Data Management in Industry 4.0: State of the Art and Open Challenges
11 Data Management in Industry 4.0:State of the Art and Open Challenges
Theofanis P. Raptis, Andrea Passarella, and Marco Conti
Abstract —Information and communication technologies arepermeating all aspects of industrial and manufacturing systems,expediting the generation of large volumes of industrial data.This article surveys the recent literature on data management asit applies to networked industrial environments and identifiesseveral open research challenges for the future. As a firststep, we extract important data properties (volume, variety,traffic, criticality) and identify the corresponding data enablingtechnologies of diverse fundamental industrial use cases, basedon practical applications. Secondly, we provide a detailed outlineof recent industrial architectural designs with respect to theirdata management philosophy (data presence, data coordination,data computation) and the extent of their distributiveness. Then,we conduct a holistic survey of the recent literature from whichwe derive a taxonomy of the latest advances on industrial dataenabling technologies and data centric services, spanning all theway from the field level deep in the physical deployments, upto the cloud and applications level. Finally, motivated by therich conclusions of this critical analysis, we identify interestingopen challenges for future research. The concepts presented inthis article thematically cover the largest part of the industrialautomation pyramid layers. Our approach is multidisciplinary,as the selected publications were drawn from two fields; thecommunications, networking and computation field as well asthe industrial, manufacturing and automation field. The articlecan help the readers to deeply understand how data managementis currently applied in networked industrial environments, andselect interesting open research opportunities to pursue.
Index Terms —Data Management, Industrial Networks, Man-ufacturing, Industry 4.0.
I. I
NTRODUCTION T HE manufacturing industry needs to lead innovations toface the global competitive pressures in the advent ofintelligent manufacturing across the broad range of manufac-turing sectors [1]. The fourth industrial revolution, or
Industry4.0 (I4.0), which is being realized in the recent and nextyears, is expected to deeply change the future manufacturingand production processes, and lead to smart factories andnetworked industrial environments that will benefit from itsmain design principles: interoperability, virtualization, decen-tralization, distributed control and communication, real-timecapability, service orientation, quick and easy maintenance,low cost, and modularity [2]. In modern industrial applica-tions however, traditional centralized point-to-point control
T. P. Raptis, A. Passarella, and M. Conti are with the Institute of Informaticsand Telematics, National Research Council, Pisa 56124, Italy. email: {t.raptis,a.passarella, m.conti}@iit.cnr.it.This work has been partially funded by the European Commission throughthe FoF-RIA Project AUTOWARE: Wireless Autonomous, Reliable andResilient Production Operation Architecture for Cognitive Manufacturing (No.723909).
M2M CommunicationWSANIIoT / ICPS Assembly Line Industrial Robots NCS
Fig. 1. Pivotal technological enablers for the I4.0. and communication cannot be suitable to meet the increas-ingly challenging new requirements [3]. For this reason, mostmembers of the I4.0 community think in terms of decadesrather than years as to when the full I4.0 vision will becomestate-of-the-art [4]. The I4.0 is highly heterogeneous; in factit is the aggregation point of more than 30 different fields ofthe technology [5].In order to address the upcoming challenges of I4.0, severalpivotal technological enablers have emerged (Fig. 1). Novel assembly lines used in the production process are expected toboost the reconfiguration of automated manufacturing systemsand provide robust operation and short production lifecyclesneeded by manufacturing firms so as to stay competitive inthe marketplace [6]. The industrial Internet of Things (IIoT)and the industrial cyber-physical systems (ICPS) utilizationin industrial settings are expected to revolutionize the wayenterprises conduct their business from a holistic viewpoint,i.e., from shop-floor to business interactions, from suppliersto customers, and from design to support across the wholeproduct and service lifecycle [7]. The cost decrease comingfrom industrial robot integration in the production processtowards mass customization is expected to further improve therobot transparency and promote human-robot collaborations,just as if they were human-human collaborations, since therobot will have ideally the same set of skills and requirementsas a human co-worker [8].
Wireless sensor and actuatornetworks (WSAN) are able to provide remote monitoring andcontrol of factory plants and machines for the sake of reducingpotential equipment failures as well as improving the industrialefficiency and productivity [9].
Networked contol systems (NCS), which connect cyberspace to physical space enablingthe execution of several tasks from long distance, eliminateunnecessary wiring reducing the complexity and the overallcost in designing and implementing industrial solutions [10].The improvements coming from novel customized protocolstacks in machine-to-machine (M2M) communication , whichachieve multi-gigabyte per-second data rates, submicrosecond a r X i v : . [ c s . N I] M a y Data Enabling Industrial Technologies
Cloud ( global/local )M2M CommunicationWSANIIoT / ICPS Energy Management AnomaliesDetectionAssembly Line Industrial RobotsJob SchedulingDecision Making Big DataAnalytics Ontologies SemanticsMachine Learning S e c u r i t y Data Centric Industrial Services
Multi-agentSystems PrognosticsProduction ProcessSensors / ActuatorsSupervisionManufacturing ExecutionEnterpriseResource PlanningControl
Data Distribution
Focus of thesurvey (2015-2018)
Camera VisionAR / VR H u m an - i n - t he - l oop FaultDiagnosisApplication 1 Application 2 Application 3NCS
Fig. 2. Mapping of traditional automation pyramid (left) to I4.0 data enabling technologies and data centric services (right). latencies, and ultrahigh reliability, are expected to approximatethe I4.0 requirements [11].On top of those technological enablers, groundbreakingservices will further boost the I4.0 vision (Fig. 2).
Big data an-alytics , machine learning and semantic modeling are expectedto make industrial integration easier because the typical dataintegration involves a lot of data volumes, traffic, mappingsand conversions among different data formats [12]. Decisionmaking , job scheduling and human-in-the-loop approaches areexpected to constitute a kind of hybrid control systems witha dynamic structure and distributed intelligence capable ofmeeting industrial needs and rapid market changes [13]. Aug-mented reality (AR), virtual reality (VR), camera and visionidentification services are expected to [14] mimic the humaninformation processing system in order to take advantage ofand interpret the ambient industrial environment.
Prognostics and prediction processes, anomalies detection and fault diag-nosis are expected not only to enable the collection of data,but also to support advanced analytics to extract useful insightswith high returns on investments in the manufacturing industry[15]. Last but not least, local or global cloud integration,smart energy management and increased security solutions areexpected to horizontally fortify a more sustainable productionprocess [16].
A. The crucial role of data
The natural evolution of those industrial technological en-ablers and services leads to the generation of huge amountsof data ; data of many different volumes, traffic and criticality.Data will serve as a fundamental resource to promote I4.0from machine automation to information automation and thento knowledge automation. In the past several decades, large amounts of data have been generated in the industrial environ-ments, through to the wide use of networked control systems (NCS). At the very beginning, those large amounts of datahave rarely been used for detailed analyses, which were insteadonly used for routinely technical checks and process logfulfillments. Later, awareness of the importance in extractinginformation from data has taken a leading role for the I4.0[17]. This is because there has been an exponential increasein the number of data sources, both archival and in real time.However, data is not equal to value and consequently, to createvalue with data, one needs data processes which facilitate datareduction to actionable items thus creating value [18].
B. Contributions of this survey article
This article surveys the literature over the period 2015-2018on data enabling industrial technologies and data centric in-dustrial services from the point of view of data management asit applies to networked industrial environments and identifiesopen challenges for the future. A thorough research in twocategories of important journals has been conducted, based ontwo different but complementary groups of scientific fields: • Communications, Networking and Computation • Industrial, Manufacturing and AutomationFig. 3 displays the primary sources of information for thisarticle, identified after an exhaustive literature research. Thereare some articles coming from some other sources as well,but the list of Fig. 3 represents the sources from which thecritical mass of the references of this article were drawn. Thechoice of reported articles is highly selective, due to the factthat in order to be included, an article needs to provide newknowledge on a technological enabler, service, architectureor methodology directly applied on industrial environments.For this reason, a large portion of related literature which
IEEE Industrial Electronics Magazine IEEE Transactions on Automation Science and Engineering IEEE Transactions of Industrial Electronics IEEE Transactions on Industrial Informatics Elsevier Advanced Engineering Informatics Elsevier Computers in Industry Elsevier Journal of Industrial Information Integration Springer Journal of Intelligent Manufacturing T&F International Journal of Computer Integrated Manufacturing De Gruyter Automatisierungstechnik IEEE Communications Magazine IEEE Communications Surveys and Tutorials IEEE Internet of Things Journal IEEE Journal of Selected Areas in Communications IEEE Transactions on Communications IEEE Transactions on Mobile Computing IEEE Transactions on Network and Service Management IEEE Transactions on Parallel and Distributed Systems IEEE Transactions on Wireless Communications IEEE/ACM Transactions on Networking ACM Communications Elsevier Ad Hoc Networks Elsevier Computer Communications Elsevier Computer Networks Elsevier Journal of Network and Computer Applications Communications Networking Computation Industrial Manufacturing Automation Data Management in Networked Industrial Environments: State of the Art and Open Challenges SpecializedSources Multi- disciplinarySource IEEE Access
Fig. 3. Primary sources of information. Focus on two fields: Communica-tions/Networking/Computation and Industrial/Manufacturing/Automation. investigates similar concepts, but on environments other thanindustrial, has purposefully been excluded from the currentsurvey.Although there are existing surveys which cover some data-centric aspects of industrial processes, like industrial data man-agement, data-driven manufacturing and cloud manufacturing,to the best of our knowledge, there is no existing surveythat covers horizontally, in a holistic way, diverse aspects ofdata management in heterogenous networked environments ofindustrial deployments. Consequently, this is the first compre-hensive survey which discusses data management in networkedindustrial environments in a broad view, exposing different usecases, technologies and services that can facilitate the man-agement of distributed data. A comparison to other publishedsurveys is provided in section II. The major contributions ofthis article are the following.
Section III:
Data properties of fundamental I4.0 use cases
Section II:
Comparison with existing survey articles
Roadmap of this article
Section IV:
Data management trends in recent I4.0 architectural designs
Section V:
Data aspects of I4.0 technologies and services
Section VI:
Open challenges Use cases necessitating high data efficiencyOther use casesArch. focusing on assembly line and industrial robotsArch. focusing on IIoT / ICPS and WSANData enabling industrial technologiesData centric industrial services
Fig. 4. Roadmap of this article.
1) An extraction of data properties (volume, variety, traffic,criticality) and an identification of the correspondingdata enabling technologies in different I4.0 fundamentaluse cases, based on practical applications, is provided(section III).2) A detailed outline of recent I4.0 architectural designswith respect to their data management philosophy (datapresence, data coordination, data computation) and theextent of their distributiveness (section IV).3) A holistic survey and taxonomy of the latest I4.0 dataenabling technologies (section V-A) and data centricservices (section V-B), spanning all the way from thefield level deep in the physical deployments up to thecloud level. This outline is based on an exhaustiveresearch of recent publications and covers the largestpart of the I4.0 automation pyramid (Fig. 2).4) A discussion on future interesting open research chal-lenges regarding data management in networked indus-trial environments (section VI).To the best of our knowledge, such practical survey for dataproperties, management, technologies and services, for indus-trial networked environments, coming from recent researchcontributions does not exist in previous works. The roadmapof this article is displayed in Fig. 4.
II. C
OMPARISON WITH E XISTING R ELATED S URVEY A RTICLES
The purpose of this article is to provide a holistic overviewon data management as it applies to networked industrialenvironments. Although both data management and industrialnetworks are quite vibrant research fields, they are rarelymentioned together in a holistic manner. To the best ofour knowledge, this is the first time that the topics of datamanagement on the industrial networking realm are system-atically extracted, dissected, categorized and put together ina survey article, hence bridging the gap between these twoseemingly disconnected yet highly complementary paradigms.There exist, however, several published works that cover indepth multiple niche areas found in our survey. In fact, someof them explore several data centric aspects, but for focusedapplication areas, services and technologies. This section willprovide an overview of some of those relevant studies. TableI displays the comparison with other survey articles focusingon networked industrial environments.
A. Industrial data management
The most relevant to this article surveys investigate indus-trial data management. In [19], the authors present a surveyon the IIoT aspects of large-scale petrochemical plants as wellas recent activities in communication standards for the IoT inindustries, with a slight flavor of data management. The articleaddresses the key enabling middleware approaches, e.g., andhighlights the research issues of data management in the IoTfor large-scale petrochemical plants. As such, it is entirelyfocused on this specific use case. In [20], the authors providea survey of the recent developments in data fusion and machinelearning for industrial prognosis. To this end, a principledcategorization of feature extraction techniques and machinelearning methods is provided. This analysis is highly focusedon the data centric services of machine learning, data fusionand prognostics. Different from those works, we investigatedata management aspects in a much wider spectrum of usecases and data centric services.
