Michael enescu keynote chicago2014_from_cloud_to_fog_and_iotMichael Enescu
Just as we became familiar with Cloud computing and the dominant role of Open Source, a new compute model has emerged, an extension of the Cloud to the Edge of the network, some call it Fog computing, others the Internet of Things or IoT. Open Source will also play a dominant role in this, as it does in the Cloud, but what exactly is it, and what does it mean in IoT. How is the model changing as the new generation of devices has stretched what we previously knew as Cloud compute. And most importantly, why, what, and how the new IoT-specific Service Provider (IsSP?) model emerges and disrupts its aging predecessor. This is the tidal wave of opportunity we see, a generation after the last one that took place in the mid 90's. Get ready!
Fog Computing – between IoT Devices and The Cloud presentation covers following topics:
- Edge, Fog, Mist & Cloud Computing
- Fog domains and fog federation, wireless sensor networks, - multi-layer IoT architecture
- Fog computing standards and specifications
- Practical use-case scenarios & advantages of fog
- Fog analytics and intelligence on the edge
- Technologies for distributed asynchronous event processing - and analytics in real time
- Lambda architecture – Spark, Storm, Kafka, Apex, Beam, Spring - Reactor & WebFlux
- Eclipse IoT platform
Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks,
Fog computing is a term created by Cisco that refers to extending cloud computing to the edge of an enterprise's network.
Cisco introduced its fog computing vision in January 2014 as a way of bringing cloud computing capabilities to the edge of the network .
As the result, closer to the rapidly growing number of connected devices and applications that consume cloud services and generate increasingly massive amounts of data.
Semantic Web Methodologies, Best Practices and Ontology Engineering Applied t...Ghislain ATEMEZING
This talk presents some best practices and ontology engineering applied to internet of things. The talk was presented during the 2nd IEEE World Forum on Internet of Things held in Milan, from December 14th to December 16th, 2015.
The Internet of Things (IoT) is one of the hottest mega-trends in technology – and for good reason , IoT deals with all the components of what we consider web 3.0 including Big Data Analytics, Cloud Computing and Mobile Computing .
Green Compute and Storage - Why does it Matter and What is in ScopeNarayanan Subramaniam
Presentation made for BITS students under the auspices of IEEE Goa on the account of Lumini '21 - BITS Goa's annual technical symposium. Topic provides an overview as to why green compute/storage is important as the Internet explodes with voice, video and other content consuming 8% (3 TWh) of total global electricity production rising exponentially to 21% (9 TWh) by 2030. This is likely to be accelerated with the advent of 5G and IoT everywhere. I explore 3 key pillars of computing with respect to "green" and the consequences that need to be mitigated in short order.
IoT ( M2M) - Big Data - Analytics: Emulation and DemonstrationCHAKER ALLAOUI
Study and simulation of the systems modern IoT with examples of connected objects such as: GPS(GLOBAL POSITIONING SYSTEM), Philips Hue, Thermometer, and connected cars implemented with the technology nodeJS and Node-Red with the communication protocol of M2M ( MQTT).
As well as an analytical study based on Elasticsearch, MongoDB, Apache Hadoop, Apache Hive and Jaspersoft.
The so-called Cyber Physical Systems (CPSs) which are known as fourth generation of Industrial Revolution, are being realized. In This presentation, a CPS is defined, its characteristics and benefits are listed and its impacts on products are discussed.
Fog Computing Reality Check: Real World Applications and ArchitecturesBiren Gandhi
Is Fog Computing just a buzz or a real business?
The IoT is flooded with a variety of platforms and solutions. Fog Computing has been notably appearing as an evolving term in the context of IoT software. There is skepticism that Fog Computing is just another buzzword destined to disappear in the dust of time. Get insight from concrete business cases in a variety of IoT verticals – Agriculture, Industrial Manufacturing, Transportation, Smart & Connected Communities etc. and learn how Fog Computing can play a substantial role in each one of these verticals. Develop a judicious point of view with respect to the future of Fog Computing through market research, technology disruption vectors and ROI use cases presented in this session.
Phoenix Data Conference - Big Data Analytics for IoT 11/4/17Mark Goldstein
“Big Data for IoT: Analytics from Descriptive to Predictive to Prescriptive” was presented to the Phoenix Data Conference on 11/4/17 at Grand Canyon University.
As the Internet of Things (IoT) floods data lakes and fills data oceans with sensor and real-world data, analytic tools and real-time responsiveness will require improved platforms and applications to deal with the data flow and move from descriptive to predictive to prescriptive analysis and outcomes.
Data Modelling and Knowledge Engineering for the Internet of ThingsCory Andrew Henson
Tutorial on Data Modelling and Knowledge Engineering for the Internet of Things, presented at EKAW 2012, Galway City, Ireland, October 8-12, 2012
http://knoesis.org/iot-tutorial-ekaw2012/
Cyber-physical systems Industrial applications in the CPSwarm ProjectAlessandra Bagnato
CPS and Cyber-Physical Systems of Systems (CPSoS) are increasingly playing the role of foundational building blocks for bringing adaptive intelligence to processes and environments, in several application domains ranging from Smart Mobility, to Smart Health, Smart Cities and Smart Production. Due to the
increasing pervasiveness of CPS, issues related to effective design of solutions, able to reach predefined goals flexibly, reliably and adapting to changing surrounding conditions, become challenging and worth of further investigation. While increasing the CPS adoption results in increasingly mature solutions for their development, a single, consistent, science of system integration for CPS has not yet been consolidated.
As a matter of fact, the increasing interactions amongst different
CPS are starting to generate unpredicted behaviours and emerging properties, often leading to unforeseen and/or undesired results. These interactions could become an advantage if they were explicitly managed, and accounted, since the early design stages. The CPSwarm project,
presented in this lecture, aims at tackling these kinds of challenges by easing development and integration of complex herds of heterogeneous CPS. Thanks to CPSwarm, systems designed through a combination of existing and emerging tools, will collaborate on the
basis of local policies and exhibit a collective behaviour capable of solving complex, real-world, problems. Three real-world use cases will demonstrate the validity of foundational assumptions of the presented approach as well as the viability of the developed tools and methodologies.
CPSwarm will demonstrate the viability of the proposed approach on 3 complimentary, yet di_erent, use cases targeted at: (a) swarms of (mixed) robotic vehicles (e.g. Unmanned Aerial Vehicles (UAV) and rovers), (b) automotive CPS systems for freight vehicles and (c) swarm logistics.
All scenarios are characterized by the presence of heterogeneous CPS interacting together and showing emerging behaviors difficult to predict with traditional approaches and will be presented in the lecture.
Towards Cyber-Physical System technologies over Apache VCLImre Kocsis
Presentation held at the 2nd International IBM Cloud Academy Conference (ICACON 2014, Atlanta, Georgia) on extending Apache VCL for teaching Cyber-Physical Systems.
The Smart City concept operates in a complex urban environment, incorporating several complex systems of infrastructure, human behavior, technology, social and political structures and the economy. A Smart City provides an intelligent way to manage components such as transport, health, energy, education and the environment.
2004 Net-centric Systems and Services Interoperability Engineering (NESSIE)Bob Marcus
Informal overview of some major US governmental projects with suggestions on how to engineer systems and services interoperability using a standards-based framework.
Michael enescu keynote chicago2014_from_cloud_to_fog_and_iotMichael Enescu
Just as we became familiar with Cloud computing and the dominant role of Open Source, a new compute model has emerged, an extension of the Cloud to the Edge of the network, some call it Fog computing, others the Internet of Things or IoT. Open Source will also play a dominant role in this, as it does in the Cloud, but what exactly is it, and what does it mean in IoT. How is the model changing as the new generation of devices has stretched what we previously knew as Cloud compute. And most importantly, why, what, and how the new IoT-specific Service Provider (IsSP?) model emerges and disrupts its aging predecessor. This is the tidal wave of opportunity we see, a generation after the last one that took place in the mid 90's. Get ready!
