cyber-physical systems: concepts, technologies and

Proceedings of TMCE 2012, May 7–11, 2012, Karlsruhe, Germany, Edited by I. Horváth, Z. Rusák, A. Albers and M. Behrendt

 Organizing Committee of TMCE 2012, ISBN 978-90-5155-082-5

CYBER-PHYSICAL SYSTEMS: CONCEPTS, TECHNOLOGIES AND IMPLEMENTATION PRINCIPLES Imre Horváth Faculty of Industrial Design Engineering Delft University of Technology the Netherlands [email protected]

Bart H. M. Gerritsen Emerald Eye Research & Consultancy the Netherlands [email protected]

ABSTRACT The practical implementations of the paradigm of cyber-physical systems appear in many different forms. However, they can be identified based on their distinctive characteristics such as distributed, multiscaled, dynamic, smart, cooperative, and adaptive. The objective of our explorative research was to cast light on the key notions, prevailing theoretical understanding, and engineering concepts, and to investigate the main principles and resources of implementation. First, a proposal is made to replace the standard architectural reasoning model with one which expresses the growing synergy between the enabling technologies. Opposite to the conventional view that differentiates the implementation technologies as cyber and physical technologies, this reasoning model introduces the class of synergic technologies. These technologies gradually dissolve the boundaries between cyber and physical as the difference between atoms and bits is disappearing due to current technological achievements, such as particle-based computing, molecular sensors and nano-actuators. Then, the paper gives a concise overview of the various physical, synergic and cyber technologies. Finally, the paper discusses the most important design and implementation principles. Its main conclusion is that though a huge number of publications are available concerning the paradigm, constituents, architectures and enabling technologies of cyber-physical systems, this domain of knowing and development is still in its infancy and many research questions should be addressed from multiple aspects.

KEYWORDS Cyber-physical systems, system paradigm, enabling

cyber technologies, enabling physical technologies, strategic application domains, future prospects

INTRODUCTION In the broadest sense, cyber-physical systems (CPSs) blend the knowledge and technologies of the third wave of information processing, communication and computing with the knowledge and technologies of physical artifacts and engineered systems [1]. There seems to be an agreement in the literature on the fact that the profession and knowledge of cyber-physical systems are not mono-disciplinary. However, it is still debated if this discipline is inter-disciplinary, multi-disciplinary, or trans-disciplinary in nature. Advocates of the inter-disciplinary view argue that the mission of CPSs science and technology is to create a bridge between the two constituent knowledge domains, namely the cyberspace and the physical space [2]. This argumentation seems to be correct since information and communication science and technologies, on the one side, and physical system science and technologies, on the other side, are epistemologically and methodologically different. The representatives of the multi-disciplinary stance claim that the science and technology of CPSs should synthesize the knowledge and methods of all intersecting foundational physical, biological, engineering and information sciences, and should develop a comprehensive science for CPSs. The supporters of the trans-disciplinary interpretation claim that once the science of CPSs provides comprehensive knowledge for implementation, the discipline should focus on providing application domain independent architectures and technologies for building practical cyber-physical artifacts and providing domain-orientated services [3]. In our

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view, achieving all of these objectives can be considered as the mission of the science of CPSs. The objectives of the discipline of CPSs are: (i) blending the knowledge of multiple domains into a consistent body of knowledge so as to underpin it by the basic principles of natural, formal, technical, social and human sciences, and (ii) developing a system-level understanding and conceptual frameworks of this family of systems. The main research themes are such as system structure identification, symbiosis of physical and cyber system parts, integration of enabling technologies, system behavior analysis, autonomous system operation, real-time system control and self-control, smart system behavior, non-deterministic scenarios and protocols, and principles of next-generation implementations. As a whole, the discipline seems to be rather immature and is still suffering from a somewhat unconsolidated, if not confusing, terminology. Experimental research in CPSs, as well as prototyping-based testing are facing empirical limitations because of the large scales, spatial distribution, inherent complexity, prevailing heterogeneity and embedded nature. The concept and the term ‘cyber-physical systems’ popped up some ten years ago in the USA [4]. In Europe the same kind and manifestations of systems are named either as ‘The Internet-of-Things’ [5], ‘Web of Things’, or as ‘cooperative adaptive systems’. The literature reflects a multitude of terms (such as, ‘smart ubiquitous systems’, ‘deeply embedded systems’, ‘software-intensive systems’, ‘hybrid automata’, sensor-actuator networks, M2M (OECD), etc.), which try to denote the same concept, putting emphasis on particular aspects (e.g. functionality, implementation, and applications) of complex systems that strongly integrate cyber and physical parts [6]. The use of different terms by various researchers raises the feeling that they are working on completely different field, but in fact they address the same or very similar issues and characteristics of CPSs. Theoretical research in this domain of interest is still very scattered and not streamlined. Actually, the literature shows a multitude of thinking models, not to mention the variety of reference systems. There are large differences in the approaches, research and development efforts, and funding programs in Europe, USA and Japan. The motivation for our background research came from two observations. Our wide-spread literature

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study explored that a large number of papers discusses and contributes to some specific aspects of functional frameworks, technologies, information flows, implementations and applications of CPSs. On the other hand, apart from some philosophical speculations and milestone reports, much less comprehensive surveys or comparative studies can be found [7]. The versatility of interpretations and conceptualizations, and the many different views applied in these technical studies clearly indicate the need for an extensive harmonization. This paper intends to provide an overview of the state of the knowledge and art in the territory of CPSs. It also intends to offer a comprehensive reasoning model. As implied by its title, it focuses on the theoretical concepts, the enabling technologies, and the design principles. Due to the extreme large number of concepts, technologies and applications, and the space limitations in the paper, a more detailed discussion was not possible. Section 1 discusses the paradigm and the distinguishing characteristics of CPSs. In addition, it proposes a novel reasoning model and gives an overview of the conceptual realization technologies. Sections 2 and 3 summarize the most important cyber and physical technologies, respectively, while Sections 4 discusses the synergic technologies. Section 5 gives an overview of the currently known design principles of CPSs. Section 6 offers some propositions as conclusions of the conducted study.

1. PARADIGM OF CYBER-PHYSICAL SYSTEMS The word ‘paradigm’ has at least two modern connotations. First, it is used to describe an emerging and consolidating worldview that (i) goes together with the evolution of knowledge, (ii) influences the objects and objectives of knowledge inquiry, and the way of doing inquiry, and (iii) creates new explanations and order. Second, it is also used as a comprehensive (constitutional) pattern or (mental) model that underpins all specific manifestations of certain things, e.g. artifacts and systems. Here, we use the term in its second meaning, though it is eventually not separable from the first meaning. The paradigm of CPSs can be made tangible through three foundational constituents of them, namely, (i) the generic system architecture, (ii) enabling knowledge and technology assets, and (iii) distinguishing system characteristics. The paradigm helps determine the essential constituents without Imre Horváth, Bart H. M. Gerritsen

consideringg all possiblle instances,, and overseee the complexityy and impose an order on o it. The ssystem features im mplied by thee paradigm helps h identifyy what belongs too and is covvered by th he paradigm m. The discipline oof CPSs hass already reaached that staage of developmeent, where thee distinguish hing characteeristics of CPSs haave been deefined. Thesee are discus sed in Sub-sectionn 1.2.

