Everything 4.0? Drivers and Challenges of Cyber Physical Systems Forschungsdialog Rheinland Invited Talk Wuppertal, December 4th, 2013 Univ.-Prof. Dr. rer. nat. Sabina Jeschke IMA/ZLW & IfU Faculty of Mechanical Engineering RWTH Aachen University www.ima-zlw-ifu.rwth-aachen.de
Outline 2 I. Cyber-Physical Systems... The term, its predecessors, its definition and its relatives II. III. IV. The fourth industrial (r)evolution From power revolutions towards a networked world About scientific challenges and achievements Challenges and approaches to interoperability and integrated intelligence Summary
The term and its predecessors: Teleautomation 3 1926 Nikola Tesla Teleautomation When wireless is perfectly applied the whole earth will be converted into a huge brain, which in fact it is, [ ] and the instruments through which we shall be able to do this will be amazingly simple compared with our present telephone. A man will be able to carry one in his vest pocket. Nikola Tesla, Teleautomation, USA Wardenclyffe Tower (built for broadcast energy experiments) remote-controlled unmanned aerial vehicle (unmanned boat) by Tesla, 1898
The term and its predecessors: and other Steps towards Cyber-physical systems 4 1926 Nikola Tesla Teleautomation 1948 Norbert Wiener Cybernetics 1961 Charles Stark Draper Apollo Guidance Computer one of the first embedded systems 1988 Mark Weiser Ubiquitous computing 1999 Kevin Ashton Internet of Things 2006 Helen Gill Cyber-physical systems Cyber-physical systems are physical, biological, and engineered systems whose operations are integrated, monitored, and/or controlled by a computational core. Components are networked at every scale. Computing is deeply embedded into every physical component, possibly even into materials. The computational core is an embedded system, usually demands real-time response, and is most often distributed. Helen Gill, NSF, USA Google Trends (October 2012): Cyber-physical systems
Cyber-physical systems and related terms: Internet of Things 5 Cyber-physical systems OR Internet of things? The frontier between CPS and Internet-of-Things has not been clearly identified since both concepts have been driven in parallel from two independent communities, although they have always been closely related. [Koubâa, 2009] A quick glance into the web: Cyber Physical System is the US version of the internet of things Internet of Things & Services, M2M or cyber physical systems are much more than just buzzwords for the outlook of connecting 50 billions devices by 2015. The term Internet of Things, originally aiming at RFID technologies, is smoothly becoming synonymous for cyber-physical systems. Vision: Internet of Things, Data and Services e.g. Smart City Vision: Cyber physical systems e.g. intelligent networked road junction
Cyber-Physical Systems and related terms: Internet of Things & Industry 4.0 6 Cyber-Physical Systems OR Internet of things? Shared Distinct Vision large-scale distributed computing systems of systems computation and intelligence is not decoupled from environment Scientific Community Internet of Things driven from computer sciences, Internet technologies driven by EC Cyber-Physical System driven from engineering aspects driven by the NSF Core Technology internet as large-scale network embedded systems (= intelligent components) Philosophy, focus Internet of Things focusing on openness and on the network - virtuality Cyber-Physical System focusing on the physical process behind, often a closed-loop system For all practical purposes: Today: more or less synonym Industry 4.0 as a special field of application
Cyber-Physical Systems Towards complex and networked social-technical systems 7 let s have a look Communication Consumer Energy Infrastructure Health Care Manufacturing Military Robotics Transportation [CAR2CAR, 2011] and [ConnectSafe, 2011]
Outline 8 I. Cyber-Physical Systems... The term, its predecessors, its definition and its relatives II. III. IV. The fourth industrial (r)evolution From power revolutions towards a networked world About scientific challenges and achievements Challenges and approaches to interoperability and integrated intelligence Summary