Fog Computing – between IoT Devices and The Cloud presentation covers following topics:
- Edge, Fog, Mist & Cloud Computing
- Fog domains and fog federation, wireless sensor networks, - multi-layer IoT architecture
- Fog computing standards and specifications
- Practical use-case scenarios & advantages of fog
- Fog analytics and intelligence on the edge
- Technologies for distributed asynchronous event processing - and analytics in real time
- Lambda architecture – Spark, Storm, Kafka, Apex, Beam, Spring - Reactor & WebFlux
- Eclipse IoT platform
Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks,
Fog computing is a term created by Cisco that refers to extending cloud computing to the edge of an enterprise's network.
Cisco introduced its fog computing vision in January 2014 as a way of bringing cloud computing capabilities to the edge of the network .
As the result, closer to the rapidly growing number of connected devices and applications that consume cloud services and generate increasingly massive amounts of data.
Semantic Web Methodologies, Best Practices and Ontology Engineering Applied t...Ghislain ATEMEZING
This talk presents some best practices and ontology engineering applied to internet of things. The talk was presented during the 2nd IEEE World Forum on Internet of Things held in Milan, from December 14th to December 16th, 2015.
The Internet of Things (IoT) is one of the hottest mega-trends in technology – and for good reason , IoT deals with all the components of what we consider web 3.0 including Big Data Analytics, Cloud Computing and Mobile Computing .
Green Compute and Storage - Why does it Matter and What is in ScopeNarayanan Subramaniam
Presentation made for BITS students under the auspices of IEEE Goa on the account of Lumini '21 - BITS Goa's annual technical symposium. Topic provides an overview as to why green compute/storage is important as the Internet explodes with voice, video and other content consuming 8% (3 TWh) of total global electricity production rising exponentially to 21% (9 TWh) by 2030. This is likely to be accelerated with the advent of 5G and IoT everywhere. I explore 3 key pillars of computing with respect to "green" and the consequences that need to be mitigated in short order.
IoT ( M2M) - Big Data - Analytics: Emulation and DemonstrationCHAKER ALLAOUI
Study and simulation of the systems modern IoT with examples of connected objects such as: GPS(GLOBAL POSITIONING SYSTEM), Philips Hue, Thermometer, and connected cars implemented with the technology nodeJS and Node-Red with the communication protocol of M2M ( MQTT).
As well as an analytical study based on Elasticsearch, MongoDB, Apache Hadoop, Apache Hive and Jaspersoft.
The so-called Cyber Physical Systems (CPSs) which are known as fourth generation of Industrial Revolution, are being realized. In This presentation, a CPS is defined, its characteristics and benefits are listed and its impacts on products are discussed.
Fog Computing Reality Check: Real World Applications and ArchitecturesBiren Gandhi
Is Fog Computing just a buzz or a real business?
The IoT is flooded with a variety of platforms and solutions. Fog Computing has been notably appearing as an evolving term in the context of IoT software. There is skepticism that Fog Computing is just another buzzword destined to disappear in the dust of time. Get insight from concrete business cases in a variety of IoT verticals – Agriculture, Industrial Manufacturing, Transportation, Smart & Connected Communities etc. and learn how Fog Computing can play a substantial role in each one of these verticals. Develop a judicious point of view with respect to the future of Fog Computing through market research, technology disruption vectors and ROI use cases presented in this session.
Phoenix Data Conference - Big Data Analytics for IoT 11/4/17Mark Goldstein
“Big Data for IoT: Analytics from Descriptive to Predictive to Prescriptive” was presented to the Phoenix Data Conference on 11/4/17 at Grand Canyon University.
As the Internet of Things (IoT) floods data lakes and fills data oceans with sensor and real-world data, analytic tools and real-time responsiveness will require improved platforms and applications to deal with the data flow and move from descriptive to predictive to prescriptive analysis and outcomes.
Data Modelling and Knowledge Engineering for the Internet of ThingsCory Andrew Henson
Tutorial on Data Modelling and Knowledge Engineering for the Internet of Things, presented at EKAW 2012, Galway City, Ireland, October 8-12, 2012
http://knoesis.org/iot-tutorial-ekaw2012/
Cyber-physical systems Industrial applications in the CPSwarm ProjectAlessandra Bagnato
CPS and Cyber-Physical Systems of Systems (CPSoS) are increasingly playing the role of foundational building blocks for bringing adaptive intelligence to processes and environments, in several application domains ranging from Smart Mobility, to Smart Health, Smart Cities and Smart Production. Due to the
increasing pervasiveness of CPS, issues related to effective design of solutions, able to reach predefined goals flexibly, reliably and adapting to changing surrounding conditions, become challenging and worth of further investigation. While increasing the CPS adoption results in increasingly mature solutions for their development, a single, consistent, science of system integration for CPS has not yet been consolidated.
As a matter of fact, the increasing interactions amongst different
CPS are starting to generate unpredicted behaviours and emerging properties, often leading to unforeseen and/or undesired results. These interactions could become an advantage if they were explicitly managed, and accounted, since the early design stages. The CPSwarm project,
presented in this lecture, aims at tackling these kinds of challenges by easing development and integration of complex herds of heterogeneous CPS. Thanks to CPSwarm, systems designed through a combination of existing and emerging tools, will collaborate on the
basis of local policies and exhibit a collective behaviour capable of solving complex, real-world, problems. Three real-world use cases will demonstrate the validity of foundational assumptions of the presented approach as well as the viability of the developed tools and methodologies.
CPSwarm will demonstrate the viability of the proposed approach on 3 complimentary, yet di_erent, use cases targeted at: (a) swarms of (mixed) robotic vehicles (e.g. Unmanned Aerial Vehicles (UAV) and rovers), (b) automotive CPS systems for freight vehicles and (c) swarm logistics.
All scenarios are characterized by the presence of heterogeneous CPS interacting together and showing emerging behaviors difficult to predict with traditional approaches and will be presented in the lecture.
Towards Cyber-Physical System technologies over Apache VCLImre Kocsis
Presentation held at the 2nd International IBM Cloud Academy Conference (ICACON 2014, Atlanta, Georgia) on extending Apache VCL for teaching Cyber-Physical Systems.
The Smart City concept operates in a complex urban environment, incorporating several complex systems of infrastructure, human behavior, technology, social and political structures and the economy. A Smart City provides an intelligent way to manage components such as transport, health, energy, education and the environment.
2004 Net-centric Systems and Services Interoperability Engineering (NESSIE)Bob Marcus
Informal overview of some major US governmental projects with suggestions on how to engineer systems and services interoperability using a standards-based framework.
Technology Convergence for Smart X ApplicationsBob Marcus
Cartoons showing convergence of emerging technologies into Cyber-Physical-Social grids to provide support for large-scale Smart X applications. This is a very high level overview meant to capture some of the technology interactions for non-technical viewers.
Snapshots of the 132 Responses to the US National Telecommunications and Information Administration (NTIA) Request for Comments (RFC) on "The Benefits, Challenges, and Potential Roles for the Government in Fostering the Advancement of the Internet of Things"
Reference Architectures for Layered CPS System of Systems using Data Hubs and...Bob Marcus
Describes extensions of current NIST Reference Architectures and Frameworks that are needed to handle CPS System of Systems Use Cases (e.g. Smart Grid, Smart City). These extensions include Data Hubs and CPS Hubs.
Architectures for Cyber-Physical Systems, or Why Ivan Doesn’t Want to GraduateIvan Ruchkin
A fresh multidisciplinary research and engineering area of Cyber-Physical Systems (CPSs) lies on an intersection of more traditional fields, like mechanical and electrical engineering, and newer approaches from AI, ubiquitous computing, and software engineering. Although modeling is a core method in these areas, the concrete mindsets and methods for it are very diverse, which makes system-level reasoning across models more complicated. For instance, it is difficult to predict how smoothing a control algorithm represented in Simulink would affect schedulability guarantees provided by a rate-monotonic analysis model. Conveniently, software architecture is well-known for reconciling concerns by loosening up model semantics, which makes it a promising tool for model-based design of CPSs. This talk discusses several examples from the automotive and robotics domains to expose the challenges of using heterogeneous models and how software architecture might help alleviate those. All these considerations will be linked to the mysterious second part of the title.