1.1. From the stan ndard model to a syn nergic mod del Formation and evolutioon of CPSs have h been foostered by the aboove-discussedd intense meerging of phhysical systems aand inform mation, com mmunicationn and computing technologiies (ICCT)). CPSs arre in intensive iinteraction with w the em mbedding teechnosocio-economic environnment, as weell as with huumans and commuunities [8]. Independent I of the objecctives, of all scales andd enablers, the basic architecture a implementeed CPSs reeflect a gen neric architeectural pattern. Thhe model off this standaard architectture is shown in F Figure 1. Evvery system m is regardedd as a structure of cyber componentts and phhysical componentts that need to t be functio onally integraated at all scales and levels. The cyberr component nts are discrete, loogical and switched, s an nd responsibble for computatioon, communication an nd control. The physical coomponents operate o in continuous tim me and are responnsible for chhanging material and eenergy flows, as reegulated by the t laws of nature. n The technoologies used in the realizzation of CPS Ss can also be loooked at as cyber c technologies or phhysical technologiees. The geneeral interpretation is thaat they are interrconnected, syneergetic rather than constituentts. This interrpretation haas its roots in the

Figure 1 The standardd architectural model of cybberphysical systtems CYBER-PH HYSICAL SY YSTEMS

n tecchnological situation of the recent past. It does not fav vor to the exposition off holism thatt is one of the t maajor intentional characcteristics off every CP PS. Ho owever, we can obbserve man ny theoretical ad dvancements (e.g. compllex system science, s theoory off self-X systems, eetc.), and technological miniaturizzation, deevelopments (e.g. new co omputational principles, nnovel forms of applicatioon, etcc.) currently y [9]. They are driving researchers to recconsider the standard m model, and to o develop moore forward-lookin ng conceptuaal models. y extrapolating from thhe trends of current data By prrocessing and d storage tecchnologies, we can expect thaat the distance d annd differen nce betweeen maanipulation of atoms and bits are graduaally dim minishing ov ver some tim me. Actually, the boundarries of beetween the cyber c and thhe physical sub-systems s CP PSs will bee blurred allready in th he near-futuure. Th herefore, wee propose too use a diffeerent reasoniing mo odel, shown n in Figure 22. This mod del reflects the t cu urrently on-g going mergee of physiccal and cybber tecchnologies in nto one new formation. It I considers the t neew category y of techno nologies as the coupliing beetween purrely cyber and purrely physical tecchnologies. We refer tto this new w category of tecchnologies as a synergic ttechnologiess. As it seem ms, sy ynergic techn nologies are ddeveloping faster f than, and a . It ev ven ahead of,, the physical al and cyber technologies t haas to be noteed here that w we use this new reasoniing mo odel to classsify and disscuss the im mplementatioonen nabling techn nologies of C CPSs in Sectiions 2-4.

1.2. Disting guishing ccharacteriistics Th he paradigm of cyber-phyysical system ms implies a set off distinguishiing characteeristics. As an a outcome of

Figure F 2 Pro oposed new arrchitectural mo odel of cyber-phy ysical systemss

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th he recent inteense researchh and system m developmeent, many m of theese have beeen identifieed. The m most im mportant oness are: C1 CPSs aree designed annd implemen nted to suppport human activities andd well-being by distribut uted cooperatiive problem solving, in harmony w with the technno-socio-econnomic enviro onment, 2 CPSs are functionally and stru ucturally oppen C2 systems, with blurred b ov verall systtem boundariees, C3 3 CPSs haave the caapability to change thheir boundariees and behaavior dynam mically, and to reorganizze and reeconfigure their intern rnal structure,, C4 4 CPSs coonsist of a digital cybeer-part and an analog pphysical-partt, which aree supposed to work toggether towarrds a high-leevel functionnal and structural synergyy, C5 5 CPSs aree articulated and heterogeeneous, and aare constructted of very diverse d sets of o componennts, which caan enter and leave the co ollective at aany time, andd may encoounter otherr systems w with similar orr conflicting objectives, C6 6 CPSs, as well as theiir componen nts, manifest on various extreme spatial scales s (froom intercontiinental to nano-scales) n and tempooral ranges (ffrom instanntaneous to quasi-infinitte), and beyoond, C7 7 componeents are hybrid structures, encapsulatiing various (spatial) compositions c of physiical (material) entities and emb bedded cybber (softwaree and knowlledge) entitiees that provvide real-time informationn processing capability, C8 8 componeents have either predefined or ad-hhoc functionaal connectioons, or botth, with othher componeents at multipple levels, C9 9 componeents may opeerate accordiing to differe rent problem solving sttrategies (p plans) towarrds achievingg the overall objective off the system, C10 componeents are knoowledge-inten nsive and ab able to handlee both built-in formal knowledge, k tthe knowledgge obtainedd by sensors, and tthe knowledgge generatedd by reasonin ng and learniing mechanissms, uated decisioons C11 componeents are able to make situ and strivve for autom mated probleem solving by gatheringg descriptivee information n and applyiing context-ddependent causal and proceduural reasoningg, C12 componeents are ablee to memorrize and leaarn from histtory and situuations in an n unsupervissed manner aand to speciialize themseelves based on

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Fig gure 3 Featu ure profiles off various CPS instances

C13

C14

C15

C16

smart s soft ftware ageents and emergent intelligence, i components c are able to reorganize themself in response r to an a unpredicttable (emerg gent) system state s or environmental ciircumstancess, as well as to t execute non-planned n functional interactions and a to act proactively, overall o decision-makingg is distribu uted over a large l numbeer of compoonents, and is i based on the t reflexive interactionss among the components c and a multi-criiteria analysiis (optimizattion), different d sop phisticated st strategies aree applied in order o to manage m ressources and d maintain security, s in ntegrity and nd reliabilitty of the components c and the CPSSs as a wholee, next-generat n ion (molecuular and bio--computingbased) b CPSs are supposeed to have so ome level of reproductive r e intelligencee.

It haas to be noted that the abovee-mentioned charaacteristics maay not be onn the same level in the practiical implementations (innstances) off CPSs. For instan nce, though it stands in general, nott every CPS needss to be open n and adaptiive. A profiile diagram, such as the one shown Figuure (3), can be used to indicaate the extents e of realization n of the charaacteristics C1 1-C16. By ddefining quaantitative or qualittative thresh hold values ffor each chaaracteristics, it caan be asseessed whetther a giv ven system impleementation complies, c orr not, the paradigm p of CPSss, and to what extent it dooes.

1.3. Conceptu ual realizaation technologies This family of technologiees occupies a special positiion among the technnologies en nabling the impleementation of CPSs. Conceptual realization Imre I Horváthh, Bart H. M. Gerritsen