B. Data-driven manufacturing
Another group of relevant articles is the surveys investigat-ing data-driven manufacturing. In [21], the authors focus onhighlighting the major specificities of data engineering and thedata-processing difficulties which are inherent to data comingfrom the manufacturing industry. They specifically emphasizeon the data centric services of machine learning and deeplearning and consequently the survey is highly focused bothin terms of use case and in terms of services. In [22], theauthors aim to provide an overview of data-based techniqueswith recent developments focused on the industrial closed-loop applications like process monitoring and control. Anotheroverview on the model-based control and data-driven controlmethods is presented in [23]. Those two articles focus entirelyon control related issues.
C. Cloud manufacturing
In [24] and [26], the authors survey the state of the artin the area of cloud manufacturing, identify recent concepts,implementations and technologies, and discuss potential re-search trends and opportunities. In [25], the authors provide areview of the more specific field of virtualization and cloud-based services for manufacturing systems and of the use ofbig data analytics for planning and control of manufacturingoperations. Although those surveys incorporate some datarelated concepts, they focus their investigation on the cloudlayer of networked manufacturing environments and explore aspecific subset of related technologies and services.
D. Industrial wireless standards
As wireless technologies penetrate more and more themanufacturing landscape, industrial wireless standards areemerging. [27] discusses key aspects of the four most popularindustrial wireless sensor network standards: ZigBee, Wire-lessHART, ISA100.11a, and WIA-PA. The detailed designand protocol architectures are comparatively examined. [28]provides a clear and structured overview of all the new802.15.4e mechanisms and describes the details of the main802.15.4e MAC behavior modes, namely Time Slotted Chan-nel Hopping (TSCH), Deterministic and Synchronous Multi-channel Extension (DSME), and Low Latency DeterministicNetwork (LLDN). [29] depicts a systematic approach to reviewIIoT technology standards and patents. The literature of emerg-ing IIoT standards from the International Organization forStandardization (ISO), the International Electrotechnical Com-mission (IEC) and the Guobiao standards (GB), and globalpatents issued in US, Europe, China and World IntellectualProperty Organization (WIPO) are systematically presentedin this study. [30] reviews the scheduling mechanisms for802.15.4-TSCH and slow channel hopping MAC in low powerindustrial wireless networks. It categorizes the numerous exist-ing solutions according to their objectives (e.g. high-reliability,mobility support) and approaches and identifies some openchallenges, expected to attract much attention over the nextfew years. All those studies provide an interesting glimpseinto the standardization domain for industrial networked en-vironments, but, naturally, their focus is highly specific andis very different from the holistic approach focusing on datamanagement which is presented in our survey.
E. IIoT technologies
Due to the fact that IIoT is a core technological enabler forthe realization of I4.0, there is a significant number of surveysthat report on various IIoT aspects. [31] provides an overviewof the Industrial Internet with the emphasis on the architecture,enabling technologies, applications, and existing challenges.More specifically, it investigates the enabling technologies ofeach layer that cover from industrial networking, industrial in-telligent sensing, cloud computing, big data, smart control, andsecurity management. Moreover, it discusses the applicationdomains that are gradually transformed by the Industrial Inter-net technologies, including energy, health care, manufacturing,
TABLE IC
OMPARISON WITH EXISTING SURVEY ARTICLES ON NETWORKED INDUSTRIAL ENVIRONMENTS (2015-2018).
Articles Focus area FocusTechnologies FocusServices
Data-centricaspects Comments
Currentarticle IIoT/ICPS, WSAN,Assembly Line,Industrial Robots,M2M Communication Machine Learning,Multi-agent Systems,Big Data Analytics,Prognostics, SecurityHuman-In-The-LoopEnergy Management,Job Scheduling,Decision MakingFault Diagnosis,Anomalies Detection,Ontologies/SemanticsCamera/Vision/AR/VR (cid:88) -[19], [20] Industrial DataManagement IIoT, WSAN,Assembly Line Machine Learning,Big Data Analytics,Prognostics,Human-In-The-Loop (cid:88)
Smaller number of use casesand data centric services[21]–[23] Data drivenmanufacturing IIoT, NCSAssembly Line Machine Learning,Multi-agent Systems (cid:88)
Very few use cases and services[24]–[26] Cloudmanufacturing IIoT, WSAN,Assembly Line Big Data Analytics,Job Scheduling,Decision Making (cid:88)
Narrow focus on cloud level[27]–[30] Industrial wirelessstandards IIoT, WSAN,M2M Communication - - Narrow focus onwireless communication[31]–[37] IIoTtechnologies IIoT, WSAN Energy Management,SecurityMachine Learning - Narrow focus on IIoTand WSAN technologies[38], [39] Industrialcognitive radio WSAN,M2M Communication Energy Management,Security - Highly specialized topic[40]–[42] Scheduling,synchronization NCS, Assembly Line Job Scheduling,Decision Making - Narrow focus onscheduling services[43]–[45] Productsystems Assembly Line Decision Making - Narrow focus onhigh-level applications public section, and transportation. A detailed discussion ondesign objectives, challenges, and solutions, for WSANs, arepresented in [32]. A careful evaluation of industrial systems,deadlines, and possible hazards in industrial atmosphere arediscussed. The primary objective of [33] is to explore thestate of the art as well as the state of practice of I4.0 relatingtechnologies in the construction industry by pointing out thepolitical, economic, social, technological, environmental andlegal implications of its adoption. The recent advancementsin FPGA technology, emphasizing the novel features that maysignificantly contribute to the development of more efficientdigital systems for industrial applications are presented in[34].Various proposed controllers for high-mix semiconductormanufacturing processes are surveyed in [35] from an appli-cation and theoretical point of view. Remaining challengesand directions for future work are also summarized with theintent of drawing attention to these problems in the systemsand process control communities. In [36], a comprehensivesurvey of IIoT technologies has been presented, includingIIoT architectural approaches, applications and characteristics,existing research efforts on control, networking and computingsystems in IIoT, as well as challenges and future researchneeds. Finally, in [37], the authors provide an overviewof the standards used to implement industrial WSANs anddiscuss the characteristics of the wireless channel in industrialenvironments. Different to the current survey, all those articles have an exclusive focus on a subset of technological enablers,IIoT and WSAN technologies.
F. Industrial cognitive radio
This is a specialized group of survey articles, which wereport in order to provide a complete list of relevant existingsurvey articles. The relevance to data management is minimal,but, nevertheless, the core technological enabler is alreadyapplied to industrial networked environments. [38] summarizescognitive radio methods relevant to industrial applications,covering cognitive radio architecture, spectrum access and in-terference management, spectrum sensing, dynamic spectrumaccess, game theory, and cognitive radio network security. [39]highlights and discusses important QoS requirements of IWSNas well as efforts of existing IWSN standards to address thechallenges. It also discusses the potential and how cognitiveradio and spectrum handoff can be useful in the attemptto provide real-time reliable and smooth communication forIWSNs.
G. Scheduling and synchronization
An interesting higher level application for the I4.0 is thescheduling and synchronization of multiple factories. [40]provides a review on the multi-factory machine scheduling.It classifies and reviews the literature according to shopenvironments, including single machine, parallel machines, flowshop, job shop, and open shop. The concept of proximityis used to analyze synchronization between suppliers and theconstruction site. [41] presents a framework for explaining I4.0concepts that increase or reduce proximity. The authors findthat Industry 4.0 technologies mainly influence technological,organizational, geographical and cognitive proximity dimen-sions. [42] gives a review on recent advances on the analysisand design of fuzzy-model-based nonlinear NCS with variousnetwork-induced limitations such as packet dropouts, timedelays, and signal quantization. With these network-inducedconstraints, the developments on various control and filteringdesign issues are surveyed in details, and some essentialtechnical difficulties are mentioned.
H. Product-service systems
Product-service systems are business models that providefor cohesive delivery of products and services. Product-servicesystem models are emerging as a means to enable collaborativeproduction and consumption of both products and services,with the aim of pro-environmental outcomes [46]. They arethus an important application on the top of the I4.0 automationpyramid. [43] is dedicated to the systematic status survey onproduct-service systems requirement management. The resultsof this work provides references for future research in thearea of product-service systems development, with the aimof offering integrated and holistic requirements managementfor product-service systems. It analyzes the state of the art ofrequirements management for product-service systems by re-viewing extensive literature of requirement identification, anal-ysis, specification, and forecast. [44] reviews multiple defecttypes of various inspected products which can be referencedfor further implementations and improvements. The objectiveof [45] is to provide a comprehensive literature review onrecent research and development in product modeling fromthree perspectives: product knowledge in product represen-tation, distributed computing in information technology, andproduct lifecycle in product development process. Contraryto our survey, this group of articles is distant both fromdata management and from industrial networking technologies.However, it is worthy having it reported, as it is a nice exampleof I4.0 post-production applications.In summary, our survey attempts to give a holistic reviewof the state-of-the-art regarding data management as it appliesto networked industrial environments. The review is centeredaround a plethora of technologies and services brought forthby the relevant I4.0 use cases and architectural designs, andprovides a more recent view of the industrial data managementfield. Our article is an ambitious effort to capture the interplaybetween data management and networked industrial environ-ments, instead of delving into one particular data centricservice or one data enabling technology exclusively. Themotivation behind this survey is to provide researchers comingfrom both the communications/networking/computation fieldsand the industrial/manufacturing/automation fields a glance ofthe intersection between these two domains at a higher level. III. D
ATA P ROPERTIES OF F UNDAMENTAL
I4.0 U SE C ASES
In this section, we provide a thorough extraction of dataproperties in different I4.0 fundamental use cases, based onpractical applications reported in recent research contributions.To the best of our knowledge, such practical extraction,coming from real world applications and reports does not existin previous work for the reported activity period. At the sametime, we identify the basic set of technological enablers thatare needed for the realization of those important use cases, andwe use them as a compass for the follow-up analysis which ispresented in section V. The extracted data properties about theuse cases are displayed in Table II. Our interest is to extractthree specific data properties, in order to understand the datarequirements in recent I4.0 use cases. The four data propertieswe focus on are the following:1)
Data volume : The size of the data to be circulated ina network environment is of crucial importance to thenetwork design and the technological enablers used inthe deployment. In industrial networked environmentsthere can be a diversity of data volumes, dependingon the scope of each use case. We label as data of small volume the data of lower sizes, such as sensormeasurements, of medium volume the data of highersizes, such as images or sound files, and of big volume,the data of the highest sizes, such as videos and detailed3D representations.2)
Data variety : The diversity of the data can also bevariable, according to the use case. We label as diverse the data variety in use cases where different kinds of dataare needed and as uniform the data variety in use caseswhere similar kinds of data are needed. The data varietycan significantly affect algorithmic decisions and serviceprovisioning when targeting efficient solutions per usecase.3)
Data traffic : Different data varieties, as well as differentdata generation velocities and use case requirements canlead to diverse traffic patterns in an industrial networkedenvironment. Although deterministic solutions for trafficregulation have started becoming mature for varioustypes of wired industrial deployments, the wireless partis still facing great challenges and comes hand in handwith strict I4.0 requirements. Communication supportfor industrial automation is challenging in wireless en-vironments as the lossy nature of radio links and nodeunreliability greatly affects the performance of real-timedata delivery. We label as intense the data traffic in a net-work where large amounts of data have to be generatedand delivered in small amounts of time, in many caseswithout predefined global schedules, typically leading tovarious networking problems necessitating algorithmicsolutions for traffic management. On the other hand, welabel as mild the data traffic in a network where data canbe circulated without serious problematic phenomena.4)
Data criticality : Data that are not managed accordingto the underlying I4.0 requirements may adversely af-fect the performance of system monitoring, control andsafety. For example in chemical plant, the chemical leakage must be informed in predefined times [47]. Thisinherent importance separates the data in two majorcategories, critical and non-critical data. We label thefirst category as data of high criticality and the secondcategory as data of low criticality.Based on the extracted data properties, we differentiate theuse cases in two categories: on the one hand we have the usecases which necessitate a combination of multiple “heavy”accomplishments in terms of data requirements and on theother hand we have the use cases with “light” data properties.The most important industrial use cases that we identified inthe recent literature are the following.
A. Use cases necessitating high data efficiency1) Oil / Gas:
Large-scale petrochemical plants incorporatedense wireless devices such as RFID tags for machine identifi-cation, sensors for large-scale rotational equipment monitoringand fault diagnosis, and employ IIoT technologies for tightand seamless integration between lower layer components,such as sensors and actuators, to the higher level connectedwith the cloud platforms [19]. In order to ensure the safetyof production sites in large petrochemical industries [49], andlong interconnected gas networks [50] those sensorial artifactsare positioned around gas pipes, targeting 24/7 monitoring.Data generated by the wireless sensors about parameters andabnormal events are processed for decision making therebyimproving production, predicting maintenance and failuresfor the industrial equipment. Data usually come from sensordevices in small volumes, typically including sensor mea-surements of various types. Although the variety can belimited to the various sensor readings, there can be increasedwireless traffic in the network; a result of thousands of sensorsoperating simultaneously both in real-time and periodically.The use case offers a mix of both critical and non-criticalapplications. An example of the first is a gas leakage must beinformed as soon as possible. An example of the second is thepredictive maintenance of a set of gas pipes over an intervalof some years.