Wireless Networked Control Systems (WNCSs) are spatially distributed systems in which sensors, actuators, and controllers connect through a wireless network instead of traditional point-to-point links. WNCSs have a tremendous potential to improve the efficiency of many large-scale distributed systems in industrial automation, building automation, automated highway, air transportation, and smart grid. Transmitting sensor measurements and control commands over wireless links provide many benefits such as the ease of installation and maintenance, low complexity and cost, and large flexibility to accommodate the modification and upgrade of the components in many control applications. Several industrial organizations, such as International Society of Automation (ISA), Highway Addressable Remote Transducer (HART), and Wireless In- dustrial Networking Alliance (WINA), have been actively pushing the application of wireless technologies in the control applications. Building a WNCS is very challenging since control systems often have stringent requirements on timing and reliability, which are difficult to attain by wireless sensor networks due to the adverse properties of the wireless communication and limited battery resources of the nodes. We provide a framework for the joint optimization of controller and communication systems encompassing efficient abstractions of both systems.
4th Industrial Revolution is Beyond Cyber Physical Systems VSR *
4th Industrial Revolution is mainly covering Cyber Physical Systems, which in my view is not correct. The digital age Cyber system with hyper computing power, communication infrastructure, algorithms and information processing change many other systems like social systems, enterprise systems, biological systems etc.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Software Architecture and the role of the Architect has been discussed and deliberated in detail. Architecture still plays major role in success of projects. While the fundamentals remain strong, how architects can contribute in teams success while in agile is an ongoing journey. As the team member endowed with skills and wisdom acquired over the experience frame, we argue Architects are best positioned to prepare a road-map of architectural aspects and participate in planning together with product owners and release owners thus enabling a more meaningful planning and guidance system. Based on Risk and Cost Based Architecture concept by Eltjo Poort and based on CAFFEA framework by Jan Bosch and team, we applied it in projects which led to seeing it as a six stepped approach described in the slides.
The current landscape of Internet of Things (IoT) applications is extremely fragmented because we are still experimenting to discover the correct mix for our respective markets. Unfortunately in the IoT world, one solution does not fit all.We need much more clarity in understanding the challenges of IoT application development, both in terms of technical feasibility as well as business opportunities. In our talk we present a toolkit approach towards accelerating IoT applications by leveraging modular components that can effectively accelerate go to market for end solutions.
Charith Perera, Ciaran Mccormick, Arosha Bandara, Blaine A. Price, Bashar Nuseibeh, Privacy-by-Design Framework for Assessing Internet of Things Applications and Platforms, Proceedings of the 6th ACM International Conference on Internet of Things (IoT), Stuttgart, Germany, November, 2016, Pages 83-92
This webinar is going to cover what is a digital twin and how all stakeholders can benefit from their functionality. You will learn how model-based systems engineering enables digital engineering. Your host will discuss use cases, a realistic look at digital engineering and digital twins, and how you can use Innoslate to get started.
The Agenda
Here's what we're covering.
What is a Digital Twin
Benefits of Digital Twin
The Digital Engineering Path Enabled by MBSE
AR + MBSE Software
A More Realistic Digital Twin
Getting You Started with Digital Twins
Question Answer Session
Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective ...Sebastiano Panichella
Lecture entitled "Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective Test Generation and Selection" at the International Summer School
on Search- and Machine Learning-based Software Engineering
June 22-24, 2022 - Córdoba, Spain
Sebastiano Panichella and Christian Birchler
Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective ...Sebastiano Panichella
Lecture entitled "Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective Test Generation and Selection" at the International Summer School
on Search- and Machine Learning-based Software Engineering
June 22-24, 2022 - Córdoba, Spain
Sebastiano Panichella and Christian Birchler
Agents for Agility - The Just-in-Time Enterprise Has ArrivedInside Analysis
Hot Technologies with Krish Krishnan, Robin Bloor and EnterpriseWeb
Live Webcast Aug. 21, 2013
The demand for agility continues to motivate today's data-driven organizations. Competitors all over the globe are vying for faster time-to-insight, or even time-to-action. But there are other issues like governance and data quality that typically slow down key processes. Almost invariably, legacy systems that perform critical business processes are late to the party, resulting in enterprise inertia. However, a new wave of innovation is solving that problem by incorporating a late-binding approach for both analytics and operations.
Register for this episode of Hot Technologies to hear Analysts Krish Krishnan of Sixth Sense, and Dr. Robin Bloor of The Bloor Group, as they outline their competing visions for the architecture of a real-time enterprise. They'll be briefed by Dave Duggal of EnterpriseWeb, who will tout his company's platform for delivering robust enterprise functionality at the speed of the network. He'll discuss how EnterpriseWeb leverages the best ideas of service orientation, combined with intelligent agents that act as virtual hubs for the sharing of data, analytics, and mission-critical business processes.
Simulation Modelling Practice and Theory 47 (2014) 28–45Cont.docxedgar6wallace88877
Simulation Modelling Practice and Theory 47 (2014) 28–45
Contents lists available at ScienceDirect
Simulation Modelling Practice and Theory
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / s i m p a t
Insight Maker: A general-purpose tool for web-based modeling
& simulation
http://dx.doi.org/10.1016/j.simpat.2014.03.013
1569-190X/� 2014 The Author. Published by Elsevier B.V.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
E-mail address: [email protected]
1 The exact search query used was ‘’’modeling tool’’ OR ‘‘simulation tool’’’ in the Topic field.
Scott Fortmann-Roe
University of California, Berkeley, Department of Environmental Science, Policy, and Management, 130 Mulford Hall, Berkeley, CA 94720-3114, United States
a r t i c l e i n f o a b s t r a c t
Article history:
Received 29 April 2013
Received in revised form 23 March 2014
Accepted 26 March 2014
Available online 14 June 2014
Keywords:
Modeling
Simulation
Web-based technologies
System Dynamics
Agent-Based Modeling
A web-based, general-purpose simulation and modeling tool is presented in this paper. The
tool, Insight Maker, has been designed to make modeling and simulation accessible to a
wider audience of users. Insight Maker integrates three general modeling approaches –
System Dynamics, Agent-Based Modeling, and imperative programming – in a unified
modeling framework. The environment provides a graphical model construction interface
that is implemented purely in client-side code that runs on users’ machines. Advanced fea-
tures, such as model scripting and an optimization tool, are also described. Insight Maker,
under development for several years, has gained significant adoption with currently more
than 20,000 registered users. In addition to detailing the tool and its guiding philosophy,
this first paper on Insight Maker describes lessons learned from the development of a com-
plex web-based simulation and modeling tool.
� 2014 The Author. Published by Elsevier B.V. This is an open access article under the CC BY
license (http://creativecommons.org/licenses/by/3.0/).
1. Introduction
The field of modeling and simulation tools is diverse and emergent. General-purpose modeling tools (e.g. MATLAB’s
Simulink or the Modelica language [1]) sit beside highly focused and domain-specific applications (e.g. [2] for modeling
network control systems, [3] for simulating the behavior of wireless network routing protocols, or [4] for the simulation
and control of turbines). Interest in and published works on such tools has grown over time. The ISI Web of Knowledge
reports a substantial growth in papers published on modeling or simulation tools with 299 such papers published in the span
of 1985–1989, 1482 published from 1995 to 1999, and 3727 published from 2005 to 2009.1
For end-users, simulation and modeling tools are generally designed as executables to be run on a consumer operating
system such as W.
AI-Driven Science and Engineering with the Global AI and Modeling Supercomput...Geoffrey Fox
Most things are dominated by Artificial Intelligence (AI). Technology Companies like Amazon, Google, Facebook, and Microsoft are AI First organizations.
Engineering achievement today is highlighted by the AI buried in a vehicle or machine. Industry (Manufacturing) 4.0 focusses on the AI-Driven future of the Industrial Internet of Things.
Software is eating the world.
We can describe much computer systems work as designing, building and using the Global AI and Modelling supercomputer which itself is autonomously tuned by AI. We suggest that this is not just a bunch of buzzwords but has profound significance and examine consequences of this for education and research.