technologiees facilitaate abstracct specificcation, conceptual modeling, and virtu ual developpment, implementaation and simulation. This famiily of technologiees includes a wide rangee of enablerss, such as (i) abstraactions and conceptual c models, m (ii) llogical frameworkks and archiitectures, (iiii) functionaal and control moodels, (iv) protocols p an nd languagees, (v) standards and regulaations, and (vi) meanns of prototypingg. The num mber and variety v of m models developed for a holistic concepttual modeliing of complex C CPSs is large, but just seccond to that of the specific asppect models. Formal mathematical and inform mation modeels of CPSs needd to be baseed on multi-level abstracctions. Typically, they are reepresented as a a timed hhybrid automaton.. The major challenge for f modelingg is to capture annd handle the overalll structurall and behavioral dynamism of CPSs, an nd the non--trivial interaction between physics and a computtation. Research inn interdiscipplinary matheematical mo deling of complexx systems tries t to reso olve the sem mantic dichotomy between thee continuouss operation of the physical paart and the discrete d operration of the cyber part by introducinng integratted specifiication frameworkks and combining discrete and contiinuous mathematiccs. Both loogical andd structurral frameeworks (architecturres) play an important rolee in conceptualiization of CP PSs [10]. A logical fram mework is a set of assumptionss, concepts and a inputs/ooutputs concerningg the elem ments, interrconnectionss and operations of the systems. Architectures A s are annotated structural representatio r ons that deescribe systems att a high leevel of absttraction, alllowing assignmentt of functionnality to eleements, evaluuating the compaatibility of the parts, and making trrade-offs beetween diffferent quality attrributes, suchh as perform mance, reliability and maintaiinability. Inttegral developmeent of thhe architecture, specificatioon of the functionality f , and simulation of the funcctions, operaations and behaviors of CPS Ss is an issu ue of current inteerest. The operaation of CPSs is integrated, coordinatedd, monitoredd and contrrolled by a com mputing andd communiccation core, whiich may neverthelesss be distributed and clustered. Early functional simulatioon CPSs of interconneccts modelinng and multim CYBER-PH HYSICAL SY YSTEMS

hysics simulaation of the tangible sysstem and tim med ph log gical simulation of information n flows and a traansformation ns with sim mulating thee control. The T traaditional clo ose-world prrocess contro ol and closeedloo op feedback models are complemented with w ap pproaches cap pable to sim mulate the beehavior of oppen an nd adaptive systems. Beelow we cllassify and discuss th he families of im mplementation enablingg technolog gies of CP PSs acccording to the new innterpretation introduced in Figure 2. The sorting of ttechnologiess into the thrree caategories (cyb ber, physicall and synerg gy) is shownn in Figure 4.

2. CYBER TECHNOL LOGIES 2.1. Progra amming a nd softwa are techno ologies Im mplementatio on of CPSs rrequires mu ultiple softwaare an nd control programmingg technologiees. In generral, theey can be standard or C CPS-specificc programmiing ressources. Prrogramming languages are usuaally diffferentiated whether w theyy are high leevel (or domaain orrientated) or low level (or domain n independent) meeans. The standard proggramming resources beloong to one of the following caategories: (i)) programmiing lan nguages and functional llibraries (AD DA95, JFC, JB, J Java3D, EJB, .NET, C++, VC++, MFC), (ii) geneeric op perating systems and proogramming platforms p (W W7, WMobile, W Maac OS, Linuxx, Unix, And droid, TinyO OS) an nd real-time operating ssystems (QN NX, VxWorrks, RT TLinux, Windows CE E), (iii) nettwork conteent deevelopment tools t (HTML L, XML, XS SL(T), ASP(X X), JS SP, DCOM, CORBA), (iiv) database definition and a daata managem ment (query) y) languagess (SQL, QB BE,

Figure 4 Interconnecttion of physicaal and cyber domains d

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QL, CQL, D, DMX, OCL), (v) information visualization kernels, languages and tools (OpenGL, DirectX, VRML, SVG, WML, Flex, WPF), and (vi) generic program implementation and testing standards (IEEE/ISO/IEC 24765, IEEE 1012, IEEE 12207:2008, ISO/IEC 20000-1:2011, UML). Very different tools have been proposed for structural and functional specification, programming and control of CPSs. Only some representative examples can be included here. For instance, the Architecture Analysis and Design Language (AADL), developed by the Society of Automotive Engineers (SAE), is now an industry standard language for textual and graphic modeling the architecture of embedded real time systems. It provides several abstractions specific to embedded systems for performance-critical characteristics [11]. However, in order to be able to model CPSs in AADL, the core language should be extended with CPSannex. Several macro-programming frameworks and runtime environments have been proposed, such as MacroLab, TinyDB, and Regiment, which aim at programming the components on system level, rather than on individual component level. Actually, the system-level global instructions are converted to component-specific local instructions. MacroLab is a general-purpose programming environment, which uses macro data vectors to describe the components. It makes possible for the developers to control an entire network of components by a single program using the deployment-specific code decomposition (DSCD) strategy [12]. TinyDB is a distributed query processor tool that allows collecting data from each of the nodes in a sensor network in the form of individual, aggregate, event-based and life-timebased queries. Yet another control tool, Etherware is a middleware between a complex application and the computing layer of sensor-based CPSs, with the objective of supporting evolvable, networked control application development. It is based on the concept of runtime microkernel used in many operating systems, which is extended with service components and application components [13]. One of the most important concepts and technologies of CPSs is the concept of agents which is realized in the practice through many different technologies. Agents are complex computing entities, which typically amalgamate physical and cyber parts, and are capable to act autonomously in dynamic environments in a context dependent manner [14].

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Towards this end, they reason smartly, collaborate, learn and adapt. The way how smart agents of a multi-agent system interact and cooperate with one another to achieve a common goal resembles the way how humans collaborate with each other to carry out mutual work. Distributed in the networked environment, agents can independently act on themselves, as well as on the environment. Through communication and cooperation with other agents, they can manipulate parts of the environment, and react to its changes. Typical fields of application of agent technologies are such as smart ubiquitous products, ambient smart environments, semantic web, pervasive services and grid computing. Agents have become one means of conceptualization and implementation of CPSs. They can contribute significantly to the development of computational systems that are able to manage themselves, as well as to evolve. They are believed to pave the way to so called self-star (self-*) systems that show various levels of self-awareness, self-configuration, selfmanagement, self-organization, self-diagnosis, selfcorrection and self-repair [15]. Agent technologies offer new opportunities for the implementation of the above self-characteristics and -functions, while self-* operation poses new challenges and task to smart agent research. This gradually leads to autonomic computing that lends itself to the manifestation of self-managed CPSs systems with a minimum of human interference. These complex and distributed computing systems simply grew organically, without any centralized human control, or complete structural, functional and behavioral understanding.

2.2. Transmission and communication technologies As used in the context of CPSs, the term ‘transmission technologies’ refers to a family of commercial and emerging technologies (and protocols) that support transmitting messages over different locations (between different devices), while the term ‘communication technologies’ is related to the activity and the way of conveying information. In other words, the primary role of the former technologies is physical enabling, and that of the latter technologies is content management [16]. Transmission and communication in CPSs can happen in peer-to-peer (1:1), peer-to-cluster (1:N), or cluster-to-cluster (N:M) topologies. The typical ranges of transmission are shown in Figure 5. Transmission can be characterized by: (i) the type of signal transmitted, (ii) the form of the carrier, (iii) the Imre Horváth, Bart H. M. Gerritsen

E 802.15.4 standard s is the t acccess (BWA)). The IEEE baasis of Intern net protocolls for Wirelless Embeddded Internet (WEI)) and 6LoWPPAN [17].