2) Automotive:
In the last two decades, distributed em-bedded electronic applications have become the norm in alarge part of the automotive assembly industry. Due to criticalrequirements and the distributed nature of the various ECUsimplementing assembly functions, the validation of end-to-endtiming constraints on those networked industrial environmentshas become an important part of the design process of a car[52]. In addition to existing stand-alone solutions, cooperatingnetworked information and control systems are increasinglyused as tools for the coordination of this challenge for produc-tion support [53]. The volume of generated data can vary in theautomotive production process, providing also a great rangeof diversity. For example, there can be small volumes of data(positioning systems with various sensors for determination ofthe exact position of vehicles, tools, resources and processes),as well as big volumes of data (assembly assistance systemthrough monitors or data glasses which guide the workersduring their working process, by exploiting audio-visual data).The majority of the generated data is usually distributed via wired deterministic networks, and for this reason the traffic canbe regulated in an offline, centralized manner. For the samereason, the data criticality is not significantly high in this typeof use case.
3) Marine Vessels:
Today’s shipbuilding industry is char-acterized by one-off manufacturing and complex constructionprocesses, and as such, it is difficult to estimate a constructionprocess at the planning stage and many diverse problems areinvolved, such as backorders and over-loaded capacity betweenconsecutive processes [54]. Data processing, can be used forfault detection and diagnosis in such complex industrial pro-cesses, starting from the construction stage of a marine vesseland finishing at its running operation [55]. Sensing technologyis a cornerstone for many industrial applications, includingpreventative equipment maintenance, both inside fabricationplants and onboard the marine vessels [56]. Recent shipbuild-ing industry advancements introduce production managementmethodologies and a pre-verification in virtual environments.Related tools facilitate the traffic and criticality constraints onthe production phase and lower their intensity [57]. Similarto the automotive industry, the volume of generated data canvary in the marine vessel production process, providing alsoa great range of diversity.
4) Asset Tracking:
Mass production in manufacturing putsgreater emphasis on real-time asset location monitoring whichrenders the sensor data to be of paramount importance. Whenlocation information can be associated with monitored contex-tual information, e.g. machine power usage and vibration, itcan be used to provide smart monitoring information, such aswhich components have been machined by a worn or damagedtool [58]. RFID is the most commonly utilized product track-ing and automation technology, especially useful in the supplychain industry [59], as well as in more specialized industriesof asset tracking like identification of individual farm animals[61]. The generated data can be diverse over all asset trackingapplications, but usually only one tracking method is used foreach individual application, leading to a uniform data variety.The volume of the data also varies per application, comingfrom some simple RFID readings in product tracking to imagesor videos in farm identification. The data criticality is low, asthe related data processing and calculations are conducted aposteriori.
5) Customized Assembly:
Serial assembly lines are mainlyused for large scale production since they can provide shortcycle times and high production rates with high efficiencyin terms of cost, time and quality. In pursuit of flexibility,different paradigms have been investigated in terms of automa-tion level and production system organization [63], like cus-tomized assembly lines. IIoT integrates the key technologiesof industrial communication, computing, and control so as toprovide a new way for a wide range of assembly resources tooptimize management and dynamic scheduling [62]. With thetechnological enablers on flexible assembly lines ranging fromIIoT and ICPS to robotic bimanipulators, NCS and movingrobots, it is natural that there is a great diversity of dataresources to be analyzed. The volumes of data significantlydiffer from application to application. For example, in the caseof mobile robotic assembly, large volumes of motion data are
TABLE IID
ATA PROPERTIES EXTRACTED FROM RECENT WORKS ON VARIOUS
I4.0
USE CASES . Data
Use Case References Enabling Technologies
Volume Variety Traffic Criticality
Oil / Gas [19], [48]–[50] IIoT, WSAN,M2M Communication small uniform intense low / highAutomotive [51]–[53] IIoT / ICPS, Assembly Line,NCS, Industrial Robots small / big diverse mild lowMarineVessels [54]–[57] Assembly Line,NCS, Industrial Robots small / big diverse mild low / highAssetTracking [58]–[61] IIoT / ICPS small / big uniform mild lowCustomizedAssembly [62], [63] IIoT, Assembly Line,NCS, Industrial Robots small / big diverse intense highCraneScheduling [64], [65] IIoT / ICPS small uniform mild lowRefrigeratedWarehouses [66] WSAN small uniform mild lowHealthcareMonitoring [67] WSAN small uniform mild lowProductionControl [68]–[70] IIoT, NCS,Assembly Line small / big diverse mild / intense low usually exchanged between the different controllers for furtherdata fusion, while in the case of custom part identification,smaller identification data are needed. This use case family isusually characterized by a high criticality factor, due to thefact that the assembly process has to be quick and accurate,affecting accordingly the related data processes.
B. Other usecases1) Crane Scheduling:
Container terminals have to improvetheir service efficiency to seek the optimal trade-off betweenenergy-saving and service efficiency improvement. Since theenergy consumption and service efficiency of container ter-minals are mainly contributed by the handling cranes, thescheduling of the handling cranes is critical [64]. Moreover,with the increase of sizes of container vessels, containerterminals are encountering another challenge, i.e., the rapidhandling of containers for mega-vessels. Thus, container ter-minals must shorten the vessel turnaround time, which isan influential factor of their service level [65]. Due to thefact that the necessary computations are conducted in anoffline manner, usually via optimization modules, the dataproperties of this use case are simple. An input module, whichis the basis for generating crane schedules and evaluating theschedules, consists of two data parts: static data and dynamicdata. The static data part include all parameters such as thehandling volume of each container, the time window on eachcontainer and the handling efficiency of each crane. The otherparameters are used for evaluation, such as the cost of unitenergy consumption. The dynamic data include all decisionvariables, which are generated by the optimization module.
2) Refridgerated Warehouses:
Changing the cold storagetemperature set points of the refrigerated warehouses willcause the reduction of product quality and further increaseeconomic costs to the industrial consumers. Reduction of theelectricity price on the grid, the total costs of maintenance,and the total energy consumption comparing has recently beena target objective of operations research [66]. This use case is characterized by small volumes of sensor data (mainlytemperature), periodically sent to a central control station forlong term planning.
3) Healthcare monitoring:
Industrial manufacturing hasrecently started embedding new functions in the form of safetymonitoring or smart factories. Another recent trend of interestis the combination of heterogeneous services from differentfields for providing automated healthcare services in industrialenvironments [67]. As with typical monitoring use cases, thedata come in small volumes, from a range of different butlimited sensors targeting long term or real-time healthcareoptimization.
4) Production Control:
Controlling the various stages andprocesses during the production process has attracted awidespread research interest in various areas, ranging fromthe shop floor with vibration control [68], PLC design control[69] up to the application layer with economic optimizations[70]. Depending on the layer of the industrial integration weare considering, data volumes can be small or large, and therelated traffic in the networked environment low or high.IV. D
ATA M ANAGEMENT T RENDS IN R ECENT
I4.0A
RCHITECTURAL D ESIGNS
In this section we attempt to place recent architecturalinnovations in the broader context of networked industrialenvironments by surveying the fundamentals of both recentlyproposed I4.0 enabling architectures and by extracting the datamanagement philosophy of these architectural alternatives. Thesection’s primary emphasis concerns data related concepts,rather than specific architectural constructs. A number ofresearch teams have proposed the development of relevant ar-chitectures which incorporate either directly or indirectly somekind of data management interfaces and control mechanismsacross one or more architectural layers. For the reported pe-riod, 2015-2018, the most important I4.0 enabling architecturaldesigns have been presented in [71]–[95].The data management information is displayed in Table III.We aim at extracting three specific data properties, in order to
TABLE IIID
ATA MANAGEMENT TRENDS IN RECENT
I4.0
ARCHITECTURAL DESIGNS . Data
Description References Supported Technologies
Presence Coordination Computation
Mass customization [72] Assembly Line localized centralized concentratedManufacturing service composition [82] Assembly Line localized centralized concentratedComputer integrated manufacturing [92] Assembly Line localized centralized concentratedCollaborative manufacturing [74] Assembly Line localized centralized distributedDynamic manufacturing reconfiguration [84] Assembly Line, NCS localized centralized distributedCloud manufacturing [77][93] Assembly Line, NCS ubiquitous hierarchicalcentralized distributedconcentratedNCS SW reuse and integration [86] NCS localized centralized concentratedControl-based robot navigation [71] Industrial Robots localized centralized concentratedDeterministic consumer services [78] IIoT ubiquitous centralized concentratedGreen IIoT [76] IIoT ubiquitous centralized concentratedService-oriented modeling [75][83][91] IIoT, WSAN, NCS ubiquitous hierarchicalcentralized distributedHierarchical data communication [81][87][95] IIoT / ICPS, WSAN, NCS,M2M Communication ubiquitous hierarchical distributedCommunication harmonization [73] IIoT / ICPS,M2M Communication ubiquitous centralized concentratedPlant-wide process monitoring [88] WSAN, NCS ubiquitous centralized concentratedWireless networked control systems [80] WSAN, NCS ubiquitous centralized concentrated understand the recent trends in recent I4.0 architectural design.Meanwhile, we also identify the major supported technologicalenablers per architectural design. The three data properties wefocus are the following:1)
Data presence : Data can be acquired from specificallydefined, localized sources, or from pervasive data gen-erators. We label the first category as localized datapresence. This category usually includes (but is notlimited to) data generation sources such as fixed roboticmanipulators in a factory environment, stationary net-work controllers, servers, office workstations, and field-bus masters. We label the second category as ubiquitous data presence. This category includes (but, again, isnot limited to) workers’ portable devices, IIoT enablers,sensors and actuators with uncertain communicationpatterns and online third party data sources (e.g., viaInternet).2)
Data coordination : Coordination of the industrial pro-cesses, based on the input data, can be performed byglobal or local process (or network) managers. In thecase of involvement of local managers, usually hierarchyis applied, where the coordination is structured amongdifferent layers of managers. We label the first caseof global managers as centralized coordination and thesecond case of local managers participating in hierar-chical managing as hierarchical coordination. The mostusual trade-off that exists between the different types ofcoordination is balancing the effect of central controlon the network over the minimization of important met-rics such as end-to-end data delivery delay and energyconsumption.3)
Data computation : Computation tasks over the receiveddata can take place either on central entities withsignificant computational abilities (which may or maynot coincide with the coordination managers) or on a large part, or all, of the devices available in thearchitectural design. We label the first method as concen-trated computation and the second method as distributed computation. Following the concentrated computationmodel, implies stronger computational power located onsingle computational components, while following thedistributed computation model implies that computationcomponents are located on different networked comput-ers (usually of lower computational ability compared tothe concentrated computation case), which communicateand coordinate their actions by passing data to oneanother. As with typical distributed systems, the threesignificant characteristics of distributed computation inI4.0 are concurrency of computations, lack of a globalclock, and independent failure of the computationaldevices. For this reason, usually, a failure in the concen-trated computation case can lead to much higher failureimpact on the industrial processes.A conclusion drawn by the information extracted by therelevant articles and provided in Table III is that the archi-tectural trends can be classified in two distinct categories,each one with their respective data management philosophy.On the one hand, we have a set of architectures dealingmostly with localized data, coordinating the industrial devicesin a centralized manner and providing a mix of either con-centrated or distributed computing. The basic data enablingtechnologies for those architectural designs are the assemblyline and the industrial robots. On the other hand, we havea set of architectures dealing mostly with ubiquitous datapresence, with a twist on coordination towards a hierarchicalmanner, providing again a mix of centralized and distributedcomputation. The basic data enabling technologies for thosearchitectural designs are IIoT / ICPS, and WSAN. Thisdistinction in two categories of architectural data manage-ment makes clear also the diversity of the two research fields (Communications/Networking/Computation and Indus-trial/Manufacturing/Automation), as well as the necessity ofa convergence between the two fields in order to address theI4.0 requirements with common tools and methodologies. Thisfact is identified as an open challenge for the future and is alsopresented in section VI-D. A. Architectures focusing on assembly line and industrialrobots
In [72], the authors introduce an architecture for the designand customization of product families. Specifically, they de-sign a formal computer-assisted approach that addresses therequirements for the design of product family architecture asidentified by academia and industry. The suggested design isbased on formal computational models which employ relatedcentralized methods, not leaving much space for ubiquitousdata presence and coordination.In [74], the authors present an architectural design for inter-operable end-to-end manufacturing which guarantees seamlessinteroperability, thus ensuring proper communication and dataexchange between all the partners in a manufacturing networkthroughout the entire manufacturing life cycle, from suppliersearch to manufacturing execution and monitoring. In terms ofdata presence, although the data can lie on different physicallocations (e.g., different factories) we consider the layout aslocalized, since it is perfectly defined beforehands where,when and how the data will be accessed by the platformprovided in the architecture.Cloud manufacturing has been a vibrant field for architec-tural research. In [77], the authors argue that existing cloudmanufacturing models operate in a centralized way througha cloud manufacturing platform, the management of which isidentified as a critical part of the manufacturing cloud oper-ation, and strive for decentralization. In fact, they propose adecentralized network architecture which builds upon the con-cept of autonomous work systems for use as service providers(Fig. 5). In this design, data can be generated from varioussources, even from third-party online knowledge clouds andthe various computations can happen in different cloud ser-vices, with a decentralized coordination, distributively amongthe users. In [93] the authors introduce the concept of a cloudmanufacturing framework with auto-scaling capability, aimingat providing a systematic and rapid development approach forbuilding cloud manufacturing systems. Contrary to [77], thedesign of [93] provides a structured and centralized bulletinboard data exchange mechanism, serving specifically defineddata. However, due to the fact that workers are involved inthe design, the number of which varies from time to time(due to the auto-scaling mechanism of the cloud manufacturingframework), the data presence can be considered as ubiquitousalso in this case.In [82], the authors investigate how to find the optimalmanufacturing service composition path from a service com-position network. In order to satisfy the specific demandsof manufacturing service composition, they provide a designwhich solves two problems: how to design the appropriateQoS evaluation model to depict the manufacturing service M anu f a c t u r i ng s e r v i c e p r o v i de r s M anu f a c t u r i ng s e r v i c e end - u s e r s Manufacturing clouds service inquiryservice
Fig. 5. Decentralized architecture for cloud manufacturing [77]. composition based on networked collaboration, and how toimprove the existing service composition method to deal withthe rapid increase of candidate service composition solutions.The structure of service supporting system they propose ishighly centralized, with regulated coordination and computa-tion of the data resources, which come on the one end frommanufacturing, lab and management sources, and on the otherend from service requestors.In [84], the authors introduce a service oriented architec-tural framework that supports a new programming paradigmfor designing dynamic distributed manufacturing systems.The framework supports concurrency and reactivity of mul-tiple computing machines that run data computations asyn-chronously with each other. Each machine is potentially run-ning concurrent software behaviors that need to execute insynchronously with each other. The entire coordination of theoperations is regulated by a master controller.In [86], the authors design an architecture to integratemodules of two industrial standards, IEC 61131-3 and IEC61499, allowing the exploitation of the benefits of both. Theproposed architecture is based on the coexistence of controlsoftware of the two standards. As both standards refer toPLCs and control systems, the presence, coordination andcomputation of data are fundamentally concentrated.In [92], the authors propose a layered architecture whichcovers five critical aspects of computer integrated manufactur-ing, separated in five architectural layers: physical, functional,managerial, informational and control. Although the holisticdesign of this architecture is hierarchical and each layer is aseparate entity from the other layers, the intra-layer functionsregarding coordination and computation can be consideredfocused on central entities.In [71], the authors present a general framework for mobilerobot navigation in industrial environments in which the open-loop behavior of the robot and the specifications are based LocalManager LocalManager LocalManagerGlobal ManagerCommunication technology 3Communication technology 2Communication technology 1
Fig. 6. Hybrid wireless communication and data management architecture[81]. on automata. A modular supervisory controller ensures thecorrect navigation of the robot in the presence of unpre-dictable obstacles and is obtained by the conjunction of twosupervisors: a first one that enforces the robot to follow thepath defined by the planner and a second one that imposesother specifications such as prevention of collisions, task andmovement management, and distinction between permanentand intermittent obstacles. The data related components arehighly centralized both in the planning and in the supervisingprocess of the robot.