Naively high-performance computing should be relevant for the AI supercomputer but somehow the corporate juggernaut is not making so much use of it. We discuss how to change this.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
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2. Key Points on Engineering Large Scale CPS - Initial Thoughts
• Many large scale CPS application (e.g. Smart City) will require the integration of
heterogenous systems into a system of systems
• These systems can be on the same horizontal level (e.g. networked embedded
systems) and on different vertical levels (e.g. IoT to Cloud)
• Standards will be needed for network, data, and command communication across
horizontal and vertical levels See the slide set on “Standards for CPS” at
http://www.slideshare.net/bobmarcus/standards-and-open-source-for-big-data-cloud-and-iot
• Enhanced design tools (e.g. modeling and simulation) will be needed to evaluate
system of system architecture and components
• Testbeds will be needed that can integrate real, virtual, and simulated systems at
multiple levels. See the slide set on “Research and Testbeds for CPS” at
http://www.slideshare.net/bobmarcus/research-and-testbeds-in-cyberphysical-systems
• Monitoring and management across multiple levels will be a major challenge
Friday, April 29, 16
3. Outline of Presentation
• IoT Engineering for CPS
• Design Patterns for IoT from Michael Koster
• Systems Engineering for CPS
• CPS Projects from the European Union
• IoT European Research Cluster (IERC) Slides
• NIST CPS Framework
• NIST-NICT JointVision for Cyber-Physical Cloud Computing (CPCC)
• Large Scale CPS System of Systems
• Complex Systems
• Modeling and Simulation for Engineering CPS
• References
Friday, April 29, 16
13. From http://iot-datamodels.blogspot.com/2014/05/design-patterns-for-internet-of-things.html
Basic Design Patterns for the Internet of Things from Michael Koster
There is no one “reference” architecture for an Internet of
Things. Design Patterns are a way to construct architecture
solutions for specific use cases and use case classes.
The work to find system architecture solutions to Internet of Things problems
has led to an obvious conclusion that there is no single architecture appropriate
for most IoT use cases.The full spectrum of IoT presents a broad range of
diverse use cases and resource constraints, and thus motivates a range of
architecture solutions. Still, we would like to ground the discussion in a reference
set of technical concepts, to help promote a unified understanding and break
down the silos of thought around IoT architecture.We would also like to find
opportunities for standardization and commonality.
Different architecture solutions are appropriate for different use case classes, and
architecture is expected to be reusable within a particular class of use
cases.Therefore it makes more sense to talk about IoT architecture as a set of
Design Patterns, working together to achieve an end-to-end solution for some
problem.
Friday, April 29, 16
16. From http://iot-datamodels.blogspot.com/2014/05/design-patterns-for-internet-of-things.html
Pattern: Middleware Abstraction for IoT from Michael Koster
Virtualization through middleware: allows many (web) applications to interact with
things. Middleware can cache the state of the thing and minimize network traffic and
power drain on constrained devices, and can also serve as a persistent end point for
things that aren’t reachable over the network due to power cycling, firewalls, etc.
Friday, April 29, 16
17. From http://iot-datamodels.blogspot.com/2014/05/design-patterns-for-internet-of-things.html
Pattern: Device to Device Connectivity from Michael Koster
• Devices talk to other devices peer-to-peer: local network connectivity enables
proximal ad-hoc networking, service federation and chaining, media stream continuity.
• Personal tracking device uses smartphone as gateway: common pattern for bluetooth
and WiFi connectivity.
• Smart home local application controller and gateway: application gateway pattern.
Friday, April 29, 16
20. From http://iot-datamodels.blogspot.com/2014/05/design-patterns-for-internet-of-things.html
Design Patterns for IoT Security from Michael Koster
• Access control using data models: semantic hyperlinks control access to resources
based on the embedded metadata
• Social to physical graph relationship: well defined concepts of ownership and
access delegation between people, entities, and things
• PGP and asymmetric public-key cryptography on devices: ways of creating SSL
sessions and signing data between devices and applications
• DTLS over UDP: security for resource constrained devices
• End-to-end encryption: transmitting and storing encrypted data independent of
channel encryption
• Device Management: using device identity, registration, and secure key exchange
Friday, April 29, 16
22. Cyber-Physical Systems Structure and Behavior from U of Maryland
From http://www.iaria.org/conferences2014/filesICONS14/ICONS-Austin-Keynote2014-02-07.pdf
Friday, April 29, 16
23. Systems Engineering Definition
From https://en.wikipedia.org/wiki/Systems_engineering
Systems engineering is an interdisciplinary field of engineering that focuses on how to
design and manage complex engineering systems over their life cycles. Issues such as
requirements engineering, reliability, logistics, coordination of different teams, testing and
evaluation, maintainability and many other disciplines necessary for successful system
development, design, implementation, and ultimate decommission become more difficult
when dealing with large or complex projects.
Systems Engineering Process with SIMILAR Method
From http://aaq.auburn.edu/node/125
Friday, April 29, 16
24. SIMILAR Systems Engineering Method
From http://aaq.auburn.edu/node/125
• State the Problem: This should be in the form of a statement or a model. It is not used to describe how to fix the
problem; it simply describes what the problem is. It should include preferences and requirements stemming from
customer needs.
• Investigate Alternatives: Different options for a solution should be considered.They should be evaluated
on several criteria such as performance, cost, schedule, and risk.This reduces the risk of the project and helps clarify the
problem statement.
• Model the System: Models will be constructed for the different alternatives, then the model for the best alternative
will be further developed to help manage the system. Models will show the product and the process which produces
the product.They can be in the form of physical analogs, analytic equations, simulations, or diagrams.
• Integrate: Integrating the different facets of the systems means establishing logical flow and communication between
the subsystems. Ideally, a subsystem would finish its work on a product before sending it to another subsystem.A set up
which would require the least amount of information sharing between subsystems would be optimal.
• Launch the System: Launching the system is running through it to produce actual output.This requires more
detailed design as parts and new knowledge are integrated into the system. The systems engineers' products are: a
mission statement, a requirements document including verification (is the system built correctly?) and validation (is the
correct system being built?), a description of functions and objects, figures of merit, a test plan, a drawing of system
boundaries, an interface control document, a listing of deliverables, models, a sensitivity analysis, a tradeoff study, a risk
analysis, a life cycle analysis and a description of the physical architecture.
• Assess Performance: Performance is assessed through various metrics such as customer satisfaction, technical
performance measures, and figures of merit.To improve performance, you must be abke to control it.To control it, you
must be able to measure it. Resources should be budgeted and allocated to each subsystem, with a reserve amount
given to the project manager.
• Re-evaluate: Observe outputs from the system, and modify accordingly.This is a continual process that is constantly
repeated, as seen below.This is necessary for control and improvement.
Friday, April 29, 16
25. SysML Definition
From https://en.wikipedia.org/wiki/Systems_engineering
The Systems Modeling Language (SysML) is a general-purpose modeling language for
systems engineering applications. It supports the specification, analysis, design,
verification and validation of a broad range of systems and systems-of-systems
From http://www.arscontrol.org/automationmenu/100-umlsysml
Friday, April 29, 16
26. System Engineering Domain Elements using SysML
From http://www.jhuapl.edu/techdigest/TD/td3201/32_01-Topper.pdf
Friday, April 29, 16
27. From www.engr.ncsu.edu/mechatronics/what-mech.php
From Mechatronics to Cyber-Physical Systems
In the last two decades, an intense shift from advanced mechatronics systems to cyber-
physical systems is taking place.The former systems, which integrate mechanical,
electronics, computing, control and situated reasoning components, are typically
implemented as closed, predefined, controlled, and deterministic systems.The latter
systems are characterized by open system boundaries, large functional and structural
complexities, self-learning and -reasoning capabilities, partial autonomy, context-driven
adaptability, and decentralized decision making
Mechatronics is a multidisciplinary field of engineering that includes a combination
of systems engineering, mechanical engineering, electrical engineering,
telecommunications engineering, control engineering and computer engineering.