2.3. Conne ectivity an nd network king techno ologies

Figure 5 Typical rangees of transmission in multi--scale CPSs

content off transmissiion, and (iv) the modde of transmissioon. In these aspects, anaalogue and ddigital, wired and w wireless, siggnal and dataa transmissio n, and simplex, seemi-duplex and a full-dup plex modes aare all widely usedd in CPSs. Analog siggnal transmiission is still used to trransfer informationn about conttinuous physical processees and characteristtics. Howevver, in mo ost cases, aanalog signals aree converted into i digital representatio r ons by transductioon. The raw transducer t signals are typpically amplified bby powered transmitter. t Transmission T n may happen via heterogeeneous med dia, includiing a compositioon of wireed and wiireless form ms of transmissioon. The signal and datta carrier c an be cable, fibeer optics andd/or radio waves, w and ccan be serial, paraallel or mixxed. Integriity of the ddigital signals traansmitted inn geograph hically distrributed CPSs is endangered by noise, attenuationn etc., therefore vvarious technnologies, such as repeaterrs, are used to secure traansmissions. Communiication between nnodes is formalized by y the ISO Open Systems Interconnecction (OSII) specificcation. Encoding, packaging, interfacing, multiplexingg, etc. of communnication conttents are sup pported by sseveral protocols aand standardss. Wireless ttransmissionn media arre such as high frequency radio wavves, radio microwavess and infrared liight-waves. While coaaxial cable--based (10Base5 tthick-wire Ethernet) E tran nsmission reeaches 10 Mbps aand a fiber-ooptic-based token t ring (F FDDI) can reach 100 Mbps data transffer rate, inffra-red communicaation channnels achieve 4 Mbpss and microwavee radio channnels can achieve a 45 Mbps transfer raates. IEEE standards s su upporting wiireless communicaation incluude Wi-Fi (IEEE 8002.11), WiMAX5 (IEEE 802.16), ZigBee (IEEE 802..15.4), and Blueetooth (IEE EE 802.15.1). The IEEE 802.16(d/e)) standard supports brroadband wiireless

CYBER-PH HYSICAL SY YSTEMS

An n intrinsic ch haracteristic of the majority of CPSss is co onnectivity, that t is, the abbility to com mmunicate with w oth her componeents of the syystem. Digitaal networks are a typ pically charracterized bby their (i)) geographical co overage (PA AN, LAN, MAN and d WAN), (ii) ( top pologies (b bus, tree, star, ring g and meesh co onnectivity), processing architecturee (centralizeed, peeer-to-peer, client/serverr computing units), and a (data) connectivity technollogies (token n ring, TCP//IP, UD DP, etc.). For F power line commu unications and a wiired connecttivity networrking, IEEE developed the t staandard 802.3 3, which is thhe basis of Ethernet. E It has h vaarious version ns, such as ccommon, fast, gigabit, haalfdu uplex, full-du uplex and sw witched Etherrnet. Neetworks caan cover different ranges and a functionalities. Restrictedd in range, personal arrea neetworks (PAN Ns) are typiically wired with compuuter bu uses (USB, FireWire), while wirelless PANs are a baased on teechnologies such as RFID, IrD DA, Blluetooth, UW WB, ZigBeee, WAP and d Z-Wave. The T prrevalent LA AN technoloogy of tod day is wirred Etthernet, whicch is speciffied by the IEEE’s 8022.x fam mily of stan ndards. This family inclu udes the 8022.1 (high level in nterface), 8002.2 (logical link controol), 80 02.4 (token n bus) annd 802.5 (token rinng) sp pecifications. At the deveelopment of smart ambieent en nvironments, also conside dered are in-b building pow wer lin ne commun nications. T The two most m comm mon tecchnologies are a Home Pluug and X10.. Home Plugg is a broadband--over-power--line (BPL)) system thhat prrovides a bit rate of approoximately 14 4 Mbps. X100 is a de facto staandard and a widely-ussed power-liine caarrier system m for home automation. Security and a prrivacy of infformation haave come to o the center of technoloogy co ommunication researrch and deevelopment in n the last deccade. Neetworking offfers both loggical topolog gy and physical arcchitecture fo or interconnnection of communicati c ion su ub-systems in CPSs [18]. Netw works can be deescribed as arrrangements of connecto or elements and a no ode elementss. Main nodee elements arre (i) hubs, (ii) ( brridges, (iii) ro outers, (iv) ggateways, an nd (v) switchhes. Ass complemen nts, rather thhan as substitutes of wirred neetworks, also o wireless networks are a extensiveely ussed in CPSs. Wireless nettworks, for example e senssor

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networks, can be based on fixed or ad-hoc connectivity. Wireless networks have several advantages over wired networks: for instance, (i) installation and maintenance costs tend to be lower, (ii) replacement and upgrading is easier for wireless networks, (iii) the flexibility of wireless systems is higher, and (iv) the recently developed wireless networks have the capability to organize and configure themselves into effective communication networks. Ad hoc networking establishes a self-configuring network of volatile routers through wireless links of an arbitrary topology. Each node may serve as a host and router to assist traffic from other nodes (multi-hop wireless networks). Significant progress has been achieved in the domain of mobile ad-hoc networks (MANET).

3. PHYSICAL TECHNOLOGIES 3.1. Advanced materials Implementation of the physical components of CPSs, for instance, the sensors, actuators, transducers and transponders, needs advanced materials. While functionally supplemented materials and multifunctional materials represented the real technological novelties a decade ago, nowadays carbon nano-tubes, quantum dots, molecular switches, molecular motors, etc. are in the focus of research. The reason is the revolution that is happening in science and technology, where efforts are made to get insights in the fundamental properties of material structures of molecular scales, and to develop nano-sized structures that are able to produce new behavior [19]. There are even new research domains in formation, such as materiomics that concentrates on merging biology and engineering in sustainable and robust materials, and in multi-scale molecular structures. Not only strong and tough carbon-based materials, but also nextgeneration self-learning material systems, whose properties can be tailored by changing their structures, are studied. Many kinds of advanced materials are already available for use in CPSs. These are peptide-based bio-materials, polymer-based bio-materials, ceramicbased bio-materials, piezoelectric materials, shape memory alloys, electro-active conductive polymers, electro-rheological fluids and smart gels, polyethylene glycols, electro- and magneto-strictive materials, self-healing materials, electro-chromic materials, etc. Micro-materials with multilevel

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interior structures have been developed as a result of bio-mimetism and bio-analogies inspired research. Examples of these are scaffolds and artificial skins. Application of nano-technologies allows material scientist to define material properties on atomic and molecular level by creating nano-structures [20]. One of them is carbon nanotube (CNT), which is actually a one-atom-thick carbon lattice (graphene) sheet, which is rolled up to form a tube of one nanometer in diameter. CNT provides a high effective material stiffness and deformability, which surpasses that of highly alloyed steels. Using these materials needs new design principles and mass manufacturing technologies, which are still lagging behind.

3.2. Advanced energy technologies Advanced electric energy technologies refer to currently existing or emerging energy (i) generation, (ii) transfer, and (iii) control solutions that can be used for powering complex, distributed and multiscale CPSs [21]. Efficiency of energy usage is of paramount importance for extreme large number of elements are typically operating simultaneously, or are in stand-by, latent, proactive or hang-on position due to its role in a the system. The current gradual shift from centralized macro-power generators to decentralized micro-power generators harmonizes with the paradigm of distributed CPSs. The used electric energy generation (EEG) technologies can be classified based on the source and level of power they provide. The electric energy transmission (EET) technologies can be clustered based on the spatial range in which they are effective and the manner of transferring the power. In the EEG technology cluster, novel conversion principles are used to avail the amount of energy requested for the operation of the components and the network of CPSs. Solar energy harvesting has been dominant and is increasing due to (i) the efficiency intensification of solar cells, (ii) the improvements in manufacturing technologies, and (iii) the economies of scale. The advantage of photovoltaic cells is that they exploit renewable sources of energy, can be geographically distributed, can be attached to mobile devices, and can be used as individual power sources or nodes of a power grid. The future is seen as a decentralized electric power system in which electricity is produced by a large number of dispersed, small-scale power generators [22]. The produced direct current generated needs an energy-storage system and distribution system. Photovoltaic technology is covered by standards such Imre Horváth, Bart H. M. Gerritsen

as EN 61000-3-2, IEEE 1547, NEC 690 and IEC 61727. Electric energy storage involves both active control (shifting the energy usage peaks to low-use times), and passive storage by physical storage devices. Grid storage seems to be functionally and financially advantageous solution, but has limitations from accessibility and mobility point of view.