The fourth industrial (r)evolution Industry 4.0 - Everybody and everything is networked 9 The first three industrial revolutions came about as a result of mechanisation, electricity and IT. The introduction of the Internet of Things is ushering in a fourth industrial revolution. Industry 4.0 will address and solve some of the challenges facing the world today such as resource and energy efficiency, urban production and demographic change. Henning Kagermann et.al., acatech, 2013 Vision of Wireless Next Generation System (WiNGS) Lab at the University of Texas at San Antonio, Dr. Kelley Weidmüller, Vission 2020 - Industrial Revolution 4.0 Intelligently networked, self-controlling manufacturing systems) local to global local to global around 1750 around 1900 around 1970 today 1 st industrial revolution Mechanical production systematically using the power of water and steam Power revolution Centralized electric power infrastructure; mass production by division of labor Digital revolution Digital computing and communication technology, enhancing systems intelligence Information revolution Everybody and everything is networked networked information as a huge brain
The fourth industrial (r)evolution The Drivers. Communication technology bandwidth and computational power Semantic technologies information integration Embedded systems miniaturization Watson 2011 10 Google Car 2012 Towards intelligent and (partly-) autonomous systems AND systems of systems around 1750 around 1900 around 1970 1 st industrial revolution Mechanical production systematically using the power of water and steam Power revolution Centralized electric power infrastructure; mass production by division of labor Digital revolution Digital computing and communication technology, enhancing systems intelligence today Information revolution Everybody and everything is networked networked information as a huge brain
The fourth industrial (r)evolution Not Restricted to Industry: Cyber Physical Systems in All Areas 11 Back to: The earth converted into a huge brain (Tesla 1926) Integrating complex information from multiple heterogenous sources opens multiple possibilities of optimization: e.g. energy consumption, security services, rescue services as well as increasing the quality of life Building automation Smart metering Smart grid Room automation Smart environment and more
Outline 12 I. Cyber-Physical Systems... The term, its predecessors, its definition and its relatives II. III. IV. The fourth industrial (r)evolution From power revolutions towards a networked world About scientific challenges and achievements Challenges and approaches to interoperability and integrated intelligence Summary
Scientific challenges and achievements Two worlds coming together 13 Physical world Cyber-physical Digital world Manufacturing process Embedded Systems Material behavior Simulation Automation Unique Identifier Semantics Things Internet Service-oriented Closed System controllable and partly predictable by simulation CPS Open System difficult to control or to predict system behavior IOT
Scientific challenges and achievements Two worlds coming together 14! Timed communication and information exchange! Time delayed communication! Well-known and controlled interaction between participants VS! Interaction between unknown participants! Static (changes are controlled)! Dynamic (continuously changing) Beyond traditional technical systems: Systems of distributed intelligence Closed System controllable and partly predictable by simulation CPS Open System difficult to control or to predict system behavior IOT
Facial Emotion Software Speech Touch Hardware Scientific challenges and achievements Beyond traditional technical systems: organic computing 15 following social systems and biological models! Large heterogeneous systems = societies Division of labor Specializations Learning and reasoning Macro-scale Automation Micro-scale Multi-Core Multimodal communication modes Service oriented Agentbased Fraunhofer IOSB Adaptive behavior Motivation sciencedirect.com Affective Computing Michael S. Ryoo, CalTech Architectural models Miniaturization and energy supply Design principles Measuring and actuation Security Modeling and simulation Interoperability
The way so far and beyond Beyond traditional technical systems: organic computing 16 inspired by social systems and biological models SOFTWARE - Algorithms HARDWARE - Robotics Genetic Algorithms Neural networks [Gar3t, 2010] and [Robbins, 2010] Evolution Awareness Self-replication [Bongard, 2006; Lipson, 2007] Zykov V., Mytilinaios E., Adams B., Lipson H. (2005) "Self-reproducing machines", Nature Vol. 435 No. 7038, pp. 163-164 Bongard J., et al., Resilient Machines Through Continuous Self-Modeling, Science 314, 2006 Lipson H. (2005) "Evolutionary Design and Evolutionary Robotics", Biomimetics, CRC Press (Bar Cohen, Ed.) pp. 129-155