From https://en.wikipedia.org/wiki/Mechatronics
From https://www.researchgate.net/publication/233687942_BEYOND_ADVANCED_MECHATRONICS_NEW_DESIGN_CHALLENGES_OF_SOCIAL-CYBER-PHYSICAL_SYSTEMS
Friday, April 29, 16
28. System Engineering Process Design, Implementation, andVerification
From https://www.quora.com/Can-Software-Architects-learn-anything-from-Systems-Engineering
based on https://www.fhwa.dot.gov/cadiv/segb/views/document/sections/section3/3_4_2.cfm
Friday, April 29, 16
31. Dynamic Integrated CPS Platforms
From http://cps-vo.org/file/20908/download/60342
Friday, April 29, 16
32. Nine Major Design Challenges for Cyber-Physical Systems
From https://www.researchgate.net/publication/233687942_BEYOND_ADVANCED_MECHATRONICS_NEW_DESIGN_CHALLENGES_OF_SOCIAL-CYBER-PHYSICAL_SYSTEMS
(i) handling aggregative complexity
(ii) static and dynamic compositional synergy
(iii) dynamic and evolutionary operation in time
(iv) multi-abstraction based modeling
(v) system integrity verification and behavior validation
(vi) dynamic scalability towards meta-systems
(vii) transformation of big data
(viii) testable surrogate prototyping
(ix) robust social compliance
Friday, April 29, 16
33. Innovative Features of CPS Systems from NIST
From https://s3.amazonaws.com/nist-sgcps/cpspwg/pwgglobal/CPS_PWG_Draft_Framework_for_Cyber-Physical_Systems_Release_0_8_September_2015.pdf
Friday, April 29, 16
34. R&D Needs for High Confidence CPS
From http://www.seas.upenn.edu/~lee/10cis541/lecs/lec-CPS-1x2.pdf
Friday, April 29, 16
35. Six Propositions on the Design of Cyber-Physical Systems
From http://tinyurl.com/jyybtqe
Proposition 1: In general, we are still a considerable way away from having a transdisciplinary theoretical
framework for true CPSs and SCPSs, or even from elucidating the major principles by which they should
operate. (SCPS = Social-Cyber-Physical System)
Proposition 2: Several definitions of CPSs have been published and many systems have been realized, but
design, implementation and utilization of these systems are still perplexing, not to mention their possible
impacts on the society and the future implications.
Proposition 3:There is no consolidated design methodology known that could provide answer to the
discussed design challenges, systematize the consideration and management of the effects of long-term
learning and self-adaptation of CPSs, and to explain the principles of designing for semi-autonomous or
fully-autonomous operation.
Proposition 4:There is a huge knowledge gap concerning the design and engineering principles and
technologies of realizing high-end, non-linear CPSs (or sub-systems and components) that are
compositional, scalable, interoperable, and evolvable.
Proposition 5: New abstraction methodologies, as well as pre-implementation modeling, demonstration,
prototyping, and empirical testing methodologies are needed in particular for the investigation of
contextualized interactions with the human/social environment.
Proposition 6: Next generation CPSs are envisioned to be a horizontally and vertically heterogeneous
system of systems, having some level of reproductive intelligence. In order to advance the state-of-the-art,
both transdisciplinary insights and multi- disciplinary operative knowledge synthesis are needed.
Friday, April 29, 16
36. Principles for IoT Clouds
From http://www.infosys.tuwien.ac.at/research/viecom/papers/Truong2015Principles.pdf
Principle 1: Enable virtualization and composition of IoT components as units
Principle 2: Enable emulated/simulated IoT parts working with production cloud
services
Principle 3: Enable dynamic provisioning of IoT and cloud service units through
uniform marketplaces and repositories for multiple stakeholders
Principle 4: Provide multilevel software stack deployment and configuration
Principle 5: Provide software-defined elasticity and primitive governance
functions for all IoT and cloud service units
Principle 6: Provide monitoring and analysis for an end-to-end view on elasticity
and dependability properties
Principle 7: Coordinate elasticity to enable a coherent elastic execution
throughout the whole IoT cloud system
Friday, April 29, 16
48. Implication of IoT for the Future Internet from EU IERC
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
49. Billions of Connected Devices from EU IERC
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
50. Management of IoT for Robustness and Reliability from EU IERC
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
51. Intelligent Reasoning over IoT Data from EU IERC
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
52. IoT Timelines for Future Technology Developments from EU IERC
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
53. IoT Timeline from EU IERC continued
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
54. IoT Timeline from EU IERC continued
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
55. IoT Research Needs from EU IERC
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
56. IoT Research Needs from EU IERC continued
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
57. IoT Research Needs from EU IERC continued
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
•
Friday, April 29, 16
58. IoT Research Needs from EU IERC continued
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
59. IoT Research Needs from EU IERC continued
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
60. IoT Research Needs from EU IERC continued
From www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
68. Cyber-Physical Systems Requirements from NIST and NICT
From http://www2.nict.go.jp/univ-com/isp/doc/NIST.IR.7951.pdf
● Configurable/Agile
● Real Time Operation
● Big Data Support
● Reliable
● Decoupled
● Core Components
● Standardized Interfaces
● Software Defined Networks
● Secure & Private
● Social Interaction
● Synchronized Clocks
Friday, April 29, 16
69. Cyber-Physical Systems Components from NIST and NICT
From http://www2.nict.go.jp/univ-com/isp/doc/NIST.IR.7951.pdf
Friday, April 29, 16
82. System of Systems Definition
From http://www.slideshare.net/RealTimeInnovations/system-architecture-for-c4i-coalition-operations
Friday, April 29, 16
83. System of Systems Definition
From https://en.wikipedia.org/wiki/System_of_systems
System of systems is a collection of task-oriented or dedicated systems that pool their
resources and capabilities together to create a new, more complex system which offers
more functionality and performance than simply the sum of the constituent systems
A Global Earth Observation example from 2016.sosengineering.org
Friday, April 29, 16
84. System of Systems Engineering Definition
From https://en.wikipedia.org/wiki/System_of_systems_engineering
System-of-Systems Engineering and Systems Engineering are related but different fields
of study.Whereas systems engineering addresses the development and operations of
monolithic products, SoSE addresses the development and operations of evolving
programs. In other words, traditional systems engineering seeks to optimize an
individual system (i.e., the product), while SoSE seeks to optimize network of various
interacting legacy and new systems brought together to satisfy multiple objectives of the
program. SoSE should enable the decision-makers to understand the implications of
various choices on technical performance, costs, extensibility and flexibility over time;
thus, effective SoSE methodology should prepare decision-makers to design informed
architectural solutions for System-of-Systems problems.
Many cyber-physical systems are actually systems of systems, compositions of diverse
subsystems, typically developed by diverse teams, often from different organizations.
Modularity is the problem of designing subsystems (modules) with well-defined interfaces
that can be used in a variety of contexts. Composability is the ability to combine modules.
A related concern is compositionality, which refers to the ability to understand a composite
system by interstanding its components and how they are combined.
Modularity and Composability from CPS Concept Map at http://cyberphysicalsystems.org/
Friday, April 29, 16
85. Cyber-Physical System of Systems (CPSoS)
From www.cpsos.eu/wp-content/uploads/2014/12/CPSoS-Initial-Research-and-Innovation-Priorities-Document-Nov.-2014.pdf
Friday, April 29, 16
86. Hybrid and Heterogeneous Models
From Cyber-Physical Systems Concept Map at http://cyberphysicalsystems.org/
Cyber-physical systems are intrinsically heterogeneous.There are two distinct approaches
to modeling heterogeneous systems: (1) a grand unified theory (GUT) and (2) an abstract
semantics.The former is about developing a modeling language and conceptual framework
into which heterogeneous modeling languages and frameworks can be translated.The latter
is about developing interfaces between heterogeneous modeling languages that are
sufficient for interoperation, but not so rich that the interface language itself becomes a
modeling language.A GUT has the advantage of enabling model exchange between tools,
but the disadvantage that the semantic richness that is required to be able to encompass all
interesting heterogeneous modeling languages makes analysis of models difficult.An
abstract semantics has the advantage of enabling composition of domain-specific modeling
languages that are themselves sufficiently constrained that analysis is still possible, but the
disadvantage that engineers must learn a multiciplicity of modeling languages and must
understand how they interact within an abstract semantics.