(reliability) through the use of ubiquitously gathered data and knowledge. They use advanced digital control technologies to save energy in transfer and applications.

In the EET cluster, we differentiate short-range (0.01 - 1 m), medium-range (1 - 50 m), and longrange (> 500 m) energy transmission systems. Typical short-range electric power transmission technologies are inductive transmitters. Optimized inductive links have been reported to transfer energy over very short distances (less than 30 mm) with an efficiency of up to 90%. Electric powering solutions for CPSs have also been addressed by merging data and power transmission technologies. Current solutions are applicable to small energy consumption components, such as wireless sensors. One example, Power over Ethernet (PoE), is the technology that transfers electrical power along with data to remote devices over standard cable in an Ethernet network. This liberates from the conventional A/C outlets. IEEE specified this technology in the 802.3af standard. A newer version, PoE Plus (802.3at), provides more available power, as well as lower costs, more flexibility and higher security, but still suffers from limitation in maximum power and distance. The IEEE P802.3az initiative tries to specify an energy efficient mechanism for Ethernet to reduce power consumption during periods of low link utilization.

The paradigm of advanced macro-robotics has been rapidly developing in the last 40 years. Traditionally, macro-robotics uses principles and tools from solid mechanics, information technology and digital control [23]. The first generation robots of 1970s were immobile electromechanical devices with preprogrammed control. The second generation of robots of the mid-1980s featured built in sensors and articulated actuators. The third generation of robots of the end-1990s benefited from the sophisticated computing and controlling software, which enabled smart reasoning, adaptiveness and contextsensitivity. The currently studied and prototyped fourth generation robots will have capabilities such as distributed architectures, situated communication, collaborative problem solving and autonomous decision making. The fourth generation clearly shows a demarcation of the macro- and meso-scale robotics and the micro/nano size robotics. This fact is reflected by our consideration of the branches of general robotic systems in our research and in this paper.

Opposite of the above direct energy transfer technologies, indirect transfer opportunities are also soaked for. One promising approach is wireless energy transfer using resonant magnetic coupling. If the surrounding is composed of magnetically neutral materials, it is not interacting with the coupled magnetic field. Other important advantage is that the magnetic fields are of less health concern compared to radiated energy transfer and electric fields. However, the efficiency of the inductive resonance links drops significantly for longer ranges. Nevertheless, middle-range indirect energy transfer creates opportunities for higher mobility for the components of CPSs. Typical representative of the long-range electric transmission technologies are smart power grids. These are networks that connect sources and consumers through a reconfigurable network, with the aim to optimize efficiency, security and stability CYBER-PHYSICAL SYSTEMS

3.3. Advanced macro-robotics technologies

The paradigm of robots covers a wide range of device functionalities, implementations and applications. Meso-scale advanced robots are not only equipped with dexterous actuators, networked sensors, adaptive reasoning and learning, but they are also intended to behave similar to human beings. The current implementations range from industrial robots and automated guided vehicles (AGV), thorough mobile domestic and service robots, to smart humanoid and anthropomorphic (walking) robots [24]. In the context of CPSs, advanced robotics gives raise to new issues, such as (i) tele-operation and tele-robotics, (ii) increasing the functional intelligence of control systems, (iii) neuro-control of robotic devices, and (iv) un-supervised reasoning and learning of robots. Research tries to enable reverse engineering of the human brain in robots, which places human-robot interaction to a completely different perspective [25].

4. SYNERGIC TECHNOLOGIES

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4.1. Digital microchip technologies The conventional desk-top and portable hardware designs, as well as the current computing programs, are built on the premise that the principal task of a computer is sequential data transformation. However, there is an intrinsic demand for a truly real-time information processing and a large concurrency in CPSs [26]. It means that the principles of conventional computing have to be reexamined in the context of real-time collaborative CPSs, where the allowed time differences should be below the fragments of nanoseconds due to the operational characteristics (e.g. motion, contact, etc.) of the physical components. The computing times should be reduced below the duration of physical and cyber events. This raises the need and fuels the efforts towards even faster computing solutions. These are seen in various forms of particle-based computing that are in a booting up stage in these days. As for now, particle-based computing research discovered many more knowledge gaps and technological limitations than that it could solve so far. It is also conceived that, as the configuration of the computing platform of CPSs changes due to up-grading, or due to internal restructuring, the software should migrate to the incoming or changed devices, and adapt itself to the new objectives and functions. Due to the miniaturization of the construction space of digital microchips (processors, memories, switches, etc.) and the simultaneous increase in the computational effectiveness, the circuits on a microprocessor can be measured only on atomic scale, probably within the next twenty years [27]. On the other hand, we can also see that the digital computing technologies are rapidly approaching their physical limits. Some new paradigms such as biocomputing or quantum computing may offer novel practical solutions. The objective is to process digital data and information more efficiently by new controlled physical, chemical and/or organic processes, and, ultimately, to replace current integrated-circuit technology-based digital data and information processing. Research in natural sub-atomic particles-based computing is proliferating, and creating quantum computers that harness the power of atoms and molecules in performing processing and memory tasks seems to be a logical next step. Quantum computers have the potential to perform certain calculations significantly faster than any siliconbased computer. They will also dissolve the

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boundaries between cyber and physical domains. Thanks to these and the other current technological achievements, such as molecular sensors and nanoactuators, the difference between atoms and bits is disappearing. Similar trends are observable in the field of sensor technologies. Conventional instrument-type sensors are giving their place to more versatile and effective sensor arrangements, substances and fields. Chemical, biological and nanotechnological research is producing new sensor solutions that require much less energy for their operation. The micro- and nano-technology research of the near-future will also go beyond the current embodiment and control principles of multi-scale mechatronics-based systems, and will change the world of micro-machines and actuator technologies. No matter how promising they are, quantum, chemical and/or organic processors and memories are potential solutions only for the future, rather than for our present time [28]. Nowadays, because of the current knowledge and technological limitations, nano-design and nano-engineering can take place only on computers, in the form of computational nano-engineering. More useful are those concepts and pragmatic implementations that try to exploit the collaborative capabilities of current computing facilities. These include efforts such as utility computing, cluster computing, cloud computing and grid computing. Utility computing means the packaging of computing and storage resources as a metered service similar to a traditional public utility. A cluster computer is a group of linked processors and memories, working together closely so that they form a single computer in many respects [29]. Grid computing is the application of several computers to support the operation of CPSs, which require a great number of computer processing cycles and/or access to large amounts of data at the same time [30]. Cloud computing is a computational strategy, which provides dynamically scalable and largely virtualized resources as services. Although grid and cloud computing share many commonalities in the objectives, architectures and technologies, they are different with respect to their programming models, computing strategies, business models and applications. For instance, clouds rely on a much more extensive resource virtualization as computing grids.