B. Architectures focusing on IIoT / ICPS, and WSAN
In [81], the authors introduce a hybrid wireless commu-nication and data management architectural design (Fig. 6).This design is coined as hybrid due to the fact that it isactually a multi-tier network architecture in which distributedcommunication and data entities interact in order to coor-dinate their decisions in a hierarchical manner and ensurethe correct operation of the whole network. Devices scatteredin the network deployment have the ability to perform localcomputations, lightening the burden of local and global man-agers by offloading data and computation. The architecture isdesigned to support ubiquitous data existence in various typesof industrial environments.In [76], the authors present an energy-efficient architecturefor IIoT deployments, which consists of an IIoT nodes domain,RESTful service hosted networks, a cloud server, and userapplications. This architecture focuses on the IIoT domainwhere large amounts of energy are consumed by large numbersof nodes. The architecture includes three layers: the IIoT layer,the gateway layer, and the control layer (Fig. 7). Unlike otherhierarchical deployment schemes like [81], in this architecturedirect communications between IIoT nodes are not allowed.Also, the gateway nodes are always used as central com-putation entities and the control node as coordination entity,allowing IIoT nodes to not necessary to implement sophisti-cated hardware or run complicated routing mechanisms, thusreducing computational complexity and system cost.
Sensing layer Gateway layer Control layer
Fig. 7. Three-layered IIoT architectural design [76].
In [78], the authors argue that a convergence between deter-ministic industrial networks and best effort IIoT should occurand support low latency and jitter, and based on this argument,they provide an architectural design for a deterministic IIoTcore network consisting of many simple deterministic packetswitches configured by an SDN control plane. Although thereis a pervasive presence of data due to the IIoT support, thedeterminism imposes a highly centralized data coordinationand schedules computation.In [80], the authors propose a closed loop design in or-der to facilitate the deployment of fully automated wirelessnetworked control systems. The topology of the architectureconsists of a plant system having sensor and actuator nodes, acontroller system having input and output nodes, an intermedi-ate network system having interconnected nodes, and wirelesscommunication links for the information transfer between thedifferent nodes (Fig. 8). The data presence in this setting isubiquitous, as data can be received by a wide number ofsensors placed in the network. However, both the computationand the coordination is taking place centrally at the controllersystem, which uses the input nodes to receive information andthe output nodes to provide controller decisions.Service-oriented modeling has attracted a lot of attentionin the I4.0 architectural design community. In [75], the au-thors suggest a service oriented architecture which exposesobjects’ capabilities by means of web services, thus support-ing syntactic and semantic interoperability among differenttechnologies. WSAN devices and legacy subsystems cooperatewhile orchestrated by a manager in charge of enforcing adistributed logic. The architecture supports dynamic spectrummanagement, distributed control logic, object virtualization,WSANs gateways, a SCADA gateway service, and data fusiontransport capability. In order to implement those functional-ities, a hierarchical coordination scheme has been followedwith different kinds of managers provided as reusable coresoftware components. The middleware’s virtualization layerenables the architecture to support pervasive data access andmanagement. In [83], the authors suggest another serviceoriented architecture, targeting structured migration of process control systems. The argue that although today’s controlsystems are typically structured in a hierarchical manner,there are nevertheless non-resolved challenges with respect tovarious fundamental migration functionalities. The suggestedapproach combines distributed computation abilities with aper-layer centralized coordination, handling data coming fromubiquitous data sources like WSANs. A particular note aboutthis design is that the coordination can also be viewed asdecentralized, if we consider the entire system definition andif we do not examine each architectural layer individually. In[91], the authors argue that the scope of I4.0 shall be definedby considering the major value chains and in order to achievethis they introduce a design and the basic process to achievea reference model for I4.0 service architectures. The designrelies upon the assumption that a reference model shouldtake into account existing reference models for distributedprocessing as well as those of the Internet of Service and IIoT.This architecture provides a computational modularity whichenables distribution through functional decomposition of thesystem into objects which interact at interfaces.In [87], [95], the authors introduce two different, yet com-plementary hierarchical data transmission architectural designsfor WSAN and smart factories. Those architectures constitutean ideal example of pervasive data generation, as data arereceived from a wide variety of stationary and mobile sources,such as automatic guided vehicles, mobile workers’ devicesand WSANs. Hierarchical coordination lies at the core ofthose designs as well as the decentralized computation throughsubnetworks formation, leader election algorithms and mobileintelligence units.In [88], the authors introduce a distributed modeling frame-work for plant-wide process monitoring. Based on this frame-work, the plant-wide monitoring process is decomposed intodifferent blocks, and statistical data models are constructedin those blocks. The data obtained from different blocksare integrated through a centrally located decision fusionalgorithm. Due to the large volume of the pervasive plant-wide data generation, the authors note that unlike traditionalindustrial processes, several new data characteristics should be Field layer M2M layerNCS layer
Fig. 8. Closed loop architectural design for automated wireless NCS [80]. paid attention to in the plant-wide process: the data volumein the plant-wide process is larger, different types of data canbe obtained, sampling rates of process variables are alwaysdifferent from each other, and the density of the collected datafrom the plant-wide process may be quite low.Finally, in [73], the authors, rather than presenting a con-crete architecture, are providing the future I4.0 architecturalinsights, based on current designs and future trends, focusingon TSN and 5G designs. Although their analysis includesdifferent vertical integration layers (which enable ubiquitousdata presence), it seems that the data coordination and therelevant computations are considered centralized, for the sakeof ultra-high reliability.V. D
ATA A SPECTS OF
I4.0 T
ECHNOLOGIES AND S ERVICES
In this section, we provide a holistic outline of the latestI4.0 data enabling technologies and data-centric services, thatwere identified through the exhaustive state of the art research,spanning all the way from the field level deep in the physicaldeployments up to the cloud level. Fig. 2 visually displays thepartitioning of the networked industrial environment buildingblocks in two fundamental planes: data enabling industrialtechnologies and data centric industrial services. It is visiblethat each building block can have thematic and functionaloverlaps with other building blocks that lie in its proximity.This is natural, and is due to the interplay between moderntechnologies and services. The articles that we have identifiedand present in this article on I4.0 technologies and servicesare displayed in Fig. 9. In fact, the information presented inFig. 9 provides a concise classification in the two categoriesof the recent research works.
A. Data enabling industrial technologies1) IIoT / ICPS:
Industrial networked environments arecomposed of the physical part, which performs the physicalprocesses, and networks of IIoT devices, which perform thecomputational processes required to control the physical ones.The cyber part of the system is constituted by computationalprocesses, which receive data from the physical processes,calculate the required outputs and apply them to the physicalplant [118], providing and using, at the same time, dataaccessing and data processing services available on the Internet[120]. Due to the fact that production scheduling is optimizedusing objective functions based on punctuality criteria such asearliness and tardiness [117], significant part of those compu-tations are taking place at the edge of the IIoT deployments,transforming edge computing in a fundamental type of compu-tation, with contributions ranging from adaptive transmissionoptimization [109] to multiple gateway optimization [110].Additionally, different IIoT deployments usually incorporatedifferent communication and networking alternatives, such asWIrelessHART [105], RPL [126] and 6TiSCH [106], as wellas frequent protocol conversions [103], operations which haveto seamlessly exchange data with each other. ConsequentlyIIoT and ICPS technologies enable intelligent, adaptive controlwith seamless vertical, horizontal and dynamic data exchangebetween heterogeneous platforms and networks, through an Data Management in Networked Industrial Environments
Data Enabling Industrial Technologies
IIoT / ICPS: [96]–[127]WSAN: [128]–[151]NCS: [152]–[162]Industrial Robots: [163]–[192]Assembly Line: [193]–[221]M2M communication: [222]–[257]
Data Centric Industrial Services
AR / VR: [258], [259]Camera / Vision: [260]–[262]Prognostics: [263]–[268]Anomalies Detection: [269]–[273]Fault Diagnosis: [274]–[280]Multi-Agent Systems: [281]–[289]Decision Making: [290]–[292]Job Scheduling: [293]–[301]Machine Learning [302]–[314]Big Data Analytics: [315]–[324]Ontologies / Semantics: [325]–[345]Human-in-the-loop: [346]–[352]Security: [353]–[363]Energy Management: [364]–[399]Cloud: [400]–[409]
Fig. 9. Taxonomy of I4.0 data management enablers. exhaustive use of data exchange, coordination and collabora-tion [119], as well as through recently proposed techniqueslike network slicing [114]. Important ICPS operations includefault management [121], clustering analytics [122], reusablesoftware [123], as well as reactive test case generation [124]and modular reconfiguration [125]. Typical IIoT applicationsinclude predictive maintenance [100], where a successfulnetwork configuration is able to determine the condition of thein-service equipment in order to estimate when maintenance should be performed, real-time RFID monitoring [96], fortracking products in the assembly line. Other research issuesinclude IIoT topology optimization [97], packet scheduling[102], and IIoT network construction and operation undermassive multiple-input multiple-output M2M communication[113].There have been some interesting recent data related ad-vancements in the IIoT domain. In [98], the authors identifythe need for data access control along the supply chain, especially when it comes to product data related to sensitivebusiness issues, and they design a scalable industry data accesscontrol system that addresses these limitations. In [101], theauthors present an industrial data exchange mechanism basedon ZeroMQ for the ubiquitous data access in rich sensingpervasive industrial applications. This investigation highlightsthe major concerns in building a distributed industrial datasystem in a systematic manner. In [104], identify that most ofthe current data clustering techniques that could only deal withstatic data become infeasible to cluster the significant volumeof data in the dynamic industrial applications, and introduce anincremental clustering algorithm by fast finding and searchingof density peaks based on k-mediods, as a way to find theunderlying pattern structures embedded in unlabeled data.Driven by the pursuit of green communication, the authorsof [116] present a space reserved cooperative data cachingscheme for IIoT, where the cache space in a base station isdivided into two parts, one is used to store the prefetcheddata from the servers ahead of the device request time andthe other is reserved to store the temporarily buffering datain the wireless transmission queue at the device request time.Timely data delivery is also another crucial data managementissue in IIoT, and has been frequently combined with theoptimization of other important metrics. For example, in [112],the authors provide a loss tolerant data delivery scheme withlow energy consumption and end-to-end guarantees. In [127],the authors present a method for identifying and selecting alimited set of proxies in the IIoT network where data neededby the consumer nodes can be cached, so as to guaranteetimely data access. In [115] they combine it with MAClayer improvements, in [111] with incremental time-triggereddata flows, and in [99] with a fusion of relaying and dataaggregation at the source nodes. Regarding this, there aremultiple open challenges to address, such as security concerns(the specific case of DDoS mitigation was addressed in [108]),and estimation accuracy [107].