Friday, April 29, 16
87. From RFIDs to CPS System of Systems from IERC
From http://www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
Friday, April 29, 16
88. CPS Conceptual Domain Model from NIST
From http://tinyurl.com/oyo7h9n
Friday, April 29, 16
89. CPSView of System of Systems from NIST
From http://tinyurl.com/oyo7h9n
Friday, April 29, 16
90. CPS and Smart Grid as System of Systems from NIST
From www.nist.gov/smartgrid/upload/SGAC-Meeting-Presentations.pdf
Friday, April 29, 16
91. Smart City as System of Systems from IBM
From http://www.urenio.org/2011/06/29/ibm-redbooks-smarter-cities-series/
Friday, April 29, 16
92. Wave Model of System of Systems Development
From http://tinyurl.com/jlvsrng
Friday, April 29, 16
93. Characteristics of System of Systems
From http://www.cpsos.eu/wp-content/uploads/2015/07/CPSoS-Scope-paper-vOct-26-2014.pdf
• Operational independence of the components of the overall system
• Managerial independence of the components of the overall system
• Geographical distribution
• Emerging behavior
• Evolutionary development process
Example: Global Earth Observation SoS from NOAA
http://www.esrl.noaa.gov/research/themes/observing/
Friday, April 29, 16
94. Characteristics of Cyber-Physical System of Systems
From http://www.seas.upenn.edu/~lee/10cis541/lecs/lec-CPS-1x2.pdf
Friday, April 29, 16
95. Characteristics of Cyber-Physical System of Systems
From http://www.cpsos.eu/wp-content/uploads/2015/07/CPSoS-Scope-paper-vOct-26-2014.pdf
• Large, often spatially distributed physical systems with complex dynamics
• Distributed control, supervision and management
• Partial autonomy of the subsystems
• Dynamic reconfiguration of the overall system on different time-scales
• Possibility of emerging behaviors
• Continuous evolution of the overall system during its operation.
Elements of Large-Scale CPS SoS (Bob Marcus)
• Heterogeneous sensors and actuators
• Human interfaces for data input, analysis, control, and collaboration
• Local networks and Internet
• Distributed data exchanges and messaging
• Multi-level analytics (streaming, historical)
• Cloud support for data integration, storage, and processing
Friday, April 29, 16
96. 16 Characteristics of Cyber-Physical System of Systems
From http://tinyurl.com/jyybtqe
1. CPSs are designed and implemented in order to support human activities and well- being by
decentralized cooperative problem solving, in harmony with the techno- econo-social environment,
2. CPSs consist of a digital cyber-part and an analog physical-part, which are supposed to work
together towards the highest possible level of functional and structural synergy,
3. CPSs are functionally decentralized and geographically distributed open systems with blurred overall
system boundaries,
4. CPSs are capable not only to dynamically reconfigure their internal structure and reorganize their
functionality/behavior, but also to change their boundaries,
5. CPSs are constructed of very heterogeneous sets of active components, which can enter and leave the
collective at any time, and may encounter other systems with similar or conflicting objectives,
6. CPSs, as well as their components, may work in extreme temporal ranges (from instantaneous to
quasi-infinite, and beyond), and manifest on various spatial scales (from intercontinental to nano-
scales),
7. Components are typically hybrid structures, encapsulating various compositions of hardware (e.g.
transformer and actuator) entities and embedded cyber (e.g. software and knowledge) entities,
8. Components may have predefined, emergent or ad-hoc functional connections, or all, with other
interoperable components at multiple levels,
Friday, April 29, 16
97. 16Characteristics of Cyber-Physical System of Systems continued
From http://tinyurl.com/jyybtqe
9. Components may operate according to different problem solving strategies (plans) towards achieving
the overall objective of the system,
10. Components are knowledge-intensive and able to handle built-in formal knowledge, knowledge
obtained by sensors, and knowledge generated by reasoning and learning mechanisms,
11. Components are able to make situated decisions and strive for automated problem solving by
gathering descriptive information and applying context-dependent causal and procedural reasoning,
12 Components are able to memorize and learn from history and situations in an unsupervised manner
and to specialize themselves based on smart software agents and emergent intelligence,
13 Components are able to adapt to unpredictable system states or emergent environmental
circumstances, as well as to execute non-planned functional interactions and to act proactively,
14 Overall decision-making is distributed over a large number of components (agents), and is based on
the reflexive interactions among the components and multi-criteria analysis (optimization),
15 Contrary to their distributed and decentralized nature, CPSs need to operate and communicate in
real-time and in a synchronized manner,
16 System resources are managed different sophisticated strategies and maintain security, integrity and
reliability of the components and the CPSs as a whole.
Friday, April 29, 16
98. System of Systems Requirements Management
From http://tinyurl.com/hod9jt6
Friday, April 29, 16
99. CPS System of Systems Security Characteristics
From http://www.slideshare.net/pfroberts/cyber-physical-systems-boston-2015-1
Friday, April 29, 16
100. CP SoS Technologies, Methodologies and Areas for Future Research
From http://www.cpsos.eu/wp-content/uploads/2015/07/CPSoS-Scope-paper-vOct-26-2014.pdf
• Requirements engineering and model-based systems engineering and validation and
verification over the system’s full life-cycle
• Modeling and large-scale simulation of heterogeneous systems of systems
• Partially autonomous decision making and system-wide control and coordination
• Collaborative decision making by computer systems and humans
• Fault detection, resilience, reconfiguration, and integration of new component
• Large-scale online data analysis and feature extraction (“artificial cognition”) for the
analysis and the dynamic management of systems of system
• Trust in large distributed systems
• Stability, structure formation and emergent behaviour in cyber-physical systems of systems
Friday, April 29, 16
101. Managing Scale and Heterogeneity in CPS SoS
From http://cps-vo.org/file/20908/download/60342f
Friday, April 29, 16
102. CPSOS Project Roadmap
From www.cpsos.eu/wp-content/uploads/2015/12/Roadmap-ICT-Info-Day-Brussels-Dec.-1-2015.pdf
Friday, April 29, 16
103. CPS System of Systems from IMC-AESOP Project
From http://imc-aesop.org/html/ProjDes.html
Friday, April 29, 16
104. Example: Vertical CPS SoS from Bob Marcus
Embedded
Systems
Networked
Embedded Systems
Internet of Things
Cyber-Physical Systems
Cyber-Physical-Social Systems
+ networking
+ multiple networks
+ analytics
+ collaboration
Devices
+ microprocessors
Friday, April 29, 16
105. Example: Horizontal CPS SoS from Bob Marcus
Embedded
Systems
Locally Networked
Embedded Systems
Devices
Embedded
Systems
Locally Networked
Embedded Systems
Devices
Embedded
Systems
Locally Networked
Embedded Systems
Devices
Friday, April 29, 16
106. Example: Horizontal andVertical CPS SoS from Bob Marcus
Embedded
Systems
Locally Networked
Embedded Systems
Devices
Embedded
Systems
Locally Networked
Embedded Systems
Devices
Embedded
Systems
Locally Networked
Embedded Systems
Devices
Embedded
Systems
LocallyNetworked
Embedded Systems
Devices
IP
IP
IP
IP
IP
IP IP
Internet of Things
Coordination
Internet of Things
Coordination
Cloud-based Analytics, Management, and Control for Cyber-Physical System of Systems
Friday, April 29, 16
107. A Key Problem for Engineering Large Scale CPS from Bob Marcus
A key problem for the future of Large Scale CPS System of Systems will be the
robust interfacing of heterogeneous systems. Some initial success should be
achievable horizontally and vertically across systems in the same domain using
emerging standardizations and tools. However applications such as Smart Cities
will require the interfacing of systems from many different domains with diverse
application models, data processing and control structures.
Friday, April 29, 16
108. Example: Horizontal andVertical CP SoS Smart City Architecture
From http://www.slideshare.net/SchneiderElectric/smart-city-anditregislargilier
Friday, April 29, 16
110. A complex system is any system featuring a large number of interacting
components (agents, processes, etc.) whose aggregate activity is nonlinear (not
derivable from the summations of the activity of individual components) and
typically exhibits hierarchical self-organization under selective pressures.
