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Published: 10 December 2022

Last updated: 28 January 2023

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1

Benila S, Benila S., and Usha Bhanu N. Benila S. "Fog Managed Data Model for IoT based Healthcare Systems." 網際網路技術學刊 23, no. 2 (March 2022): 217–26. http://dx.doi.org/10.53106/160792642022032302003.

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<p>In Internet of things enabled healthcare system, sensors create vast volumes of data that are analyzed in the cloud. Transferring data from the cloud to the application takes a long time. An effective infrastructure can reduce latency and costs by processing data in real-time and close to the user devices. Fog computing can solve this issue by reducing latency by storing, processing, and analyzing patient data at the network edge. Placing the resources at fog layer and scheduling tasks is quite challenging in Fog computing. This paper proposes a Fog Managed Data Model (FMDM) with three layers namely Sensor, Fog and cloud to solve the aforementioned issue. Sensors generate patient data and that are managed and processed by Fog and cloud layers. Tasks are scheduled using a Weighted Fog Priority Job Scheduling algorithm (WFPJS) and fog nodes are allocated based on Priority based Virtual Machine Classification Algorithm (PVCA). The performance of this model is validated with static scheduling techniques with variable patient counts and network configurations. The proposed FMDM with WFPJS reduces response time, total execution cost, network usage, network latency, computational latency and energy consumption.</p> <p> </p>

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2

Williamson, Ron C. "Model-based Systems Engineering for Systems of Systems." INSIGHT 12, no. 4 (December 2009): 12–14. http://dx.doi.org/10.1002/inst.200912412.

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3

Joshi, Ravindra V., and Chandrashekhar N. ".i – A Complexity Theory based Platform for Model based System Engineering." Webology 19, no. 1 (January 20, 2022): 3348–57. http://dx.doi.org/10.14704/web/v19i1/web19220.

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Complexity Theory and Complex Adaptive Systems is fast emerging as optimal and efficient design alternative many of the existing technologies to address various functional anon-functional criterion. However, it remains predominantly laboratory resident software. One of the main obstacles to convert it into mainstream is its abstract terminology and black box “emergent” philosophy. In this paper an attempt is made to create a platform on the core foundation of cognitive agent and complex world concepts. The platform can be used to develop industry strength products incorporating complexity theory principles.

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4

Coghill, G. M. "Towards Model-based Methods for Developing Model-based Systems." International Journal of General Systems 33, no. 5 (October 2004): 485–504. http://dx.doi.org/10.1080/0308107042000202236.

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5

NISHIMURA, Hidekazu. "Systems Engineering and Model-Based Systems Engineering." Journal of the Society of Mechanical Engineers 119, no. 1177 (2016): 646–49. http://dx.doi.org/10.1299/jsmemag.119.1177_646.

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6

Djouab, Rachida, and Moncef Bari. "An ISO 9126 Based Quality Model for the e-Learning Systems." International Journal of Information and Education Technology 6, no. 5 (2016): 370–75. http://dx.doi.org/10.7763/ijiet.2016.v6.716.

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7

Calida, Behnido Y., Raed M. Jaradat, Sawsan Abutabenjeh, and Charles B. Keating. "Governance in systems of systems: a systems-based model." International Journal of System of Systems Engineering 7, no. 4 (2016): 235. http://dx.doi.org/10.1504/ijsse.2016.080313.

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8

Calida, Behnido Y., Raed M. Jaradat, Sawsan Abutabenjeh, and Charles B. Keating. "Governance in systems of systems: a systems-based model." International Journal of System of Systems Engineering 7, no. 4 (2016): 235. http://dx.doi.org/10.1504/ijsse.2016.10001152.

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9

Leitch, R., H. Freitag, G. Tornielli, and Q. Shen. "Composing Model-Based Diagnostic Systems." Integrated Computer-Aided Engineering 2, no. 3 (July 1, 1995): 203–17. http://dx.doi.org/10.3233/ica-1995-2304.