2) WSAN:
WSAN are defined as a group of spatiallydispersed and dedicated sensors and actuators for indoor [135]and outdoor [131] monitoring and recording of the physicalconditions of the industrial environment. WSAN cooperativelydeliver the collected data at a central location via single-hopor multi-hop communication [150]. WSANs measure environ-mental conditions like temperature, sound, pollution levels,humidity, and so on. In fact, WSANs are the base to establisha supervisory control and data acquisition system with thebenefits of extending the network boundaries and enhancingthe network scalability of the industrial environments [151].Recent research interest in the data-driven industrial WSANliterature has been focused on a number of emerging problems.Localization achieved by using the available plant data inWSAN-enabled industrial environments is one of the problemsaddressed, both in terms of finding the optimal placementsensor locations in the industrial space space (with Delaunaytriangulations [129] or particle swarm optimizations [149])and of managing to effectively localize mobile robots [142].The industrial environment that the WSANs operate in is verychallenging because of dust, heat, water, electromagnetic in-terference, and interference from other wireless devices, which make it difficult for current WSANs to guarantee reliable real-time communication. For this reason several communicationoriented performance improvements have been achieved. Suchimprovements include reliable communication slot assignment[128], autonomous channel switching for spectrum sharing[130], synchronization for nodes with imprecise timers [138],and real-time link quality estimation [144]. Cooperative datarelaying schemes also facilitate secure and interference-freedata management, with recent approaches employing fountain-coding aided transmissions [132] and belief function basedcooperation [134]. Other interesting identified data-drivenproblems for industrial WSANs include neighbor discoverywith mobile nodes based on distributed topology data [141],network isolation avoidance based on local energy data [145],distributed node clustering based on (among others) nodesimilarity data [139], and coverage data hole healing [148].Data routing improvements are also traditionally a core re-search aspect, recently with approaches targeting networkstability based on nodal data [143], and reliable, SNR-assured,anti-jamming data transfers [147]. Cross-layer optimizationframeworks have also been proposed for this technologicalenabler, with SchedEx-GA [133] (spanning MAC layer andnetwork layer) attempting to identify a network configurationthat fulfills all application-specific process requirements overa topology, and CLOC [137], attempting at maximizing theminimum resource redundancy of the network under systemstability and schedulability constraints. Last but not least, data-driven learning with sensing data [136], delay and energyimprovements with empirical data [140], [146] have alsoemerged as important research directions, especially with theintroduction of local clouds in the production process.
3) NCS:
NCS are control systems wherein the control loopsare closed through a communication network. An NCS uses anetwork as a communication medium to connect the plant to acentral controller [153]. The defining feature of an NCS is thatcontrol data and feedback data are exchanged among the sys-tem’s components in the form of data through a network. Themost important feature of NCS is that they connect cyberspaceto physical space enabling the execution of several tasks fromlong distance. In addition, networked control systems eliminateunnecessary wiring reducing the complexity and the overallcost in designing and implementing the control systems. Theycan also be easily modified or upgraded by adding sen-sors, actuators. Usual types of such network communicationare fieldbuses like CAN and LON, wired connections likeIP/Ethernet, etc. Automated or semiautomated verification ofaccess control is a necessary building block in NCS [152],and sampled-data control has been proven to guarantee theirsynchronization by reducing the updating frequency of thecontroller and the network communication burden [161]. Dueto the difficulty in observing the full relationship amongnumerous NCS components, high-dimensional and sparsema-trices describing partial relationships among them have beenrecently introduced [159]. NCS can also be used to connectdifferent plants with solutions provided to achieve given spec-ifications when there are communication delays and losses incommunication networks linking central network controllersand the plants [154]. Data-driven network control is known Industrial RobotsStationary RobotsTracking controland correction:[164], [174], [175][167], [180], [182][184], [186], [190][191]Collaboration:[179], [187] Mobile RobotsLocalization:[165], [169], [172]Navigation:[166], [168], [171][173], [181], [188]Collaboration:[176]–[178], [185]Other operations:[163], [183], [192]
Fig. 10. Industrial robots: An I4.0 data enabling technology. to be one of the most efficient control schemes for complexindustrial processes due to the difficulty in obtaining accuratemathematical control models [155] and to the frequent exis-tence of nonlinearities and stochastic disturbances [156]. Infact, data delivery latency is among the most active topics inthe NCS field recently. Networked degradations such as datadelivery delay and data dropout can nevertheless cause NCS tofail to satisfy performance requirements, and eventually affectthe overall reliability [158]. In order to address this problem,NCS can be specified in the form of function blocks throughrelevant standards such as the IEC 61499 standard, the end-to-end data delivery latency over switched Ethernet of whichcan be assessed with low complexity techniques [157]. Also,delay compensation schemes for NCS using CAN bus [160],as well as energy efficient sampling methods [162] have beenpresented.
4) Industrial Robots:
Robot systems have been widely usedin industry and also play an important role in human sociallife [189]. Industrial robot research can be classified in twocategories (Fig. 10): stationary robots and mobile robots.Tracking control of robot manipulators is a fundamentaland significant problem in robotic industry [191]. Trackingcontrol of robotic manipulators with uncertain kinematicsand dynamics (gravitational torque, friction torque, momentof inertia and disturbance) is addressed using data-drivenobserver-based control designs [174], some of which providingconvergence of tracking errors [175]. Preplanned path trackingcorrections of robotic [164] or teleoperated manipulators [167]can be achieved through iterative learning control algorithms.Smaller robotic parts of larger potential constructs can be con-trolled distributively through redundant actuation (an exampleis provided in [170], for a tracking control of a joint). Energy and power efficient methods have also been presented, for anumber of cost functions [180]. Manipulability optimizationof redundant manipulators is shown to be achieved throughdynamic neural networks [182]. Neural control is also appliedin the case of bimanual robots (which are able to performmore complicated tasks that a single manipulator), resultingin guaranteed stability and precision [184], or in reduced vi-brations [186]. Data delivery delay is also an important aspect,subject to minimization, shown to be decreased with practicaland adaptive time-delay control schemes [190]. Coordinationand cooperation control for networked mobile manipulatorsover a jointly connected topology with time delays is anothertopic that needs fast data delivery in the network [179].Modular design has been proven helpful in the configurationof multirobot cooperation (for example in [187] for sewingpersonalized stent grafts).Localization of mobile robots in industrial environments isa classic topic that will remain challenging in the I4.0 era. Mo-bile robots operating in indoor environments [169] can be lo-calized with a combination of data coming from heterogeneoussensors, and those operating in outdoor environments [172],with a combination of ambient data (movement dynamics,velocity data, RSSI) High-precision probabilistic localizationof mobile robotic fish can be achieved using visual and inertialcues [165]. Robot navigation in space is another major topicfor data-driven research. Online navigation of humanoid robotshas been proven feasible through multi-objective evolution-ary approaches [166]. Wall-following trajectory control ofhexapod robots can be realized via data-driven fuzzy controllearned through differential evolution [168] and relevant un-certainties can be addressed with decentralizing this controlwith dynamic controllers [171]. Homing (mobile robot returnsback to a reference home position) using just the visualinformation can be implemented by extracting coarse locationdata with respect to the reference position using a bit encod-ing algorithms [173]. Autonomous exploration using mobileclimbing robot allow dangerous tasks to be completed morequickly and more safely than is possible with human inspectors[181]. Wireless charging helps mobile robot to become moreand more autonomous and navigate easier [188]. Except fornavigation, several approaches regarding other robot propertieshave been presented, such as balancing and velocity control[163] with in-wheel motors, human behavior transfer to robotsthrough learning by imitation/demonstration [183] and visualservo regulation with simultaneous depth identification [192].Robot collaboration and data sharing is also an emerginginteresting research issue. Teleoperation control frameworksfor multiple coordinated mobile robots through have beenproposed using a brain-machine interface [177]. A particularlyinteresting topic in the mobile robots collaboration field fieldis the collaborative and adaptive data sharing. Collaborativerobots are multirobot systems working together for the sameindustrial task such as robotic assembling. To achieve anefficient collaboration, robots require not only locally sensingthe environmental data but also immediately sharing thesedata with neighbors. However, there exists a dilemma betweenthe large amount of sensory data and the limited wirelessbandwidth. The relevant problem of throughput maximization of sensory data sharing in collaborative robots has been studiedin [176]. Another interesting topic which again necessitatesdistributed data exchange is the consensus problem. Theconsensus problem has experienced a fair amount of researchinterest, aiming at forcing a group of mobile robots to reachan agreement on a quantity of interest such as the rendezvousposition, velocity, and heading direction [178]. Multiple robotscan also collaboratively achieve a common coverage goalefficiently, which can improve work capacity, share coveragetasks, and reduce completion time [185].
5) Assembly Line:
The assembly process is composed ofseveral data intensive stages, namely, resource identification,resource recognition, data collection, data transmission, datamining, and feedback control [212]. Flexibility is critical formanufacturing firms to respond to demand uncertainty andachieve product customization. For example, in automotiveplants, vehicles with multiple styles, models, and options canbe made on the same production line. Similarly, computerswith different configurations are assembled on the same lineas well [206]. Similar observations are found in many othermanufacturing systems, such as appliances, electronics, furni-ture, food, and are usually described by model-based processes[199]. However, replacing a resource or introducing a newproduct variant often requires manual integration work andconsiderable downtime. For this reason, automated systemsfor manufacturing need to adapt increasingly fast to the new[200]. Data is already playing a crucial role in customizedmanufacturing, as advanced systems are needed that analyzethe assembly and use the plethora of data available at theshopfloor to generate highly flexible assembly sequences.In order to increase the requested flexibility and boost thedata availability in the production process, assembly linesare being evolved and are featuring new technological im-provements. Some fundamental data-enabling advancementsfor the modern assembly lined include: Sensor data acquisitionsystems producing large amounts of small volume data [210],(3D) CAD/CAM systems and models producing considerableamounts of large volume data [202], simulation-based sys-tems [220] for rearranging manufacturing facilities targetingmaterial handling and costs minimization producing complexmathematical data [209], digital twins of physical productsproducing assembly orchestration data [216], as well as in-tegrated ICPS producing coupled cyber-physical data [213].In [196], the authors introduce a knowledge-based approachexploiting distributed declarative data and cloud computingand target data and software exchange and reuse, maximizingthe potential to facilitate new business models for industrialsolutions.Real-time data operations for flexible manufacturing arebecoming increasingly popular, are now in the core of theproduction process and are using different kinds of data.Real-time performance assessment of manufacturing systemsby monitoring continuous and discrete variables of differentmachines is based on data extracted from factory machines[218]. Real-time monitoring of the production process is basedon data (features) extraction and selection (for example, high-power disk laser welding in [219], with fifteen features ex-tracted). Real-time production exception diagnosis is based on sensor data streams [207]. Real-time geometrical re-definitionsof products in the assembly line are based on 3D data fromCAD systems and models [215]. The same holds for real-time capturing, structuring and assessing the design rationaleof product design [205]. Real-time coded aperture techniquestargeting the alignment process for industrial machinery pro-ducing high resolution image data [195].Some specialized recent contribution on assembly line im-provements include the following. In [194], the authors arguethat the diversity and uncertainty of data over the dimension,damage degree and remaining life characteristics of usedparts make the remanufacturing process route decision morecomplicated, and they propose a model for finding the optimalremanufacturing route. Due to similar uncertainties of complexmechanical products, the authors of [198] suggest an assemblyquality adaptive control system, in order to improve theproducts’ assembly precision, stability and efficiency. In [197],the authors adopt a visual product architecture representationin combination with a PLM system data to support the devel-opment of a family of products. In [193], the authors introducean efficient automation and control for a particular type ofindustry, the conventional cable manufacturing industry, aconventional stranding plant of which takes up approximately300-400 m of space. Last but not least, taking into accountthat the practice of kitting (to supply the required parts for asingle assembly in pre-set containers) provides an alternativeto the currently dominant practice of continuous supply line-stocking, the authors of [221] analyze the value of model-based kitting for additive manufacturing.Several theoretical frameworks have also been proposed. In-dustrial machines using probabilistic Boolean networks enablethe study of the relationship between machine components,their reliability and function [217]. Manufacturing systemswith batches and duplications can be effectively modeled bytimed event graphs and then studied using algebraic tools[214]. Time-varying properties of industrial processes can alsobe seen as data-driven, autoregressive models and be esti-mated with relevant recursive algorithms [208]. Improvementsof key features of product manufacturing can be realizedvia weighted-coupled network-based quality control methods[203]. Petri nets modeling can augment the performance ofevent driven systems like intelligent part dispatching usingtemporal data [201]. Integrated process planning and systemconfiguration for machining on rotary transfer machines can beeffectively realized through the employment of sophisticatedoptimization tools [211]. Finally, automatic adaptation ofassembly models can be modeled with attributed kinematicgraphs [204].