Complex Systems Definition
From http://www.informatics.indiana.edu/rocha/publications/complex/csm.html
From doursat.free.fr/pubs.html
Friday, April 29, 16
111. Scalability and Complexity Management
From Cyber-Physical Systems Concept Map at http://cyberphysicalsystems.org/
Cyber-physical systems are inherently heterogeneous, since at a minimum they combine
physical dynamics with computational processes. But they are often heterogeneous even
within the physical and cyber domains.The physical domain may be multi-physics, combining
for example mechanical motion control, chemical processes, biological processes, and
human operators.The cyber domain may combine networking technologies, programming
languages, software component models, and concurrency mechanisms. Software in CPS
applications can grow to very large systems.The challenge, therefore, is to provide design
methodologies and tools that support those methodologies, that scale to large designs,
facilitate analysis, and promote understanding of complex systems.The problems include:
• Systems engineering;
• Software engineering processes;
• Software engineering technologies (refactoring tools, program analysis, etc.)
• Design tools;
• Co-simulation technologies;
• Model exchange.
Friday, April 29, 16
112. Tradition System Engineering vs. Complex Systems Engineering
From https://www.mitre.org/sites/default/files/pdf/norman_engineering.pdf
Friday, April 29, 16
113. Some Possible Complex Systems Characteristics from Wikipedia
From https://en.wikipedia.org/wiki/Complex_system
• Feedback loops
• Some degree of spontaneous order
• Robustness of the order
• Emergent organization
• Numerosity [large scale]
• Hierarchical organization
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114. Complex Systems Properties from Bob Marcus
From http://www.slideshare.net/bobmarcus/2010-complex-systems-engineering-for-the-global-information-grid
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115. Complex Systems Characteristics
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From http://www.acq.osd.mil/se/webinars/2010-03-09-SoSECIE-MS-Emergent-Behaviors-Zentner-etal-brief.pdf
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116. Complex Systems Interaction Patterns from Bob Marcus
From http://www.slideshare.net/bobmarcus/2010-complex-systems-engineering-for-the-global-information-grid
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117. Complex Systems Questions from 1995 from Bob Marcus
From http://www.slideshare.net/bobmarcus/2010-complex-systems-engineering-for-the-global-information-grid
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118. Definition of Complex Adaptive Systems from MIT
From http://web.mit.edu/esd.83/www/notebook/Complex%20Adaptive%20Systems.pdf
Complex Adaptive Systems include many natural systems (e.g., brains, immune systems,
ecologies, societies) and increasingly, many artificial systems (parallel and distributed
computing systems, artificial intelligence systems, artificial neural networks, evolutionary
programs) that are characterized by apparently complex behaviors that emerge as a result of
often nonlinear spatio-temporal interactions among a large number of component systems at
different levels of organization.
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119. Complex Systems Organization Map from Wikipedia
From https://en.wikipedia.org/wiki/File:Complex_systems_organizational_map.jpg
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122. Specification, Modeling, and Analysis for CPS
From Cyber-Physical Systems Concept Map at http://cyberphysicalsystems.org/
Cyber-physical systems are intrinsically concurrent.At a minimum, the cyber and the
physical subsystems coexist in time, but even within these subsystems, concurrent
processes are common. Models of concurrency in the physical world (coexisting physical
dynamics in a time continuum) are very different from models of concurrency in software
(arbitrary interleaving of sequences of atomic actions), and very different from models of
concurrency in networks (asynchronous, partially-ordered discrete actions or clock-driven
time slots). Reconciling these divergent models of concurrency, and ensuring
interoperability and communication between components that have divergent models of
concurrency, is a central problem in CPS.
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123. Adaptive and Predictive Control for CPS
From Cyber-Physical Systems Concept Map at http://cyberphysicalsystems.org/
CPS systems are typically closed-loop systems, where sensors make measurements
of physical processes, the measurements are processed in the cyber subsystems,
which then drive actuators that affect the physical processes.The control strategies
implemented in the cyber subsystems need to be adaptive (responding to changing
conditions) and predictive (anticipating changes in the physical processes).
Model Reference Adaptive Control (MRAC) from Drexel U.
From http://www.pages.drexel.edu/~kws23/tutorials/MRAC/MRAC.html
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124. Model-Based Systems Engineering
From http://www.lboro.ac.uk/research/avrrc/research/currentprojects/bigdata/platform-independent-model-driven-architectures-for-future-vehicle-systems.html
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125. Aspects of Modeling CPS
From https://modelica.org/events/modelica2011/Proceedings/pages/papers/20_1_ID_121_a_fv.pdf
• The scale and complexity of CPS require compositional methods for integrated design
and modeling
• Most CPS applications are built up from controlled sub-systems, systems-of-systems,
with local interaction between physical systems and controllers affecting global
performance.
• Distributed sensing, actuation, and control needs to be modeled and simulated.
• The interfaces between the cyber and physical needs to be identified and properties of the
interfaces should be easy to specify.
• New hybrid models may be needed to completely model all aspects of CPS
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126. Open META CyPhy Toolchain from DARPA
From http://www.slideshare.net/joe_porter/verification-cps
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127. Model-Based Design for CPS fromVanderbilt U
From http://www.nist.gov/el/msid/upload/1Neema_Model-Based-Tools-for-Design-releaseversion.pdf
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128. Model Based Systems Engineering (MBSE)
From http://www.iaria.org/conferences2014/filesICONS14/ICONS-Austin-Keynote2014-02-07.pdf
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129. Mixing Formal and Semi-Formal Approaches in MBSE
From http://www.iaria.org/conferences2014/filesICONS14/ICONS-Austin-Keynote2014-02-07.pdf
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130. Layered Diagram for Standardized Systems and Service Development
From http://www.et-strategies.com/great-global-grid/SE-SOA-MS.pdf
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131. MultiScale Modeling Definition
From https://en.wikipedia.org/wiki/Multiscale_modeling
In engineering, mathematics, physics, meteorology and computer science, multiscale
modeling or multiscale mathematics is the field of solving problems which have
important features at multiple scales of time and/or space
Physical Scales for CPSfrom http://tinyurl.com/jyybtqe
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132. Multiscale Coupling Library and Environment
From http://www.qoscosgrid.org/trac/muscle
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133. Features of a CPS Design and M&S Framework
From http://www.cs.virginia.edu/sigbed/archives/2008-01/JKim.pdf
• Heterogeneous (in terms of various types of sensors/actuators) applications support: CPS usually consists of non-
homogeneous applications.Thus, it should be able to simulate heterogeneous application logics simultaneously.
• Various physical modeling environments: the physical modeling environment should support mathematical
expressions and incorporate domain specific physical modeling descriptions (e.g. floor plan of buildings) by
extracting relevant information from them.
• Scalability support: support for the development and simulation ranging from small scale (tens) to large scale
(thousands of) sensors and actuators.
• Mobility support: support for modeling systems using relevant properties (e.g., communication, signal strength).
• Integration of existing simulation tools: easy-to-use support to link to existing simulation tools is required.
• Integration of proprietary solutions and open standard support: proprietary solutions and open standards including
protocols, infrastructures and existing software should be able to be easily incorporated into a generic framework.
• Software reuse: a generic framework should support software reuse either by exploiting code generation
techniques (which can also use proprietary infrastructure), linking libraries or using configurable components.
• Usability: Graphical representation of modeling and simulation environment can enable easy development of new
applications. Domain-specific3D modeling environments can also be supported depending upon requirements.
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134. Modeling and Simulation Enhanced SoS Engineering by Bob Marcus
From http://www.et-strategies.com/great-global-grid/emerging-trends.pdf
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135. Simulation for CPS
From Cyber-Physical Systems Concept Map at http://cyberphysicalsystems.org/
Simulation is the process of validating a design by imitating its behavior for a given set of
inputs. Particular CPS challenges include
• Heterogeneous simulation: co-simulation of diverse physical and cyber subsystems;
• Multiresolution simulation: cosimulating subsystems expressed at different levels of
abstraction or with different time scales and precisions;
• Models of time: distributed cyber-physical systems cannot precisely share a single
measurement of time, and discrepancies in their measurements can lead to unexpected
artifacts, so simulators need to accurately models these discrepancies.
• Hardware in the loop simulation:This is where a subsystem simulation interacts in real
time with hardware realizing either a physical or a cyber susbsystem.
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136. Need for IoT Simulations
From http://futureinstruments.ornl.gov/pdfs/T1_N%201420%20IOT_FIIW15_050415.pdf
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137. Unique Requirements of IoT Simulations
From http://futureinstruments.ornl.gov/pdfs/T1_N%201420%20IOT_FIIW15_050415.pdf
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138. SimpleIoTSimulator Supported Protocols
From http://www.smplsft.com/SimpleIoTSimulator.html
• CoAP is an IETF proposed standard for retrieving and managing information for sensors and devices in a
constrained environment.The simulator can "learn" from existing CoAP sensors/devices to duplicate customer
environments, or use the learnt data as template to create thousands of sensors and gateways.