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10

Fuchs, Joachim. "Model Based Systems Engineering Editorial." INSIGHT 18, no. 2 (August 2015): 9. http://dx.doi.org/10.1002/inst.12012.

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11

Petnga, Leonard, Mark Austin, and Mark Blackburn. "SEMANTICALLY-ENABLED MODEL-BASED SYSTEMS." INSIGHT 20, no. 3 (September 2017): 29–38. http://dx.doi.org/10.1002/inst.12161.

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12

Griego, Regina. "Model-Based Systems Engineering Activities." INSIGHT 11, no. 4 (September 2008): 45–46. http://dx.doi.org/10.1002/inst.200811445.

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13

Kübler, Karl, Stefan Scheifele, Christian Scheifele, and Oliver Riedel. "Model-Based Systems Engineering for Machine Tools and Production Systems (Model-Based Production Engineering)." Procedia Manufacturing 24 (2018): 216–21. http://dx.doi.org/10.1016/j.promfg.2018.06.036.

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14

John, Fitzgerald. "COMPREHENSIVE MODEL-BASED ENGINEERING FOR SYSTEMS OF SYSTEMS." INSIGHT 19, no. 3 (October 2016): 59–62. http://dx.doi.org/10.1002/inst.12111.

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15

Kirshner, Mitchell. "Model-Based Systems Engineering Cybersecurity for Space Systems." Aerospace 10, no. 2 (January 25, 2023): 116. http://dx.doi.org/10.3390/aerospace10020116.

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As industries in various sectors increasingly adopt model-based systems engineering (MBSE) for system lifecycle design and development, engineers can manage and describe systems of higher complexity than ever before. This is especially true for the field of space systems; while past missions have developed using document-based planning, it is only in the last several years that NASA and other organizations in the space industry have begun using MBSE. One crucial factor of space systems development that is often overlooked is cybersecurity. As space systems become more complex and cyberphysical in nature, cybersecurity requirements become more difficult to capture, especially through document-based methods; a need for a means by which to continuously verify and validate systems cybersecurity for cyberphysical space missions arises. By expanding upon a National Institute of Standards and Technology (NIST) framework for cyber resiliency, this work proposes a methodology that uses MBSE traceability functionality to demonstrate adequate cybersecurity for cyberphysical space systems using SysML requirements modeling capabilities. Key goals, objectives, and strategic principles leading to achieving cybersecurity at all levels of the system’s architectural hierarchy are presented. Recommendations for the future of space cybersecurity include the addition of the space sector to the Department of Homeland Security Cybersecurity & Infrastructure Security Agency’s list of critical infrastructure sectors to improve standardization and control of space cyberinfrastructure.

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16

Yasuda, Gen'ichi. "Petri Net Model Based Specification and Distributed Control of Robotic Manufacturing Systems." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 410–15. http://dx.doi.org/10.1299/jsmeicam.2010.5.410.

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17

Riccobene, Elvinia, Patrizia Scandurra, Sara Bocchio, Alberto Rosti, Luigi Lavazza, and Luigi Mantellini. "SystemC/C-based model-driven design for embedded systems." ACM Transactions on Embedded Computing Systems 8, no. 4 (July 2009): 1–37. http://dx.doi.org/10.1145/1550987.1550993.

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18

Gräßler, Iris, Dominik Wiechel, and Jens Pottebaum. "Role model of model-based systems engineering application." IOP Conference Series: Materials Science and Engineering 1097, no. 1 (February 1, 2021): 012003. http://dx.doi.org/10.1088/1757-899x/1097/1/012003.

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19

Rauzy, Antoine B., and Cecilia Haskins. "Foundations for model-based systems engineering and model-based safety assessment." Systems Engineering 22, no. 2 (September 25, 2018): 146–55. http://dx.doi.org/10.1002/sys.21469.

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20

Bohner, Shawn A., Denis Graĉanin, Michael G. Hinchey, and Mohamed Eltoweissy. "Model-based evolution of collaborative agent-based systems." Journal of the Brazilian Computer Society 13, no. 4 (December 2007): 17–38. http://dx.doi.org/10.1007/bf03194254.