6) M2M communication:
Industrial M2M communicationrefer to direct communication between industrial networkeddevices using any communications channel, including wiredand wireless. Emerging smart factories are envisioned to beseamlessly integrated with diverse communication technolo-gies. Consequently, production, networking, and communica-tion will become tightly integrated. Cooperation among differ-ent sites of a factory or even different factories will be easilypossible [232]. The research emphasis on this technologicalenabler is put less on the large scale network optimization aspects (which are investigated in the rest of the technologicalenablers) and more on the device to device communicationlinks, channels, transmissions and one hop data exchanges.The exact contributions range from the lower technologicallevel of circuit network model design [224], up to the highertechnological levels of antenna design [243], filtering [247],multiplexing [249], interference management [235] and others.Particular attention has been paid on guaranteeing the QoS ofthe subsequent data delivery over the communication media,through various methods, such as function splitting betweendelay-constrained data delivery and resource allocation [227],redundant communication schemes [245], or precise commu-nication and network modeling [239]. Optical communicationshave also started penetrating the industrial sector, especiallyfor moderate and high data rates with enhanced security (dueto the spatial confinement of optical links) for both short [225]and longer ranges [252], however their full potential remainsto be unlocked, as the cost of optical equipment is still high[254].The M2M Communication configuration has a direct impacton the efficiency of the industrial network data manage-ment, and especially on specific sensitive data-related metrics.Those metrics are fundamental operatives of the I4.0 andare guaranteeing the smooth function of resource-intensiveindustrial applications. Some indicative examples where theimpact of communication scheme is highly beneficial are thefollowing: self-triggered sampling schemes for NCS targetinglow data losses and delays [228], statistical dependencesmanagement in channel gains of industrial WSAN target-ing efficient data routing [229], phase-sensitive sensing andcommunication targeting safety-critical data distribution [256],mmW deployments targeting large number of data hops [242],field-oriented network control decoupling targeting effectivemachine operation [238], and optimized cooperative multipleaccess techniques targeting efficient resource sharing [253].A useful standardized recent data enabling communicationmechanism is a recent extension of IEEE 802.15.4. Severalstudies have highlighted that the IEEE 802.15.4 communi-cation standard presents a number of limitations such aslow reliability, unbounded packet delays and no protectionagainst interference, that prevent its adoption in applicationswith stringent requirements in terms of data reliability andlatency [28]. For this reason, IEEE has released the 802.15.4eamendment that introduces a number of enhancements to theMAC layer of the original standard in order to overcome suchlimitations. Following this release, there is a constant flow ofresearch on improving various aspects of the amendment. Thispart of research includes a great number of works on the M2Mcommunication technological enabler, and more specificallyconcentrated on three of the main 802.15.4e MAC operationmodes, Time Slotted Channel Hopping (TSCH), Deterministicand Synchronous Multi-channel Extension (DSME) and LowLatency Deterministic Network (LLDN) (for more details onthe functions of those modes, the reader can consult [28]).Regarding the TSCH mode, the main research focus has beenrecently placed on synchronization, with some techniquesusing learning and prediction data from neighboring nodes[223], and other techniques using mutual synchronization of TABLE IVS
TANDARDIZED DATA ENABLING COMMUNICATION TECHNOLOGIES . Technology Articles
IEEE 802.15.4e [223], [233], [237], [244], [250]IEEE 802.11(a/n) [230], [234], [236], [240], [241], [251], [255]EtherCAT [226]CAN [222]OPC-UA [231], [257]ISA100.11a [248]WirelessHART [246] distributed nodes [237], as well as on fast network join-ing algorithms [250]. Regarding the DSME mode, improvednetwork formation has been studied in [244]. Regardingthe LLDN mode, significant efforts have been invested intransforming the standard compatible for ultra-low latencyapplications, where the critical data need to be delivered withhigh reliability [233].Another widely used data enabling technology used fordata management in industrial environments is the IEEE802.11 WLAN and its various amendments. The IEEE 802.11standard revealed effective since it is able to provide sat-isfactory performance for several industrial applications inwhich tight requirements in terms of both timeliness andreliability are encountered [230]. Specifically, the possibilityof implementing ad hoc data management schemes as well asinfrastructure configurations, renders it very convenient. Herethe emphasis is put on several important aspects. The firstaspect is seamless redundancy to improve reliability throughreference architectures [255], experimental campaigns [241]and joint interference prevention [251]. The second aspectconcerns soft real-time control applications where the relevantconstraints are met through efficient bandwidth management[236], as well as enhanced communication determinism [240].The third aspect is dynamic rate selection algorithms, wheredata is delivered within the deadlines, while transmission erroris minimized [234].Other data enabling communication technologies include:CAN with jitterless communication via stuff bits preven-tion [222], OPC-UA with enhanced throughput increased viaRESTful architecting [231], [257], EtherCAT with very shortcycle times via priority-driven swapping-based scheduling ofaperiodic real-time data [226], ISA100.11a with increasedreliability via adaptive channel diversity [248], WIrelessHARTfor harsh industrial environments [246]. Table IV displays anoverview of selected references regarding specific communi-cation technologies.
B. Data centric industrial services1) AR / VR:
There have been very few works on augmentedreality (AR) and virtual reality (VR) services. Typically,those services require large volumes of video data whichare processed centrally with high computational overhead. In[258], the authors introduce a context-aware augmented realityassisted maintenance system, in which industrial users can addand arrange various contents spatially, e.g., texts, images andCAD models, and specify the logical relationships between the AR contents and the maintenance contexts. The data inthis system are stored in a context database of the contextmanagement module. A context sensing module acquires rawdata from the users and various physical sensors in theenvironment, and interprets the raw data to obtain low-levelcontexts. The sensor interpreter obtains and interprets datafrom the physical sensors. For example, it processes the rawimages captured by the cameras, and outputs the marker IDand transformation matrix. The data processing is conductedoffline on large volumes of acquired data. In [259], the authorsapply AR technologies for the improvement of occupationalsafety in industrial environments. The application is installedon workers’ mobile devices that are used as the input andoutput of the system. All the necessary data are stored in acentral database that is accessed by the application wheneverrequired. The system is personalized according to skills ofa worker by taking into account his professional trainingand work experience. Depending on that it is determined theamount of data to be displayed to a worker helping evenless skilled workers to perform a task. Therefore, in this casealthough the data presence is localized, the data processing isdistributed.
2) Camera / Vision:
There have been some works whichuse camera and vision technologies for efficient pattern recog-nition, fault estimation and template matching. In [260], theauthors develop a data-driven decoupling feedforward controlscheme with iterative tuning to meet the challenge of thecrosstalk problem in MIMO motion control systems. Thisscheme is data-driven in the sense that, unlike typical model-based approaches of this field, it uses an iterative tuning whichuses the available data to overcome the practical obstacles inobtaining an accurate dynamic model. The authors show thatthrough the beneficial use of data and with only one mea-surement data collection, the decoupling control scheme canreduce the effect of the crosstalk with a decrease of two ordersof magnitude ( − → − ). In [261], the authors presenttwo estimator designs for WSANs in multi-target trackingunder signal transmission faults due to the uncertainties in thesurrounding environmental conditions. In [262], the authorsdescribe a model-based template matching system, which isrobust to undergo rotation and scaling variations. The dataused as input in the system are comprised of image data, and,in fact, the authors test the system with different categories ofimage data, through three diverse datasets: logos and badges,image patches, and PCB components.
3) Prognostics:
Prognostics engineers face various situa-tions regarding collected data from the past, present, or futurebehavior, and have to come up with efficient data-drivensolutions. Generally, the modeling of data-driven prognosticshas to go through necessary steps of learning and testing. First,raw data are collected from machinery and are preprocessed toextract useful features to learn degradation behavior. Second,in the test phase, the learned model is used to predict futurebehavior and to validate model performance. An example ofprognostics operations in industrial environments is systemshealth management, an enabling discipline that uses sensorsto assess the health of systems, diagnoses anomalous behavior,and predicts the remaining useful performance over the life of the asset [268]. In [263], the authors present a new approachfor feature extraction based on vibration data, targeting ac-curate prognostics for machinery health monitoring. The mainbreakthrough of the paper is the mapping of raw vibration datainto monotonic features with early trends, which can be easilypredicted. The data collection and processing is concentratedon central computation entities. The contribution is naturallydata-driven and the authors strive for a good balance betweenmodel accuracy and complexity. Prognostics also present awidespread application in network-based industrial processes,with [264], where combined fault-tolerant and predictive con-trol is introduced and [267], where a weighted linear dynamicsystem for nonlinear dynamic feature extraction is proposed.In those works, the authors try to identify the considerableredundancy and the strong correlations between data as well asto manage the random noises present at data. Other interestingdata-driven industrial prognostics applications include [265],which presents an extended prediction self-adaptive controlleremploying graphical programming of industrial devices forcontrolling fast processes, and [266], which investigates faultprediction of power converters in industrial power conversionsystems.
4) Anomalies Detection:
Considering the aspect of datamanagement, current anomalies detection approaches are ei-ther centralized and complicated or restricted due to strictassumptions, a fact that renders them difficult to apply onpractical large scale networked industrial systems. The ac-commodation of high rates of data capture and total datavolume generated by complex WSANs that typically monitorindustrial systems pose one of the main challenges for onlineanomalies detection. The paper [271] outlines such centralizeddata-driven systems for anomalies detection for ICPS usingseveral use cases from industry. Based on data, these systemsextract most necessary knowledge about the diagnosis task.Another ICPS-enabled work is [273], in which the authorspresent an anomaly detection approach for ICPS based on zonepartitioning. Additionally, in [272], an online two-dimensionalchangepoint detection algorithm for sensor-based anomaliesdetection is proposed. Interestingly enough, in [269], theauthors introduce a distributed general anomaly detectionscheme, which uses graph theory and exploits spatiotempo-ral correlations of physical processes to carry out real-timeanomaly detection for large scale networked industrial sensingsystems. Finally, in [270], a work of different flavor, theauthors display the concept of early problem identification incollaborative engineering with different product data modelingstandards.
5) Fault diagnosis:
Fault detection, isolation and recon-struction methods are essential to improve the reliability,safety of the automatic control systems. In [274], the authorsdevelop a model-based fault location method is developed forintermittent connection problems on controller area networks.In this type of networks time critical data are transmitted,hence, the reliability of the network not only has a directimpact on the system performance but also affects the safetyof the system operations. In [275], the authors introduce acondition monitoring and fault diagnosis scheme of electricmotors for harsh industrial applications. The authors also note that for a real implementation in industry, since the proposedscheme assumes prior knowledge of various data in a motorcurrent spectrum, small additional memory might be requiredto implement the proposed method. Also sufficient bandwidthof data acquisition is required, particularly for high-frequencysignal detection. In [276], the authors discuss some basicproperties of the failure rate of redundant reliability systemsin industrial electronics applications. They note that the theproblem of reliability evaluation of the single components isdata related and is not an easy matter, and this is exactly inview of the scarcity of failure data. In [277], the authors designa fault isolation technique based on the k -nearest neighborrule for industrial processes. A notable data related remarkon this paper is that the technique focuses on the problem ofisolating sensor faults only based on the normal data, withoutany fault information. In [278], a reconstruction-based methodis proposed to monitor nonlinear industrial processes andisolate their fault types. This method includes numerous dataoperations (such as normal data decomposition and faulty datadecomposition), and In the experimental section, monitoringdata of an electro-fused magnesia furnace is used to showits effectiveness. In [279], the authors suggest a componentanalysis algorithm for fault monitoring in industrial processes,and in [280] a threshold-free error detection scheme forWSANs. Various data oriented techniques are used by theauthors, such as exploitation of the information related to thespatial and temporal relationships among sensor data streams,data correlations and mapping of residual data streams.