4.2. Sensor and sensor network technologies Imre Horváth, Bart H. M. Gerritsen

One of the major functions of centralized or distributed CPSs is information distillation and control based on the proactive operation of embedded sensor devices [31]. Sensors are hardly used alone, but in clusters or networks. In these cases the function of sensing is extended with signal conversion, transmission and communication. For the control of these, sensor manager agents are used that maintain a list of available sensors, processors, memories and switches, and provide access to them. Wireless sensor networks (WSN) are typical manifestations of combined information eliciting and transmission technologies [32]. In fact, WSN are also typical from the perspective of issues related to the operation of transmitters on network level. Common topologies are star and tree networks, but more flexible topologies are also possible at the cost of extra overhead and negotiation time. Depending on the tasks, the topology of the WSN and its synchronization can be adaptive. Data transmitted by the sensor nodes are collected and aggregated in memories, and processed further in a master node, or by dedicated host microprocessors. Signal transfer by transceivers may happen through wire-like transfer media or by wireless communication. In this respect, WSNs can be subdivided into pre-configured connection networks and broadcast-based networks. In a heterogeneous sensor networks, signals are typically detected by various sensors at distributed locations, and the sensory data can be availed by the sensors in multiple different modalities [33]. Therefore, they need dedicated control. In the late 1990s, efforts were made to develop tiny operating systems and microkernels for local control for WSN motes. For instance, in collaboration with Intel and Crossbow, a working group at Berkeley developed TinyOS, which is in widespread use since 2002. Other important research topics are such as distributed data aggregation, network query processing, multi-object tracking and localization, and multi-hop wireless communication [34]. In the context of CPSs, sensors can also be seen as a branch of interfaces between the physical world and the electronic signal processors. Modern sensors are already hardware and software combinations that can detect external events or environmental conditions based on various principles. The kind of hardware sensors ranges from (micro)mechanical and electronic sensors, through biosensors and chemical sensors, to molecular and atomic sensors. They provide information on (i) situations (location, CYBER-PHYSICAL SYSTEMS

orientation, motion, image, lighting, etc.), (ii) physical attributes (e.g. humidity, temperature, pressure, force, light, etc.), and (iii) behavioral changes (e.g. mood, stress, haste, presence, proximity, gesture, etc.). Logical sensors are also used to provide data without using a hardware device. In the case of many CPSs, an important issue is sensing crowd and collective human behavior, collaborative system operation, complex social structures, or community-level phenomena and context. This makes the ability of collecting, comparing, filtering and combining data from many users indispensable. The implementation of these functions however requires not only instrumentation, but also efficient strategies for context dependent interpretation, aggregation and operationalization of information.

4.3. Sub-micro scale electromechanical technologies As manifestations of CPSs, micro-systems are smallsize, multi-functional, stand-alone, but integrated appliances. In general, they are built up from multiple micro-fabricated units and components, which enable their smart and integral behavior (e.g. sensing, communication, reasoning and acting). There are three representative categories of microand sub-micro scale systems and technologies, namely (i) general micro-electromechanical system (MEMS) technologies, (ii) micro-robotics technologies, and (iii) nano-technologies. Originally produced on silicon wafers, MEMS have their roots in integrated circuits and semiconductors [35]. Current MEMS incorporate a broad set of function-enabler technologies and integrate mechanical elements, sensors, actuators and electronics on a common substrate through microfabrication technologies. The fabrication technologies used to create MEMS devices are usually categorized as (i) surface micro machining, (ii) bulk micro machining, and (iii) LIGA (lithography, electroplating and molding) technologies. Micro-gears, micro-pumps, micropipes, micro-relays, micro-mirror, etc. are the typical devices produced by these technologies. Most MEMS devices have at least one transducer components that enable them to sense, amplify and actuate. This points at the fact that MEMS communicate with their environment not only through a digital channel, but also through mass-

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streams, fluid and gas flows, and light beams. MEMS technologies are progressing from the discrete devices stage, through basic, multipart and selection integration, to a fully-fledged integration. Producing chemical and organic system components have also become possible, as a result of merging molecular chemistry, micro-biology and micro-systems technologies. Micro-robotics contributes with new capabilities to manipulating objects (masses) in the micro-scale, and to developing miniaturized intelligent machines [36]. Major accomplishments in the field of micro-robots are functional components such as flexure joints, micro-grippers, self-correcting actuators, visual servoing, bending cantilevers, adhesive grippers. Contemporary micro-robot research extends to the territory of biology in two ways. First, it tries to explore analogies in the motor (robotic) characteristics of living organs (bugs, mammals, etc.). Second, it considers the agent-type and collective behaviors of populations/communities of living organs (colonies, swarms, flocks, crowds, etc.) and uses the revealed behavioral principles in action planning and problem solving with crowds of microrobots. The results are bio-mimetic robots, bionic components, micro-robotic swarms and evolving micro-robotic arrangements. Swarm intelligence supplies new algorithms for solving complex tasks by coordinated sets of simple robotic agents. Part of micro-robotics is moving towards nanorobotics, where the focus is on programmable assembling of nano-scale components either by manipulation with larger devices, or by directed selfassembly [37]. In the field of nano-robotics, nanoparticle manipulators, molecular motors, multi-tip silicon photomultiplier arrays, nanotube structures and nanowire sensors represent the latest results. Nano-robotics creates machines of microscopic scale. Nano-robots (also called nanobots) are combined of molecular components and are able to change their shape, structure and properties in relationship with accomplishing tasks. Nanoids are another family of nanoscopic robots, which are able to imitate behavior and characteristics of biological organs. Nanotechnology is an anticipated manufacturing technology to provide inexpensive control over the structure of matter. Researchers hope to design and program nano-machines that are capable to build large-scale objects atom by atom. With such selfreplicating assemblers, objects of any size and quantity could be manufactured using common materials like dirt, sand and water.

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5. IMPLEMENTATION PRINCIPLES As exposed above, the operating principles of CPSs fundamentally differ from those of conventional engineering systems, and even go beyond the informing, control and optimization theories of the current ICT systems. In addition to this, the paradigm of CPSs also implies different implementation principles. In the mirror of the literature, a relatively large set of specific implementation principles should be operationalized at the development of CPSs. These most important ones are as follows: Principle of systemic (holistic) interconnections Though the two principal constituents of CPSs are extremely different in nature, operation and realization, synergetic relationship should be implemented between them. This can be achieved only through the implementation of systemic interconnections among the physical and cyber components. Traditional interfacing of the two kinds of components is based on adding interface elements that bridge the gap between computational and physical functionality. Usually, physical-to-cyber (P2C) and cyber-to-physical (C2P) connectors are used as directed connector elements. Examples of P2C connectors are physical sensors that detect multiple physical quantities, measure physical properties, extract information and convert analogue signals into electric and digital signals. Examples of C2P connectors are actuators that are able to convert electrical signal into a physical phenomenon. These typically involve auxiliary physical phenomena to convert digital signals to physical effects. The current trend of integrating bits and atoms creates new opportunities for more synergetic interfaces between cyber and physical entities of CPSs. In the near future, P2C/C2P transponder and transducer elements will be used, which will be able to do more than just a simple translation between the cyber and the physical domains (Figure 1). On one side, they will have ports to cyber elements and, on the other side, ports to physical elements. In the further future, as hardware miniaturization goes on and the boundary between mechanics and electronics becomes dissolved, even more sophisticated interfacing will be possible. It is seen however that interfacing of components of large multi-scale CPSs remains a hot issue, as well as the issue of interface components shared by multiple different systems. Principle of model-driven specification Imre Horváth, Bart H. M. Gerritsen