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21

Onat, Ahmet, A. Teoman Naskali, and Emrah Parlakay. "Model Based Predictive Networked Control Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 13000–13005. http://dx.doi.org/10.3182/20080706-5-kr-1001.02198.

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22

Ephraim, Yariv. "Statistical model‐based speech enhancement systems." Journal of the Acoustical Society of America 84, S1 (November 1988): S131. http://dx.doi.org/10.1121/1.2025775.

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23

Narasimhan, Sriram, and Gautam Biswas. "Model-Based Diagnosis of Hybrid Systems." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 37, no. 3 (May 2007): 348–61. http://dx.doi.org/10.1109/tsmca.2007.893487.

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24

Nordstrom, Steve, Abhishek Dubey, Turker Keskinpala, Sandeep Neema, and Theodore Bapty. "Autonomic Healing of Model-Based Systems." Journal of Aerospace Computing, Information, and Communication 8, no. 4 (April 2011): 87–99. http://dx.doi.org/10.2514/1.31940.

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25

van Ruijven, Ing L. C. "Ontology and Model-based Systems Engineering." Procedia Computer Science 8 (2012): 194–200. http://dx.doi.org/10.1016/j.procs.2012.01.042.

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26

Bentahar, Jamal, John-Jules Meyer, and Wei Wan. "Model checking communicative agent-based systems." Knowledge-Based Systems 22, no. 3 (April 2009): 142–59. http://dx.doi.org/10.1016/j.knosys.2008.11.006.

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27

Salva, Sébastien, and Elliott Blot. "Model generation of component-based systems." Software Quality Journal 28, no. 2 (January 2, 2020): 789–819. http://dx.doi.org/10.1007/s11219-019-09485-y.

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28

Gerhold, Marcus, and Mariëlle Stoelinga. "Model-based testing of probabilistic systems." Formal Aspects of Computing 30, no. 1 (January 2018): 77–106. http://dx.doi.org/10.1007/s00165-017-0440-4.

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29

UENO, Haruki, and Yasumasa OOMORI. "Expert Systems Based on Object Model." Geoinformatics 2, no. 2 (1991): 97–108. http://dx.doi.org/10.6010/geoinformatics1990.2.2_97.

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30

Kelemen, Michal. "Model Based Design of Mechatronic Systems." Acta Mechanica Slovaca 21, no. 4 (October 31, 2017): 8–9. http://dx.doi.org/10.21496/ams.2017.029.

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31

Ephraim, Y. "Statistical-model-based speech enhancement systems." Proceedings of the IEEE 80, no. 10 (1992): 1526–55. http://dx.doi.org/10.1109/5.168664.

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32

Meyer, Jürgen, Andreas Werner, and Lutz Bichler. "Model-based testing for infotainment systems." ATZelektronik worldwide 1, no. 2 (June 2006): 20–22. http://dx.doi.org/10.1007/bf03242084.

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33

Oliver, Dave. "Introduction to Model-Based Systems Engineering." INSIGHT 10, no. 3 (July 2007): 23. http://dx.doi.org/10.1002/inst.200710323.

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34

Dickerson, Michael, Harry P. Frisch, and David W. Oliver. "Definitions for Model-Based Systems Engineering." INSIGHT 10, no. 3 (July 2007): 23–24. http://dx.doi.org/10.1002/inst.200710323a.

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35

Friedenthal, Sandy. "IW2014 - Model-Based Systems Engineering Workshop." INSIGHT 17, no. 1 (April 2014): 45–46. http://dx.doi.org/10.1002/inst.201417145.

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36

Burkhart, Roger, Sandy Friedenthal, Regina Griego, Mark Sampson, and Phil Spiby. "Model Based Systems Engineering (MBSE) Track." INCOSE International Symposium 17, no. 1 (June 2007): 2080. http://dx.doi.org/10.1002/j.2334-5837.2007.tb02999.x.