6) Multi-Agent Systems:
Multi-agent systems have beenpresented as a suitable service to develop modular, flexible,robust, and adaptive large-scale production lines. However, theclassical multi-agent systems are defined by a static hierarchyof data structures, which makes them very difficult to modify[282]. For example, in [283], the authors present a softwareplatform structured around a central data repository, containingengineering data and information from ongoing and completedline design projects. The central data repository is used bysoftware agents that allowed the seamless update and use ofengineering data. Also, in [284], the authors investigate thetracking control problem of networked multi-agent systemscentrally with multiple delays and new characterizations ofimpulses. Many of the recent works focus on the decen-tralization of industrial functions and data distribution overa community of distributed, autonomous, and cooperativeagents. The application of distributed agent data and servicesallows the achievement of important features, namely modu-larity, flexibility, robustness, adaptability, reconfigurability, andresponsiveness [288]. Some recent ones are the following. In[281], the authors develop a multi-agent system for processand quality control in a laundry washing machines factory.They construct an agentification of the factory’s productionline and distribute the various types of data among differentkinds of agents. In [285], the authors model manufacturingmachines as agents, which can collect production data and dis-tributively control the machines. Giving them self-organizationcapability, machines can be reconfigured for different tasksto achieve the highest resource efficiency. Manufacturing pro-cesses are monitored and adjusted by the self-adaptive model when exceptions occur. In [286], the authors propose the mod-eling and synthesis procedures to obtain optimal decentralizedindustrial controllers in state-feedback form for distributedagents. [287], presents a multi-agent method for industrialprocess integration implementing coordination optimizationmechanisms that enable distributed agent data exchanges, byusing cultural algorithms. In [289], the authors introduce non-cooperative agents which make decisions based on the capacityallocation and the data of all other agents, thus creating adecentralized feedback loop.
7) Decision Making:
The integration of ubiquitous sensingcapabilities of IIoT with the industrial infrastructure of I4.0can enable the automation of the decision making processinside and outside the shop-floor. The data collected by IIoTsystems in smart industries can be used to replace manualemployee evaluation systems where there are ample chancesof bias. In [291], the authors develop a large-scale data-driven multitask learning and decision-making system, whichcan quickly coordinate machine actions online for large-scalecustom manufacturing tasks. In [292], the authors present aself-organized system with data based feedback, coordinationand improved decision making ability. In [290], the authorspropose a model for automated performance evaluation ofemployees in a smart industry. The model uses the datacollected by embedded sensors in smart industrial systemto identify various industrial activities of employees. Theidentified activities are then classified as positive, negativeand neutral activities. Here the word “decision” refers to theaction taken in response to the performance of employees. Theproposed model consists of an IIoT network, an informationprocessing system and a central database system. The datacollected by the IIoT network are stored in the database andused by the information processing system to infer the usefulrequested results. Another interesting data enabling entity inthis paper is the data conversion block, which is used toclassify a particular activity into positive, negative or neutraland to calculate the amount of profit or loss corresponding topositive or negative activity respectively. Finally, a decisionmaking block is automatizing the decision making processusing game theoretical tools.
8) Job Scheduling:
Job scheduling has been traditionallyconsidered as a core field in the manufacturing research area.The field spans from the single machine scheduling problemwhich is the simplest type of industrial scheduling problem,to multiple machine scheduling, and even multiple assemblylines scheduling or even inter-factory job scheduling. Exam-ples of single machine scheduling are [293], where nestedpartitioning-based integration of process planning and schedul-ing in flexible manufacturing environment is introduced, [298],where the authors study the single machine scheduling prob-lem with deadlines where the processing times are describedby uncertain variables with known uncertainty distributions,and [295], where the recovery policy of job-shop manufactur-ing systems is evaluated. Also in [296], the authors propose asoftware composition method for automated machines that ex-ploits their mechatronic modularity, and they demonstrate thatdesired behavior of a certain class of machines can be com-posed of behaviors of its mechatronic components, including TABLE VD
ATA - DRIVEN MACHINE LEARNING SERVICES FOR DATA ENABLING TECHNOLOGIES . Data enabling technology Articles Type of service Method used
IIoT / ICPS [302][310] missing QoS values predictionintelligent IIoT traffic classification kernel least mean square algorithmfast-based-correlation feature selectionWSAN [305][308][309] exposition of sensing featurescritical quality variables estimationspatiotemporal feature learning high-accuracy measurementssemisupervised deep learningdeep neural networkNCS [307] cloud virtual machines workload prediction canonical polyadic decompositionIndustrial Robots [311][312] high-accuracy force tracking in robotized tasksfeature learning from raw mechanical data iterative learning with reinforcementdeep neural networkAssembly Line [303][304][313][314] nonlinear process monitoring optimal operational indices selectionlocally weighted learningradial basis function networksrecursive slow feature analysisM2M communication - fully decentralized scheduling and operation control. Multiplemachine job scheduling has been presented in [294], where theauthors address the problem of scheduling multi-robot cellswith residency constraints and multiple part types, in [299],where the authors consider the serial batching scheduling prob-lem in which a group of machines can process multiple jobscontinuously to reduce the processing times of the second andsubsequent jobs, and in [300], where the authors study a two-machine scheduling problem in fuzzy environments. Multipleassembly lined scheduling is presented in [301], where theauthors investigate robust order scheduling problems in thefashion industry by considering the preproduction events andthe uncertainties in the daily production quantity. Inter factoryscheduling is presented in [297], where production planningwith remanufacturing and back-ordering is discussed, in whichthere are multiple factories in a cooperative relationship toproduce new or remanufactured products.
9) Machine Learning:
Machine learning services are bydefinition data-driven and are used on top of the technologicalenablers in order to further enhance industrial applications.An outline of the recent industrial machine learning servicesand the corresponding technical methods used is displayed inTable V. For the IIoT technologies, emphasis has been puton data-driven schemes for predicting the missing QoS valuesfor the IIoT based on kernel least mean square algorithms[302] and on intelligent IIoT traffic classification using searchstrategies for fast-based-correlation feature selection [310].WSANs benefit from the exposition of features for sensing thatprovide high-accuracy measurements for reducing the requiredmanufacturing precision (capacitive displacement sensing in[305]). Machine learning is also beneficial for industrial robotenablers, for example with iterative learning procedures withreinforcement for high-accuracy force tracking in robotizedtasks [311]. Applications in the assembly line focus on processmodeling and include data-based methods for automaticallyselecting optimal operational indices for unit processes in anindustrial plant using measured data (without knowing dynam-ical models of the unit process) [303], data-driven approachesfor nonlinear process monitoring under the framework oflocally weighted learning [304], using radial basis functionnetworks [313], as well as adaptive process monitoring andfault diagnosis through recursive slow feature analysis [314]. Data classification is an active research problem in the in-dustrial data mining and machine learning communities andspreads horizontally over all technological enablers [306].Deep learning, as one of the most important tools of current in-dustrial computational intelligence, achieves high performancein predicting numerous parameters and attributes of industrialapplications. However, it is a nontrivial task to train a deeplearning model efficiently since the deep learning model oftenincludes a great number of parameters. In [307], the authorsintroduce an efficient deep learning model to predict cloudvirtual machines workload for industrial NCS deployments.In [308], the authors employ deep learning of semisupervisedprocess data with a hierarchical extreme learning machine ona soft sensor industrial application. Spatiotemporal featuresfrom sensors can also be learnt through deep neural networks[309]. In [312], the authors propose a deep learning networkto learn features adaptively from raw mechanical data withoutprior knowledge.
10) Big Data Analytics:
The enormous amount of real-timedata is used for the analysis of various industrial applicationshas led to a trend in I4.0 environments pointing to the useof big-data as a relevant element in the development of nextgeneration industrial systems. Big data analytics offer manyopportunities to evaluate data in all layers of the industrialinstallations, for example, to identify preferences from end-users, to better understand technological enablers’ behaviors,or to relate issues derived from a combined and statistical pro-cessing of data. The common trend in many current industrialapplications is to transfer IIoT data from the physical locationswhere they are generated to some global cloud platform, whereknowledge is extracted from raw data and used to supportIIoT applications. Moreover, as [319] notes, several big dataprocesses (such as deep learning) require expensive computa-tional resources including high performance computing unitsand large memory to train a deep computation model witha large number of parameters, limiting its effectiveness andefficiency for industry informatics big data feature learning.Consequently, real-time delay constraints might require thatdata elaboration or storage is performed at the edge, i.e., closeto where it is needed, rather than in remote data centers.However, there are concerns whether this approach will besustainable in the long run. For this reason, decentralized Stationary sensors Mobile sensors Data processing unitsData centerApplicationsDistributed computationCentralized computationContent analysis
Fig. 11. Generic big data framework for industrial edge deployments as it isenvisioned by recent research approaches. generic big data framework for industrial edge deploymentslike the one displayed in Fig. 11, as they is envisioned in recentapproaches, such as [323], [317] and [318], are becomingmore and more common. It is visible that the I4.0 trendspush towards computation decentralization mainly from thestandpoint of data ownership, as well as wireless networkcapacity.Some representative examples of this computation decen-tralization and of maintaining the data at the edge for dis-tributed operations are the following. In [315], the authorsdesign and test a real-time big data gathering algorithm basedon indoor WSANs for risk analysis of industrial operations. In[316], the authors show different approaches that a classicalmanufacturing systems company can take into account whenapplying data mining techniques to address the requirementswhich come with the IIoT technological enabler. In [318], adistributed and parallel big data analytics system for modelingand monitoring large-scale plant-wide processes is introduced.In [320], the authors explore the development of an industrialbig data implementation able to improve computing perfor-mance by splitting the analytic into different segments thatmay be processed by the engine in parallel using a hierarchicalmodel. Of course, there are also hybrid big data approacheswhich employ two kinds of computation and data communi-cation: both localized real-time processing and global offlinecomputations. In [317], a manufacturing big data solution foractive preventive maintenance in manufacturing environmentsis implemented. Another hybrid approach is [321] whichintroduces a concentric computing model paradigm composed
TABLE VIT
YPES OF COMPUTATION FOR BIG DATA ANALYTICS . Computation anddata analytics Articles
Concentrated(cloud / offline) [319], [322], [324]Distributed(edge / real-time) [315], [316], [318], [320]Hybrid [317], [321], [323] of sensing systems, outer and inner gateway processors, andcentral processors for the deployment of big data analyticsapplications in IIoT. In [323], the authors analyze the relation-ship between the data processing and the energy consumptionthrough investigating the content correlation of the captureddata. Traditional centralized approaches are presented in [322],where the authors develop a big data toolbox for manufac-turing prediction tasks to bridge the gap between machinelearning research and concrete industrial requirements, andin [324], where the authors use big data services in order todesign a new method for product design, manufacturing, andservice driven by digital twin. Table VI displays the extent ofcentrality that the various recent approaches have adopted, interms of computation for big data analytics.
11) Ontologies / Semantics:
In industrial automation, on-tology services encompass a representation, formal naming,and definition of the categories, properties, and relations be-tween the data and entities that substantiate various industrialprocesses. This will lead to the further automation of manytasks in the life cycle of the industrial systems from design tocommissioning and operation [342]. Those services frequentlyrely on synergies of industrial standards, such as IEC 61850[336] and IEC 61499 [339], which are used to representspecifications and resulting software models. Due to the factthat semantic data modeling usually deals with data irregu-larity and diversity, sophisticated dynamic modeling methodshave been derived [338]. With regards to IIoT and ICPS,OPC-UA and semantic web technologies are able to achieveintegration at various levels [345]. UML-based approaches canfully automate the generation process of the IIoT-compliantlayer that is required for the cyber-physical components tobe effectively integrated in the shop-floor [333]. In order toachieve rapid response to changes from both high-level controlsystems and plant environment, self-manageable ontologicalagents can improve flexibility and interoperability [337] andautomate the process engineering using a knowledge-basedassistance system [341]. IIoT gateways have already beenintegrated with dynamic and flexible rule-based control strate-gies [344]. Model-driven NCS enable increased usability [325]and model checking [326]. In the assembly line, knowledgebased ontology services can assist complementary contentcustomization [327], mechanical design knowledge [328],and semantic web service composition [329]. Recognition,semantic annotation and calculating the spatial relationshipsof a factory’s digital facilities [330], as well as the modelbased synthesis of its automation functionalities [331] areother emerging topics of interest. Ontology services also come TABLE VIID
ATA SECURITY SERVICES FOR DATA ENABLING TECHNOLOGIES . Data enabling technology Articles Type of security provisioning
IIoT / ICPS [356][358][359][360][361] covert attack for service degradationquantification of the impact of cyberattacks on the physical partlegal aspectsblockchain-based remote user authentication with fine-grained access controlcertificateless searchable public key encryption with multiple keywordsWSAN [354] intercept behavior in the presence of an eavesdropping attackerNCS [353][355][357] energy efficient intrusion detectionlightweight secure authentication mechanism for broadcast mode communicationdynamic cybersecurity risk assessmentIndustrial Robots -Assembly Line -M2M communication [363][362] application-layer traffic filteringsensor-cloud trust-based communication handy in cloud manufacturing and take advantage of semanticlinks to enable automated integrating and distributed updatingin resource service clouds [335]. Ontology services can alsosupport the development of global production network systems[332] and business integration [343] in a more general sense,as well as CAD assembly model retrieval (using multi-sourcesemantics information and weighted bipartite graph [340]) andvisual exploration systems [334].
