\sc HyBrSim}. In 2009, he received the Distinguished Service Award of The Society for Modeling and Simulation International (SCS) for his services as editor in chief of SIMULATION: Transactions of SCS. Dr. Mosterman was guest editor for special issues on computer automated multiparadigm modeling of SIMULATION, IEEE Transactions on Control Systems Technology, and ACM Transactions on Modeling and Computer Simulation.
Rahul Bhadani is a PhD student in the Department of Electrical and Computer Engineering at the University of Arizona. He earned his BS (2012) from Bengal Engineering and Science University and MS (2017) in Computer Engineering from the University of Arizona. His research interests include modeling, simulation, and control of autonomous vehicles, developing novel statistical models for traffic simulation and software engineering. Prior to joining the University of Arizona, Mr. Bhadani worked as a software engineer for Oracle.
Richard B. Harris is a principal cybersecurity policy engineer for the Homeland Security Center, MITRE. He has over 14 years of experience in cybersecurity with the Department of Homeland Security and MITRE, and a perspective on complex risk environments that was seasoned by a 26 year career in the US Marine Corps.
Roberto G. Valenti is a Senior Robotics Research Scientist at MathWorks in the Advanced Research & Technology Office. His research interests include robotics, robotics sensing for navigation, sensor fusion, mobile autonomous robots (self‐driving cars, unnamed aerial vehicles), inertial navigation and orientation estimation, control, computer vision, and deep learning. Previously, he worked as a research and development engineer within the autonomous driving team at Nvidia. He obtained a PhD in Electrical Engineering at the City University of New York, The City College, NY, USA where he focused his research on state estimation and control for autonomous navigation of micro aerial vehicles. Dr. Valenti received his MSc in Electronics Engineering from the University of Catania, Italy. He is a member of IEEE and the Robotics and Automation Society (RAS).
Rodrigo D. Castro is a Researcher with the Instituto de Ciencias de la Computación (ICC‐CONICET) and Associate Professor in the Computer Science Department, School of Exact and Natural Sciences of the University of Buenos Aires (UBA), Argentina. Rodrigo holds a degree in Electronic Engineering and a PhD in Engineering from the National University of Rosario (Rosario, Argentina). He was Postdoc Associate at the Swiss Federal Institute of Technology in Zurich (ETH Zurich), Switzerland (Department of Environmental Systems Science and Computer Science). He heads the Laboratory of Discrete Events Simulation and is a member of the Society for Modeling and Simulation international (SCS) and the IEEE.
Ryan B. Jacobs is a Group Leader at The MITRE Corporation Systems Engineering Technical Center. He has over 10 years of experience in systems analysis, modeling and simulation, and model‐based engineering. He holds a BS in Aerospace Engineering from Embry‐Riddle Aeronautical University, an MS in Aerospace Engineering from the Georgia Institute of Technology, and a PhD in Aerospace Engineering from the Georgia Institute of Technology.
Sachin Shetty is an Associate Professor in the Virginia Modeling, Analysis and Simulation Center at Old Dominion University. He holds a joint appointment with the Department of Modeling, Simulation and Visualization Engineering and the Center for Cybersecurity Education and Research. Sachin Shetty received his PhD in Modeling and Simulation from the Old Dominion University in 2007. Prior to joining Old Dominion University, he was an Associate Professor with the Electrical and Computer Engineering Department at Tennessee State University. He was also the associate director of the Tennessee Interdisciplinary Graduate Engineering Research Institute and directed the Cyber Security laboratory at Tennessee State University. He also holds a dual appointment as an Engineer at the Naval Surface Warfare Center, Crane Indiana. His research interests lie at the intersection of computer networking, network security, and machine learning. His laboratory conducts cloud and mobile security research and has received over $10 million in funding from National Science Foundation, Air Office of Scientific Research, Air Force Research Lab, Office of Naval Research, Department of