Scientific challenges and achievements Towards energy harvesting 17 Powering wireless sensor nodes Energy (or power) harvesting or (scavenging) is without any doubt a very attractive technique for a wide variety of self-powered microsystems. Examples of such systems are wireless sensors, biomedical implants,... [Harb, 2011] Indefinitely operation of sensor nodes by energy harvesting Goal: wireless autonomous devices Multiple kinds of energy harvesting [Pimparel, 2012]! Photovoltaic Energy Harvesting! Kinetic Energy Harvesting! Thermoelectric Energy Harvesting from environment and machines up to human energy harvesting Besides others: Mani Srivastava Professor of Electrical Engineering & Professor of Computer Science, Circuits and Embedded Systems, UCLA Architectural models Miniaturization and energy supply Design principles Measuring and actuation Security Modeling and simulation Interoperability
Scientific challenges and achievements Towards nanotechnology and nanomaterials 18 Graphene - the silicon of the 21st century novel nanomaterials show new properties not observed at the micro level, Amongst others, graphene, [ ], has been lately referred to as the silicon of the 21 st century. The unique optical and electronic properties of this nanomaterial enable the development of a new generation of electronic devices. [Jornet, 2012] Internet of Multimedia Nano-Things (IoMNT, Georgia Tech) [Jornet, 2012] Microcontrollers realized by nanodevices, at nano-size Interconnection between nano-device and communication networks!! Enables more advanced applications (e. g. in biomedicine or security) Solutions for each component of the IoMNT has been proposed Besides others: Ian F. Akyildiz School of Electrical and Computer Engineering, GATECH Architectural models Miniaturization and energy supply Design principles Measuring and actuation Security Modeling and simulation Interoperability
Scientific challenges and achievements Rethinking concurrency and time predictability in hardware 19 HARDWARE approaches [Current] instruction set architectures do not constrain time properties of instructions [ ]. Precision Timed ARM (PTARM) provides timing predictability and composability without sacrificing performance. [Liu, 2012] Besides others: Edward A. Lee, Berkeley, Elect. Eng. Dep. Precision Timed ARM (PTARM/Berkeley)! Extended instruction-set architectures (ISA) with control over execution time! Time as a central design principle, new language elements Time behavior controllable by extended instruction set [Liu, 2012] ON TOP: PTIDES as programming model Architectural models Miniaturization and energy supply Design principles Measuring and actuation Security Modeling and simulation Interoperability
Scientific challenges and achievements Rethinking concurrency and time predictability in software 20 SOFTWARE approaches Most real-time software is structured either as threads with priorities or as tasks with periods or deadlines. Zhao et al. proposed an alternative programming model [ ] that structures real-time software as an interconnection of actors communicating using timestamped events. PTIDES leverages network time synchronization to provide a coherent global temporal semantics in distributed systems. [Zou, 2012] Besides others: Edward A. Lee, Berkeley, Elect. Eng. Dep. Programming Temporally Integrated Distributed Embedded Systems (PTIDES/Berkeley) Global distributed time model Coherent global temporal semantics Actor-based communication PTIDES Workflow Integration of technical system with regard to real time Validation of satisfaction of temporal semantics Architectural models Miniaturization and energy supply Design principles Measuring and actuation Security Modeling and simulation Interoperability
Scientific challenges and achievements Metamaterials new possibilities beyond nature 21 Artificial materials Metamaterials are artificial materials engineered to have properties that may not found in nature. [ ] Their precise shape, geometry, size, orientation and arrangement in a periodic pattern can affect the waves of light or sound in an unconventional manner [ ]. [Richard W. Ziolkowski, 2006] Wikipedia characterized by a negative refractive index! realized by materials arranged in specialized periodic patterns very perfect structures! Manifold applications of metamaterials: Invisibility, new design of antennas, remote aerospace applications, ultrasonic sensors, infrastructure monitoring, superlenses with a spatial resolution below the wave length, Besides others: Thomas Bertuch, Fraunhofer-Institut für Hochfrequenzphysik und Radartechnik FHR, Wachtberg Architectural models Miniaturization and energy supply Design principles Measuring and actuation Security Modeling and simulation Interoperability