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37

Meyer, Bernhard. "Generic Model-Based Systems Engineering Methodology." INCOSE International Symposium 24, s1 (2014): 207–22. http://dx.doi.org/10.1002/j.2334-5837.2014.00017.x.

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38

Wang, Yue-Yun. "Model Based Calibration." Mechanical Engineering 137, no. 12 (December 1, 2015): S19—S21. http://dx.doi.org/10.1115/1.2015-dec-10.

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This article describes calibration of control systems for downsized boosted engines. Model-based design of powertrain and aftertreatment control systems includes plant modeling and controller synthesis. The article illustrates the typical work flow of the model-based calibration process for an engine. In the process, the first step is the design of experiment. The experiment design should cover a multisurface space. That guides the next step to collect data from an engine or powertrain at critical operating points. Once the data are collected, an engine system model is built along with its designed controller models, then the operation of the control systems and controller parameters are optimized or calibrated based on the plant models. With these initial values of the calibrations handy, one can either download the calibration into the production ECU or use a rapid prototyping controller to conduct a full validation or final fine tuning of the engine powertrain control system in test cells or on vehicles.

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39

Acheson, Paulette, Cihan Dagli, and Nil Kilicay-Ergin. "Model Based Systems Engineering for System of Systems Using Agent-based Modeling." Procedia Computer Science 16 (2013): 11–19. http://dx.doi.org/10.1016/j.procs.2013.01.002.

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40

Lopes, A. J., R. Lezama, and R. Pineda. "Model Based Systems Engineering for Smart Grids as Systems of Systems." Procedia Computer Science 6 (2011): 441–50. http://dx.doi.org/10.1016/j.procs.2011.08.083.

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41

Do, Nhon V., Hien D. Nguyen, and Ali Selamat. "Knowledge-Based Model of Expert Systems Using Rela-Model." International Journal of Software Engineering and Knowledge Engineering 28, no. 08 (August 2018): 1047–90. http://dx.doi.org/10.1142/s0218194018500304.

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Knowledge about relations plays a crucial role in human’s knowledge. Different methods for representing this type of knowledge have been proposed. However, due to the lack of theoretical foundations, these methods cannot guarantee criteria in knowledge representation such as formality, universality, usability and practicality. They are not adequate to represent the knowledge domains in practice which have many components. Based on formal ontology approach, a knowledge model about relations, called Rela-model, is presented in this paper. It has the components such as concepts, relations between concepts, and rules. The concepts in this model consist of attributes, facts and rules of itself. Each object in a concept has also equipped its behavior to solve problems on it. The methods for solving problems based on Rela-model are also studied. The general problems on this model are the following: Given some objects and facts on them, determine the closure of set of attributes and facts on the objects or determine an object or consider a relation between the objects. The algorithms to solve problems are designed and their properties, such as finiteness, effectiveness, have also been proved. Besides the solid mathematical foundation, Rela-model also has a simple specification language which can effectively represent the knowledge, thus it can be used in many real situations. Our approach is also applied to build two systems: the intelligent problem solver about solid geometry in high school mathematics, and the expert system to diagnose diseases in diabetic microvascular complication.

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42

Inkermann, David. "Towards Model-based Process Engineering." Proceedings of the Design Society: International Conference on Engineering Design 1, no. 1 (July 2019): 3741–50. http://dx.doi.org/10.1017/dsi.2019.381.

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AbstractThe high interaction between process and product models in product development and systems engineering (SE) is common sense. However, most research in the field of model based systems engineering (MBSE) focusses of physical systems (hardware and software). The authors claim that this focus is a main reason for the low acceptance and high effort for implementation of SE and MBSE in industrial practice. Thus, this contribution aims at supporting an integrative analysis and synthesis of process and product models by introducing the concept and framework of Model-based Process Engineering. Based on established research this framework introduces three main systems, namely the system of processes, system of product models, and system of tools to describe complex product development. The main contribution of this work is a preliminary concept to structure and link the systems of processes and product models. Besides form the description of the main relations between the systems an integrated modelling concept to represent links between the process and product model system is proposed.