• MQTT is a publish/subscribe based protocol. Both MQTT ver 3.1 and ver 3.1.1 clients are supported and simulated
sensors can be setup to periodically publish messages to a specified broker.A built-in learner utility is also included
that subscribes to a broker and learns messages for subseqent replay.
• MQTT-SN is a variation of MQTT for Sensor Networks that has a more compact packet encoding. Like MQTT,
simualted sensors can be setup to periodically publish MQTT-SN client messages to a specified broker and a built-in
learner utility is included to learn messages for subsequent replay.
• MQTT-Broker receives MQTT subscribe requests from applications within the cloud/platform and sends publish
messages to them.The simulator supports this functionality to simulate thousands of gateways.
• HTTP/s client sends periodic XML/REST requests to cloud/platform servers.The simulator includes learner
applications to learn http requests and periodically send them to specified servers for simulating thousands of
gateways.
• HTTP/s server responds to incoming HTTP requests with responses.The simulator can be setup using learnt
data for simulating gateways that support this functionality.
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139. Simulation Testbed Model from ORNL
From http://futureinstruments.ornl.gov/pdfs/T1_N%201420%20IOT_FIIW15_050415.pdf
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140. Federated CPS Simulations from NIST
From http://www.nist.gov/smartgrid/upload/SGAC-Meeting-Presentations.pdf
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141. M&S for Complex Networked Systems (Research Areas from DOE)
From http://science.energy.gov/~/media/ascr/pdf/program-documents/docs/Complex_networked_systems_program_final.pdf
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142. FIESTA IoT Testbed from Europe
From www.com4innov.com/var/input/FileManager/PAGES_WEB/FIESTA/FIESTA_Fact_Sheet 2015.pdf?PHPSESSID=0onnd2irqan1o82b9pc4sg9bj6
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143. FIESTA IoT Testbed from Europe
From www.com4innov.com/var/input/FileManager/PAGES_WEB/FIESTA/FIESTA_Fact_Sheet 2015.pdf?PHPSESSID=0onnd2irqan1o82b9pc4sg9bj6
Federation Model for IoT Testing
Experiment as a Service Scenario
IoT Functional Blocks Architecture
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144. References
Inventory of all Bob Marcus CPS Slides on Slideshare
http://www.slideshare.net/bobmarcus/inventory-of-my-cps-slide-sets
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145. Reference Links (Systems Engineering for CPS)
Institute for Software Integrated Systems (ISIS) Lab Vanderbilt CPS Research
http://www.isis.vanderbilt.edu/research/SST
Workshop on Cyber-Physical System Engineering
https://es-static.fbk.eu/events/eitcpse2013/program.php
Cyber-Physical Systems Design Challenges
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.156.9348&rep=rep1&type=pdf
Cyber-Physical Society
http://www.computer.org/csdl/proceedings/wetice/2015/7692/00/7692a114.pdf
Engineering Cyber-Physical Systems Book
http://link.springer.com/chapter/10.1007%2F978-3-642-34404-6_1#page-1
Cyber Physical Systems: The Next Computing Revolution
http://www.seas.upenn.edu/~lee/10cis541/lecs/lec-CPS-1x2.pdf
Cyber-Physical Systems Design Challenges
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.156.9348&rep=rep1&type=pd
Mechatronics for Cyber-Physical Systems
http://ade.sagepub.com/content/6/591629.full
Top 49 tools for the Internet of Things
https://blog.profitbricks.com/top-49-tools-internet-of-things/
IoT Open Platforms
http://open-platforms.eu/libraries/
Patterns for building IoT systems from Iron.Io
http://resources.idgenterprise.com/original/AST-0162936_Ironio-IoT-Whitepaper-Jun2015_4.pdf
AT&T Collaboration with Cisco, Microsoft, and Intel
http://about.att.com/story/att_grows_iot_ecosystem.html
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146. Reference Links (CPS System of Systems and Consortia)
Cyber-Physical Systems Engineering with Examples of Systems of Systems
http://www.utexas.edu/research/cem/smartgrid_images/UTSA%20Forum/Jamshidi%20-%20UT_Austin_Lec.pdf
AMADEOS (Architecture for Multi-Critically Agile Dependable Evolutionary Open System of Systems)
http://amadeos-project.eu/project/
System of Systems Project from Europe
https://ec.europa.eu/digital-agenda/en/system-systems
Cyber-Physical Virtual Organization
http://cps-vo.org/
Road2CPS Initiative in Europe
http://www.road2cps.eu/#
CPS Engineering Labs in Europe
https://ec.europa.eu/digital-agenda/en/news/cyber-physical-systems-engineering-labs-first-open-call-innovation-projects
http://www.cpse-labs.eu/
Smart Anything Everywhere Initiative in Euope
https://smartanythingeverywhere.eu/
EuroCPS Projects
https://www.eurocps.org/
European Research Cluster on IoT (IERC)
http://www.internet-of-things-research.eu/
Downloadable Book on Building the Hyper-Connected Society from IERC
http://www.internet-of-things-research.eu/pdf/Building_the_Hyperconnected_Society_IERC_2015_Cluster_eBook_978-87-93237-98-8_P_Web.pdf
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147. Reference Links (CPS as Complex Systems)
Building Self-Adaptive Cyber-Physical Systems using Unreliable Components
http://www.ee.washington.edu/research/nsl/aar-cps/WeisongShi-20081015085116.pdf
Complex Adaptive System of Systems Engineering
www.sandia.gov/casosengineering/
Complex Adaptive Systems Modeling
http://www.casmodeling.com/
Complex Systems Engineering for the Global Information Grid
http://www.slideshare.net/bobmarcus/2010-complex-systems-engineering-for-the-global-information-grid
Progress on Complex Cyber-Physical Systems Engineering
http://www.slideshare.net/ghackenberg/research-group-seminar-progress-on-complex-cyberphysical-systems-engineering
Multiple Drones Collision Avoidance
haoyi.io/projects/short-term-conf-reso.pdf
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148. Reference Links (Modeling and Simulation for CPS)
Modelica for CPS
https://modelica.org/events/modelica2011/Proceedings/pages/papers/20_1_ID_121_a_fv.pdf
Model Based Systems Engineering for System of Systems using Agent-Based Modeling
http://tinyurl.com/jlvsrng
DPWSim: A Simulation Toolkit for IoT Applications
http://servicearchitecture.wp.tem-tsp.eu/files/2014/10/DPWSim-A-Simulation-Toolkit-for-IoT-Applications-using-
A Framework for Multiscale-Multiscience Modeling and Simulation from University of Geneva
http://www.lorentzcenter.nl/lc/web/2013/569/presentations/03.%20Chopard.pdf
Using Complex Event Processing for the Modeling and Simulation of Cyber-Physical Systems
https://www.researchgate.net/publication/264815514_Using_complex_event_processing_for_modelling_and_simulation_of_cyber-physical_systems
Model Based Design of Cyber-Physical Systems using MATLAB and Simulink
www.mathworks.com/discovery/cyber-physical-systems.html?requestedDomain=www.mathworks.com
Methodology for Design, Integration, Modeling, and Simulation of Cyber-Physical Systems SIG
https://www.designsociety.org/ds_group/30/methodology_for_design_integration_modeling_and_simulation_of_cyber_physical_systems_design_cps
Modeling and Simulation of Complex Systems
http://eco83.econ.unito.it/dottorato/michele_sonnessa/sonnessa_thesis.pdf
CoSMo Complex Systems Modeling
http://www.thecosmocompany.com/technology
CoSMoS Process for Complex Systems Modeling and Simulation from University of York
http://offog.org/publications/york2010-cosmos.pdf
Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA) in Europe
http://www.com4innov.com/var/input/FileManager/PAGES_WEB/FIESTA/FIESTA_Fact_Sheet%202015.pdf?PHPSESSID=0onnd2irqan1o82b9pc4sg9bj6
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