Scientific challenges and achievements Rethinking concurrency and time predictability in software 22? Meaning of security goals in CPS [Cárdenas, 2008, 2011, Berkeley] Confidentiality Availability! to keep information secret from unauthorized users more or less: the old challenge! maintain the operational goals by preventing or surviving denial of service attacks to the information collected by the sensor networks, and the physical actions taken by actuators. Enhanced problem: sensors are public, and simple-minded @www.arduino.cc! Integrity maintain the operational goals by preventing, detecting, or surviving deception attacks in the information sent and received by the sensor, the controllers, and the actuators. Enhanced problem: extension of mobile phone in plane Models of Attack: formal description of integrity and DoS attacks (upper/lower boundaries, time behaviour etc.) Detection of Attacks: Anomaly-based intrusion analysis based on physical models validate output sequence against representative model Architectural models Miniaturization and energy supply Design principles Measuring and actuation Security Modeling and simulation Interoperability
Scientific challenges and achievements Modeling and simulation of complex systems 23 Bottom-up: Agent-based modeling Model using autonomous agents [Lin, 2010 (on CPS)] Dynamically interacting rule-based agents!!! Established method to simulate large distributed systems Good at predicting appearance of complex phenomena Good at modeling of dynamically changing participants Open Agent Based Modeling Consortium (www.openabm.org) Swarm Development Group (www.swarm.org) More information www.agent-based-models.com Architectural models Miniaturization and energy supply Design principles Measuring and actuation Security Modeling and simulation Interoperability
Scientific challenges and achievements Semantic heterogeneity : different approaches 24 A something-in-between approach Abstract semantics is about developing interfaces between heterogeneous modeling languages that are sufficient for interoperation [www.icyphy.org, 2013]! Besides others:, D. Schilberg, T. Meisen, RWTH Abstract domain semantics for translation Translation Domain Ontology Domain Ontology Semantic annotation Interface Ontology Semantic annotation Concrete data (product design) Transformation Concrete data (resource planning) Architectural models Miniaturization and energy supply Design principles Measuring and actuation Security Modeling and simulation Interoperability
Scientific challenges and achievements Concept Map of Cyber-Physical Systems 25 cyberphysicalsystems.org (Broman, Lee, Torngren, Sunder) As a discipline, CPS is an engineering discipline, focused on technology, with a strong foundation in mathematical abstractions. The key technical challenge is to conjoin abstractions that have evolved over centuries for modeling physical processes (differential equations, stochastic processes, etc.) with abstractions that have evolved over decades in computer science (algorithms and programs [ ]) [CPS, 2013]
Outline 26 I. Cyber-Physical Systems... The term, its predecessors, its definition and its relatives II. III. The fourth industrial (r)evolution From power revolutions towards a networked world About scientific challenges and achievements Challenges and approaches to interoperability and integrated intelligence IV. Summary
Examples from the institute cluster e.g., Electronically Coupled Vehicle Convoys, 27 The KONVOI Project (RWTH & partners) automated / partly autonomous transportation e.g. by electronically coupling trucks to convoys several successful test have been conducted with trucks (KONVOI, SARTRE (EU), Energy-ITS (Japan)) advanced driver assistance system for trucks short distances between vehicles of approx. 10m at a velocity of 80 km/h expected improvements: reduction of fuel consumption; improved vehicle occupancy; gained road space; optimization of traffic flow; relief for professional drivers; increase in safety
Examples from the institute cluster e.g., by increasing the efficiency of SME freight cooperation, 28 The CloudLogistic Project (RWTH & partners) Innovative line-based logistics concept: combining Less Than Truckload (LTL) shipments of cooperating Logistic Service Provider (LSP) Pickups along the baseline realize direct transportation with no need for a centralized HUB-structure Using geographical information of origin and destination combined with a time-based approach to calculate needed distances Using heuristical approaches (e. g. genetic solvers) to handle the implied Constraint Satisfaction Problems (CSP) Using data mining techniques to improve the networks structure in a cybernetic way