Multi-scalee CPSs are subject to a wide rannge of physical reequirements, such as phy ysical size, ppower consumptioon, latency and a dynamiccs, and to syystemlevel requirrements, succh as safety, security, andd fault tolerance. T Their compllexity of usu ually exponenntially grows withh the functionnality, properrties, structuure and heterogeneeity of their componentss. Handling of the inherent syystem compleexities canno ot be based oon the classical divide-and-cconquer prrinciple. Innstead, various fforms of structural and behaavioral abstractionns are needeed that redu uces the struuctural and operaational com mplexities, but b maintainn the internal syynergy on both b system and compoonents levels. Thiis explains why w the issu ues of multii-level abstractionns, abstraction-based mod deling and m modelbased design of CPSss have been receiving ssuch a strong attenntion in reseaarch and dev velopment [3 8]. Literature informs thaat four kind ds of modeels are typically generated: (i) abstracttion modelss, (ii) formal moodels, (iii) executable models, andd (iv) performancce models. Abstraction--based multii-scale models cann be used in i conceptuaalization of CPSs, and to suupport both a holistic (top-down)) and incrementaal (bottom-uup) system integration, while preserving predictabiility and testability. The mentioned other models m sup pport behaavioral simulation of the desiggned systemss, and can bee used for comparrative benchhmarking off the implem mented systems. Model-basedd design generates many different m models of varrious fidelity levels and tr tries to predict thee behavior of o a CPS, wh hile it is virrtually running. T This approach is an alternative a oof the traditional design meethods and practices, which usually m manage multti-objective design prooblems based on the separattion of concerns (or, ddesign views). The latter workks well when n the design views are orthogoonal, i.e., design decision ns in one vi ew do not influennce decisionss in other vieews. In the ccase of CPSs, com mplex interacctions among g the compoonents cannot bee modeled exhaustively y, especiallyy not across diffferent designn views. An nother recoggnized issue is relaated to the non-linear n an nd non-increm mental nature of m multi-scale CPSs. C It mean ns that addinng new componentts either lead to unpredicted and undesirablee system beehavior, or that the ddesired overall sysstem properrties or the required sppecific properties of other syystem components cannnot be maintainedd.

sy ystem operatiion together.. The traditio onal separatiion off computattion (softw ware) from m physicallity (hardware) does not workk for CPSs. Instead I a moore ho olistic approach that integrates all essenttial co onstituents iss needed [399]. This can be achievedd if thee constituen nts form an integrated d compositiion plaatform (CP). An integgrated CP includes suubplaatforms th hat complem ement each h other and a intteroperate. The develoopment of Autosar, an ex xtended worrldwide plattform in th he automotiive ind dustry for digital coomponents and system ms inttegration, ex xhibits this ccharacteristiccs. There haave beeen hardwaare descripption langu uages (HDL Ls) prroposed for the t programm ming of adap ptive hardwaare (e.g. FPGAs and a CPLDs). As shown in Figure 4, the t ph hysical operaation enabliing sub-platfform (P) off a geeneric CPS is complem mented by five f computiing su ub-platforms, namely: (i) netware (N)), (ii) hardwaare (H H), (iii) softw ware (S), (iiv) firmwaree (C), and (v) kn nowledgewarre (K) sub-pllatforms. Eaach sub-plattform incluudes a set of alternatiive ressources (too ols, technoloogies and other o enableers) thaat can be ussed in system m developm ment dependiing on n the objecctives, requiirements, co onstraints and a co ontext. The composition c platform sy ynchronizes the t vaarious sub-p platforms annd facilitatees an integgral co onsideration of the conceerns, aspectss and demannds rellated to them m. The conceept of compo osition platform is in line with h the currentt convergencce of networrks, eq quipment, com mputations, controls, and d knowledgee at naano-, micro o-, macro-, and meg ga-scales. The T co omposition platform-driv p ven design thinking t favoors to a holistic conceptualizzation of CP PSs throughh a co oncurrent co onsideration of the matching m a and intteroperability y of the elem ments of thee P-N-H-S-F F-K su ub-platforms. Prrinciple of component-bbased imple ementation

Principle o of platform-b based deve elopment The impleementation of CPSs requires vvarious resources w which are supposed s to enable a hholistic CYBER-PH HYSICAL SY YSTEMS

Figuree 4 Compositition platform of o CPSs

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The principle of component-based implementation has proliferated not only in software development, but also in the development and implementation of CPSs [40]. This system implementation methodology has been underpinned by the generic theory of composition, which is a technical foundation of all engineering disciplines. It explains that the applicability of components-based design depends on two key conditions: compositionality and composability. Compositionality is a measure of how much system-level properties can be realized by local properties of the system components. Composability is the measure of how much the properties of the individual components are changing due to the interactions with other components of the system. The lack of compositionality results in brittle systems, which do not perform well outside of their small operational envelope, and which are hard to maintain. High composability implies that the operation and behavior of the components are not strongly influenced by other system components, or by the system itself. Thus, compositionality formulates a top-down aspect, while composability formulates a bottom-up aspect of CPSs design. In computer science, the method of separation of concerns (SoC) was proposed to facilitate component-based implementation. It is advised in the literature that components needs to be designed to encapsulate discrete functionality. Methodologically, it requires disintegration of the system into distinct concerns that overlap in functionality as little as possible. Though a perfect separation of the concerns is not possible, it is still the only way of coping with complexity and achieving sufficient holism of CPSs. Components are seen as stand-alone entities and resources for service-orientation. They have to be systematically chosen and combined to form complete solutions. In this regards, combination of this design principle with the principle of platformbased development seems to be necessary. Principle of real time computation No matter how complex, distributed and functionally articulated a cyber-physical system is, it should be capable to obtain, process, and manage data and information in real time. Need of real time handling also stands for both inter-component and withincomponent communications. The previously discussed two advancements, namely: (i) the enormous increase of the computing capacity of selfstanding and embedded processors, and (ii) the emergence of the concept of cooperative utilization

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of digital computing and data storage facilities are largely favoring to real time computation [41]. An active cloud or a grid of processors and memories is supposed to provide sufficient computer power even for mega-scale CPSs. It is capable to put resources into use automatically according to the actual computing requirements, and deactivate them to save energy. In addition, it can ensure the reliability of execution according to pre-defined operational quality parameters. However, whatever computing power is provided it is not able to overcome the constraints originating in the delays caused by the operation and the functional couplings between the physical part and components. For this reason it is better to design the operation of CPSs by assuming quasi-real-time-computing, and to optimize it by means other than computational. Principle of event-orientated control CPSs are characterized by interacting time-triggered and event-triggered dynamics. There is no centralized, scenario-based control in CPSs. The global behavior of the system emerges from the interaction of the behaviors of local components and agents. In the physical world, processes happen in (continuously measurable) time, which is inexorable of their happening, but independent from the logical sequence of events. Unfortunately, current computing and networking technologies cannot replicate these well. In current system design, continuous physical processes are converted into a queue of discrete events with attention to the start time and the duration of the events, or into a pattern of discrete events in case of parallel processes. With this, the overall operation of CPSs can be modeled as a discrete-event system, which consists of an event record with the associated time-stamp. However, CPSs need multiple time scales of control. In addition, there are many other factors that influence the control of components and the CPS as a whole. One is vagueness due to the lack of predefined system boundaries, actors and connections. This raises the need for adaptive, situated and context-sensitive control mechanisms. The second is variability that is a result of the tight process-level integration of information/computation processes and physical processes, and that prevents the identification of whether the change of behavioral attributes are the result of computations, physical laws, or both together. Furthermore, it is entirely possible that some components of the CPS could adapt themselves automatically to the other Imre Horváth, Bart H. M. Gerritsen