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43

Kaiser, Lydia, Christian Bremer, and Roman Dumitrescu. "Exhaustiveness of Systems Structures in Model-based Systems Engineering for Mechatronic Systems." Procedia Technology 26 (2016): 428–35. http://dx.doi.org/10.1016/j.protcy.2016.08.055.

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44

Gianni, Daniele, Andrea D'Ambrogio, Pierluigi De Simone, Marco Lisi, and Michele Luglio. "Model-based Interface Specification for Systems Integration in Systems of Systems Engineering." INCOSE International Symposium 22, no. 1 (July 2012): 2040–52. http://dx.doi.org/10.1002/j.2334-5837.2012.tb01455.x.

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45

JAGANATHAN, V., R. KARINTHI, G. ALMASI, and M. SOBOLEWSKI. "MODEL BASED INFORMATION ACCESS." International Journal of Cooperative Information Systems 03, no. 02 (June 1994): 107–27. http://dx.doi.org/10.1142/s0218215794000089.

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The engineering data of a large enterprise is typically distributed over a wide area and archived in a variety of databases and file systems. Access to such information is crucial to a team member, particularly in a concurrent engineering setting. However, this is not easy, because (1) a model of the relevant information is not available, and (2) there is no simple way to access the information without being knowledgeable about various computer data formats, file systems, and networks. We have developed a system called the Information Sharing System (ISS) to enable access to diverse and distributed information within a corporation. Such data could be stored in different repositories such as databases and file systems including those that contain multiple media. Our paper describes the methodology of the ISS, the details of the implementation and extensions planned for the future.

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46

Mažeika, Donatas, and Rimantas Butleris. "MBSEsec: Model-Based Systems Engineering Method for Creating Secure Systems." Applied Sciences 10, no. 7 (April 9, 2020): 2574. http://dx.doi.org/10.3390/app10072574.

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This paper presents how Model-Based System Engineering (MBSE) could be leveraged in order to mitigate security risks at an early stage of system development. Primarily, MBSE was used to manage complex engineering projects in terms of system requirements, design, analysis, verification, and validation activities, leaving security aspects aside. However, previous research showed that security requirements and risks could be tackled in the MBSE model, and powerful MBSE tools such as simulation, change impact analysis, automated document generation, validation, and verification could be successfully reused in the multidisciplinary field. This article analyzes various security-related techniques and then clarifies how these techniques can be represented in the Systems Modeling Language (SysML) model and then further exploited with MBSE tools. The paper introduces the MBSEsec method, which gives guidelines for the security analysis process, the SysML/UML-based security profile, and recommendations on what security technique is needed at each security process phase. The MBSEsec method was verified by creating an application case study that reflects real-world problems and running an experiment where systems and security engineers evaluated the feasibility of our approach.

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47

Tekinerdogan, Bedir, Murat Kaan Özcan, İskender Yakın, and Sevil Yağız. "Model‐Based Systems Product Line Engineering of Physical Protection Systems." INCOSE International Symposium 31, no. 1 (July 2021): 1–15. http://dx.doi.org/10.1002/j.2334-5837.2021.00822.x.

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48

Ramos, A. L., J. V. Ferreira, and J. Barcelo. "Model-Based Systems Engineering: An Emerging Approach for Modern Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42, no. 1 (January 2012): 101–11. http://dx.doi.org/10.1109/tsmcc.2011.2106495.

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49

Delp, Christopher L., Cin-young Lee, Olivier de Weck, Carlee Bishop, Evan Analzone, Roli Gostelow, and Chelsea Dutenhoffer. "The Challenge of Model-based Systems Engineering for Space Systems." INSIGHT 11, no. 5 (December 2008): 14–18. http://dx.doi.org/10.1002/inst.200811514.

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50

Cressent, Robin, Vincent Idasiak, and Frederic Kratz. "Model-Based Systems Engineering with SysML for Reliable Systems Design." INSIGHT 14, no. 4 (December 2011): 18–20. http://dx.doi.org/10.1002/inst.201114418.

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