Examples from the institute cluster and from single boxes to intelligent container families! 29 The TelliBox/TelliSys Projects (RWTH & partners) Intelligent MegaSwapBoxes for Advanced Intermodal Freight Transport focus on the development of a 45 feet intermodal transport unit 3m internal height (using a specialized chassis and towing vehicle from DAF) ideas and contributions of freight forwarders, shippers, rolling stock manufacturers and scientists successfully tested on over 5000 km long trimodal demonstration track 7 th EU Framework Program, 2009-2015 working at a market introduction Further steps: intelligence and cognition concerning a) route and b) inner condition
Summary Research on an Interdisciplinary Sandwich with Changing Paradigms 30 Embedded systems miniaturization Semantic technologies - representing the field of artificial intelligence information integration Communication technology bandwidth and computational power Physical world Cyber-physical Digital world Reactivity Schedulability Diagram adopted from R. Alur, Uni. Penn Material beyond nature Time Concepts in Computer Sciences CPS Bottom up vs. Top down Living with Uncertainty @ ABB Bionic Principles IOT
Summary Not only science 31 Diversity young to old, able and disabled, rich and poor, literate and illiterate, Expectations 24/7 availability, 100% reliability, 100% connectivity, instantaneous response, store anything and everything forever Privacy Individual private levels, context-situative Science Society How can we provide people and society with Cyber-Physical Systems they can trust? Technology Challenges Boundaries are unknown and always changing Complex systems are unpredictable (How) can we build systems that interface between the cyber world and the physical world? Ideally, with predictable, or at least adaptable behavior. Adapted from [CPS Summit, 2008]
Summary Leading to interdisciplinary science and education 32 In the 2nd industrial revolution, we have been networking the resources of power. In the 4th industrial revolution, we will network the resources of intelligence. Hybrid systems Network technologies Usability Human-computer interaction [, EuMW, 2013] Embedded systems Software engineering Formal methods Security and privacy Physics Real-time systems Biology The innovation and development of Cyber-Physical Systems will require computer scientists and network professionals to work with experts in various disciplines. This, [ ], will revolutionize how universities educate engineers and scientists. Control systems [Rajkumar, Cyber-Physical Systems: the next computing revolution, 2010] Academic Medical Sciences Neurosciences Mechanical Engineering Mathematics Chemistry Statistics Artificial Intelligence Robotics Electrical engineering Cognitive Sciences Operations research Automation Psychology Civil Engineering Sociology Behaviour Anthropology Philosophy
33 Thank you! Univ.-Prof. Dr. rer. nat. Sabina Jeschke Head of Institute Cluster IMA/ZLW & IfU phone: +49 241-80-91110 sabina.jeschke@ima-zlw-ifu.rwth-aachen.de Co-authored by: Dr.-Ing. Tobias Meisen Institute Cluster IMA/ZLW & IfU phone: +49 241 / 80 91139 tobias.meisen@ima-zlw-ifu.rwth-aachen.de www.ima-zlw-ifu.rwth-aachen.de
Prof. Dr. rer. nat. Sabina Jeschke 34 1968 Born in Kungälv/Schweden 1991 Birth of Son Björn-Marcel 1991 1997 Studies of Physics, Mathematics, Computer Sciences, TU Berlin 1994 NASA Ames Research Center, Moffett Field, CA/USA 10/1994 Fellowship Studienstiftung des Deutschen Volkes 1997 Diploma Physics 1997 2000 Research Fellow, TU Berlin, Institute for Mathematics 2000 2001 Lecturer, Georgia Institute of Technology, GA/USA 2001 2004 Project leadership, TU Berlin, Institute for Mathematics 04/2004 Ph.D. (Dr. rer. nat.), TU Berlin, in the field of Computer Sciences from 2004 Set-up and leadership of the Multimedia-Center at the TU Berlin 2005 2007 Juniorprofessor New Media in Mathematics & Sciences & Director of the Media-center MuLF, TU Berlin 2007 2009 Univ.-Professor, Institute for IT Service Technologies (IITS) & Director of the Computer Center (RUS), Department of Electrical Engineering, University of Stuttgart since 06/2009 Univ.-Professor, Institute for Information Management in Mechanical Engineering (IMA) & Center for Learning and Knowledge Management (ZLW) & Institute for Management Cybernetics (IfU), RWTH Aachen University since 10/2011 Vice dean of the department of Mechanical Engineering, RWTH Aachen University since 03/2012 Chairwoman VDI Aachen
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