components in their assembly. Together with the possible changes and the assigned priorities, his fact should also be taken into consideration in planning the control of CPSs. Event-based control is based on the assumption that control can be extended to emergent events and unscheduled event interactions in the operation flow of the system. Therefore, usually a two-phase eventoriented control is implemented, in which the first phase focuses on event detection and the second phase deals with event handling. Event-based architectures have been introduced in hardware design in the 1950/60s, and in graphical user interface design in the 1980s. An event-driven system architecture typically consists of event emitters (or agents) and event consumers (or sinks). Sinks have the responsibility of applying a reaction as soon as an event is presented. Event-driven CPS control is experienced to be more normalized to unpredictable and asynchronous environments. Event-driven architecture can complement serviceoriented architecture (SOA) because services can be activated by triggers fired on incoming events. Principle of service-oriented functionality In a service-oriented architecture (SOA), services, rather than components receive attention, since they can be generated by alternative systems [42]. When SOA is applied as a principle of designing, the functionality of a cyber-physical system is packaged as a suite of interoperable services. Services are much larger units of functionality than traditional functions, classes, or objects. These services can be produced by different parts of the system. SOA defines the interface in terms of protocols and functionality. Current SOA implementations aim at web services, which can be published and found through UDDI online catalogs, and accessed through XML interfaces by clients. Developers make the service accessible for users over a network, and they can combine and reuse them in various applications. Likewise objects in object-oriented programming, services are independent units in a service-oriented architecture. Their functionality is independent, but they pass over data and messages as it is regulated by protocols and metadata. In the context of WWW, the services themselves are typically described by the Web Services Description Language (WSDL), while the communications protocols are specified by the SOAP protocol.

CYBER-PHYSICAL SYSTEMS

Principle of minimal intrusiveness CPSs encompass not only the cyber world and the physical world, but they also interconnect and strongly interact with the human domain and the embedding environment, even if it not always happens in an explicit form. Therefore, they should be seen as complex socio-technical systems, in which human and technical aspects are massively intertwined [43]. CPSs are supposed to adapt to changing communities of users with changing requirements. However, no matter how good the original specification is, ICT systems become less well adapted to users and user needs over time. Therefore, CPSs should also adapt to changing human environments, no matter if it becomes a narrower or a broader environment. Current technological limitations make CPSs intrusive. They are more syntactic, than semantic - therefore create a mismatch with respect to the human way of thinking and doing. Human cognitive processes should be studied from the perspective of living with and cooperating with CPSs. For instance, recognizing patterns by humans and generalizing them into models are not well understood and not implemented in computers. This also recalls the interface development issues, as well as the motor, perceptive, cognitive and affective cooperation of humans with these systems. Typical form of interaction with a branch of CPSs is teleoperation that manifests in a remote and distributed communication and manipulation. This is applied, for instance, in the case of networks of robots and sensors that work in a cyber-physical space with a remote human in the loop to accomplish dangerous, unpleasant, or super-human activities. Minimal intrusion to human and environment can be facilitated by enrichment of system operation by agent-based smartness. There are huge knowledge gaps in these context, as well as challenges such as overseeing complexity, real time information provisioning, etc.

6. CONCLUSIONS The main findings of our survey research can be highlighted as follows:  The paradigm of cyber-physical systems has been nurtured by the aggregated theoretical and methodological knowledge, the affordances of the recently developed physical and cyber technologies, and the progress in engineering of complex, multi-scale, distributed and adaptive

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systems. The progress has also been stimulated by the growing need for CPSs in order to be able to address several societally-based issues and to effectively solve problems that cannot be handled otherwise. Though the paradigm of cyber-physical systems has become consolidated and widely accepted, there are still vaguely defined or investigated conceptual elements. Practical implementation of CPSs needs a massive synthesis of multidisciplinary knowledge, tight integration of the enabling technologies, and careful consideration of the human and environment aspects. In addition, there is a strong need for unifying terminologies, comprehensive concepts, abstract models, system frameworks, and interoperable standards. Conceptualization, development, implementation and testing of CPSs need both a robust theory and methodology of abstracting, as well as conceptual and computational abstractions that are meaningful for both human and computational agents. Abstractions are to be incorporated in formal models that facilitate specification, composition, and interoperation of CPSs. Continuous control models of physical systems, which are based on differential and integral calculus, and the discrete digital models that are based on mathematical logics and numerical computation need a higher level of integration. Many theoretical and methodological concepts such as synergy, compositionality, time-correct control and operation, goal-oriented learning, etc. should be revisited in research. Due to the on-going intensification and miniaturization of electromechanical and computing technologies, as well as due to the advancements in software, information, communication and control technologies, the boundary between the physical and the cyber domain is gradually dissolving. This is the evidential backing of our proposal to introduce the category of synergic technologies that will also support creating a bridge between physical and cyber technologies. In this paper a new reasoning model is introduced and used in the discussion of the related technologies. CPSs share an extensive set of distinguishing features based on which they can be identified, profiled and assessed. Nevertheless, there are huge differences not only in terms of the functionality, manifestation and applications of

CPSs, but also in terms of their intelligence, autonomy, adaptability and self-management. Many scientific, technological and application challenges arise from some intrinsic characteristics, such as symbiosis, complexity, heterogeneity, uncertainties, adaptability, scalability, robustness, safety, security, etc. which need to be addressed in research.  CPSs are supposed to (i) be installable everywhere (home, office, mobility, healthcare, entertainment, etc. environments), (ii) be used by everyone (individuals, special groups, social networks, cultures, populations, etc.), and (iii) serve for many purposes on a 24/7 availability basis. In addition to providing 100% connectivity and reliability, they should provide instantaneous and context-appropriate response, inform themselves and store the obtained or generated information, make reliable decisions either autonomously or collectively, and to be selfaware, self-sustaining, self-adapting and selfrepairing.  CPSs need to be equipped with the capability of first-, second- and third-level system learning. First-level learning involves collecting information through sensors and miners, and reasoning with the distilled information towards an optimized system operation in predefined task windows. Second-level learning enables a CPSs to learn the context of its operation, and to adapt to changing situations (to maintain a static normstate) by functional adaptation. Third-level learning enables them to adapt their structure and change their functionality according to the incoming combinations of synchronous and asynchronous events.  The next-generation of CPSs will not emerge by aggregating many un-coordinated ideas and technologies in an incremental fashion. Instead, they will require a more organized and coordinated attack on the synergy problem, driven by an overarching view of what the future outcome should be. Enforcing only the currently known design principles, such as establishing systemic (holistic) interconnections, model-driven specification, real time computation, eventoriented control, platform-based development, component-based implementation, serviceoriented functionality and minimal intrusiveness, will not suffice. More sophisticated principles are needed and should be taken into consideration not just in the embodiment design of CPSs, but Imre Horváth, Bart H. M. Gerritsen

[5]]

Figure 6 Multi-scale innformation traansfer

already in the stage of conceptuaalization.  Future C CPSs will innvolve not only o physicaal, but also cheemical and biiological sub b-systems in which informaation manifessts in very diifferent substtances and form ms, and existts on multiplle spatial andd time scales (F Figure 6). Thhese differen nces imply baarriers with rrespect to informatio on transfer and transform mation amonng heterogen neous sub-syystems that maay range froom particle systems thhrough biologiccal systemss and engiineered tecchnical systemss to galaactic systeems. Multii-scale informaatics (MSI) is supposed to havve the potentiaal to capturee information n and inform mation flows, and interconnect th hese sub-syystems physicallly, syntacctically, seemantically, and operativvely. MSI would w lend itself to an inntegral manifestation of innformation th heory, but iit still needs w widely-based and intense research. r

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