12) Human-in-the-loop:
Human-in-the-loop services, willbe an indispensable component of most I4.0 approaches andapplications related to the large scale ICPS and assembly linenetworked environments. This is because large and complexindustrial environments necessitate advanced planning andscheduling, careful coordination, efficient communication andreliable activity monitoring, ingredients essential for produc-tivity and safety purposes. A notable relevant area of interest tothe researchers recently is human tracking and localization inthe industrial facilities. There is a diverse variety of approachesin this field, in terms of generated and used volumes of data.In [346], the authors propose an approach that leverages theinertial sensors embedded in smartphones, uses WiFi finger-prints based on the angle-of-arrival and exploits the ubiquitouspresence of diverse data to assist in human localization, thusutilizing data of small volumes. Similarly, in [347], the authorspropose a real-time system for human body motion sensingwith special focus on joint body localization and fall detection.The proposed system continuously monitors and processesambient data propagated by industry-compliant radio devicesthrough supporting M2M communication functions. In [349],the authors propose a positioning system for tracking people inhighly dynamic industrial environments, such as constructionsites. The proposed system leverages the existing CCTVcamera infrastructure installed in the industrial environment,along with radio and inertial sensors within each worker’ssmartphone to accurately track multiple people. Consequently,in this case the data’s volume varies according to the datageneration source. Even larger volumes of data are used in[351], where the authors employ video analytics in order toimplement motion detection framework through motion blobsand successfully provide a features-based person trackingsystem. Other human-in-the-loop concepts are mobile appsdeveloped to support the customer integration in the product design phase and subsequently the design of the manufactur-ing network [348], cross-disciplinary mobile crowdsensing ofpervasive sensor data applied in industrial processes [350], aswell as automated methodologies for worker path generationand safety assessment [352].
13) Security:
Security aspects in factory automation andindustrial operations have become a hot topic in the lastyears since monitoring and control tasks are more and morecomplex. Also, ICPS are vulnerable to external attacks dueto the tight integration of cyber and physical parts. In fact,security incidents such as targeted distributed denial of service(DDoS) attacks on power grids and hacking of factory NCSare on the increase [359]. Data management in such systemsis crucial, as the increased scalability of the deployments canfrustrate effective management of security risks, partly dueto the complexity of managing the large volumes of dataand risks manifesting across interdependent systems. Securityhas been recently studied across most of the technologicalenablers presented in this article. Table VII displays theservices that have been presented for security provisioningacross the different technologies. In [356], a covert attack forservice degradation of ICPS is proposed, which is plannedbased on the intelligence gathered by another system identifi-cation attack. In [358], a risk assessment method is presentedtargeting the quantification of the impact of cyberattackson the physical part of ICPS. The proposed method helpscarry out appropriate attack mitigation measures. In [360], theauthors establish a secure remote user authentication with fine-grained access control for IIoT, by proposing a blockchain-based framework. The proposed framework leverages theunderpinning characteristics of blockchain as well as severalcryptographic materials to realize a decentralized, privacy-preserving solution. In [361], the authors design a securechannel-free certificateless searchable public key encryptionwith multiple keywords scheme for IIoT. In [354], the authorsstudy the intercept behavior of an industrial WSAN consistingof a sink node and multiple sensors in the presence of aneavesdropping attacker, where the sensors wirelessly transmittheir sensed data. In [353], the authors present an energyefficient intrusion detection and mitigation system for NCSsecurity. The system is data oriented in the sense that itemploys data-based selective encryption to reduce energy consumption, and to detect when an attack starts and ends. In[355], the authors present a lightweight secure authenticationmechanism for broadcast mode communication in NCS. In[357], a fuzzy probability bayesian network approach fordynamic cybersecurity risk assessment in NCS is proposed. In[363], the authors present a performance model for industrialM2M communication, able to perform advanced application-layer filtering of traffic generated by protocols widely usedin industrial deployments (Modbus/TCP). In [362], the au-thors investigate trust-based communication for industrial de-ployments, devoting attention to sensor-cloud communication.They propose three types of trust-based M2M communicationmechanisms for sensor-cloud. Furthermore, with numericalresults, they show that trust-based communication can greatlyenhance the performance of sensor-cloud.
14) Energy Management:
Energy management for the IIoTand WSANs has naturally received significant attention, as inmany cases the devices operate on limited battery supplies(Table VIII). On the IIoT part, there have been energy effi-cient improvements on QoS-aware services composition [372](similarly for the ICPS [386]), robust authentication protocols[392], routing and data collection [393], [394], as well asresource allocation and utilization [395] (similarly for theICPS [398]). On the backbone of the IIoT networks, in thecases where Ethernet is used as an enabler, energy efficiencyhas also been a timely topic [375]. Specifically, in [366], theauthors investigate the IEEE 802.3az amendment, known asEnergy Efficient Ethernet (EEE) and address its applicationto Real-Time Ethernet (RTE) networks in factory automation.Additionally, in [367], the same authors expose some dataservice aspects of the EEE/RTE interplay.On the WSAN part energy efficiency is focused on spe-cific data intensive operations. Industrial low power WSANprotocols are one of the key enablers of that revolutionbut still energy consumption is what is limiting ubiquitousdeployments of perpetual and unattended devices [370]. Real-time usage data as well as historical data can help identifywhether various WSAN components are functioning properly[379]. Routing and data collection is traditionally assistedenergetically, either through joint data transmission and wire-less charging [369], or through adjustable data sampling rates[396] and distributed and collaborative sleep scheduling [377].Other energy efficient approaches include integrity check inthe network [373], node localization [376], data loss mini-mization [378], and connected target coverage [381]. Energyefficient approaches for WSANs of particular interest withrespect to the data management mechanisms employed arethe following: In [371], the authors apply compressed sensingin order to break the redundant data collection (and thus savesignificant amounts of energy), by differentiating the availablesensed data in principal and redundant, through an onlinelearning component and a local control component. In [374],the authors derive both global and local data storing in theWSAN, and expose the inherent difficulties of each case (dataimportance degrees definition and data stream reading ability).Energy optimization of industrial robotic cells and assemblylines is also essential for sustainable production in the longterm. A holistic approach that considers a robotic cell as a
TABLE VIIIE
NERGY MANAGEMENT FOR DATA ENABLING TECHNOLOGIES . Data enabling technology Articles on energy management
IIoT / ICPS [372], [386], [392], [393],[366], [367], [375], [394], [395], [398]WSAN [369]–[371], [377], [396],[373], [374], [376], [378], [379], [381]NCS -Industrial Robots [384]Assembly Line [62], [364], [365], [383], [385], [387],[6], [388]–[391], [399]M2M Communication [368], [397] whole toward minimizing energy consumption is proposed in[384]. Dynamic low-power reconfiguration [364] and machineenergy consumption minimization [365] are key objectives ofnovel assembly lines. In [62], the authors discuss how dynamicenergy management in manufacturing systems can not onlysolve the current technical issues in manufacturing, but canalso aid in the integration of additional energy equipmentinto energy systems. The significantly important role of datain this process is demonstrated in [383] where the collecteddata are shown to improve energy consumption awarenessand allows the manufacturing energy management systems tomake further analysis and to identify where to take actionsin the manufacturing process in order to reduce the energyconsumption. There have been several energy management andenergy consumption optimization methods for the assemblyline in the recent literature, with the most notable focusingon production control [385], forecasting models with neuralnetworks [387], mobile service composition [388], real-timedemand bidding [389], ontological modeling [390], processparameter modeling [391], machine energy consumption pro-filing [6], and concurrent energy data collection [399].Methodologies and a models which reliably dimensionenergy scavenger properties to M2M communication require-ments and network needs, allowing industries to optimize theadoption of that technologies while keeping technical risks low[368]. MAC layer power management schemes which achievesthe user specified reliability with minimal power consumptionat the node are also of interest to the M2M communicationcommunity [397]. Interestingly enough, there no significantcontributions on energy management issues have been foundfor the data enabling technology of NCS.
15) Cloud:
Cloud manufacturing has lately gained a fairshare of attention from the automation and manufacturingcommunities. Cloud manufacturing transforms manufacturingresources, capabilities and data into manufacturing services,which can be managed and operated in an intelligent andunified way to enable the full sharing and circulating of man-ufacturing resources and manufacturing capabilities. Cloudservices in the supply chain can greatly reduce time andcosts incurred in deploying automation systems, which arequite complex and require large human effort to build [410].Cloud manufacturing can be divided into two categories. Thefirst category concerns deploying manufacturing software onlocal or global clouds, i.e., a “manufacturing version” ofcloud computing. The second category has a broader scope, TABLE IXT
YPES OF DATA SOURCES AND CLOUD LOCALITY IN CLOUDMANUFACTURING . Data enablingtechnology Articles Data source Cloud
Assembly LineIndustrial Robots [400][404][408][409] manufacturing resources globalAssembly Line [402] manufacturing servicesAssembly Line [403] shared memoriesWSAN [401] mobile network nodesNCS [406] network servicesNCS [405] virtual resources hybridIIoT [407] network devices local cutting across production, management, design and engineer-ing abilities in a manufacturing business. Unlike with classiccomputing and data storage, manufacturing involves physicalequipment, monitors, materials and so on. In this kind ofcloud manufacturing, both material and non-material facilitiesare implemented on the cloud, in order to support the wholesupply chain. The great majority of recent works can beclassified in the first category. Cloud manufacturing solutionscan be categorized according to the locality of the cloud. In thevast majority of the recent literature the cloud infrastructureis centrally placed, with large public clouds delivering datausually over the internet. In Table IX, the types of data sourcesand cloud locality in cloud manufacturing are displayed.As shown in the table, a large portion of works employglobal clouds. In [400], the authors target manufacturingresource composition and propose an approach that can bettercope with the temporal relationship between the resourceservices in a business process. In [404], the authors designa cloud resource sharing based on the Gale-Shapley algorithmand analyze it in the context of fluctuating resource supplyand demand. In [408], the authors present an agent-adapter-based method of for manufacturing clouds to enable manu-facturing with various physically connected machines fromgeographically distributed locations over the Internet. In [409],the authors suggest a multi-granularity resource virtualizationand sharing method for cloud manufacturing. In [402], theauthors introduce service clustering network-based servicecomposition. In this approach, services are first clusteredinto abstract services, and then a clustering network of theabstract services is established. In [403], the authors designan effective load-adjusted allocation algorithm for enhancingmemory reusability and improving the performance of serversby balancing their workloads. In [401], the authors considerindustrial WSAN with mobile nodes and propose a fixed-path mobile node handover strategy, assisted by cloud servicesand an ants-colony algorithm. In [406], the authors proposea cloud-based decision support system for self-healing indistributed automation systems using fault tree analysis. Somefewer recent works employ hybrid or local clouds. In [405],the authors study the problem of how to maximize the profitof a local (private) cloud in architectures of a combinationof local and global (hybrid) clouds while guaranteeing theservice delay bound of delay-tolerant tasks. In [407], the authors suggest an embedded cloud database service methodfor distributed IIoT monitoring.VI. O
PEN R ESEARCH C HALLENGES
In this section, we identify some open research challengeson data management in industrial networked environments andtheir inherent tradeoffs. Subsequently, we focus our attentionon a wide variety of thematic topics pertaining to the re-quirements of data management, as presented in the previoussections. These notes provide crisp insights for the design offuture data management applications.
A. Energy efficient data delivery with small delays
Ensuring energy efficient, low-latency data delivery in in-dustrial networked environments is of capital importance andis currently receiving more and more attention in academiaand industry. However, in current industrial configurations, thecomputation of the data exchange and distribution schedules isquite primitive and highly centralized. Usually, the generateddata are transferred to a central network controller usingwireless or wired links. The controller analyzes the receivedinformation and, if needed, reconfigures the network paths andthe data forwarding mechanisms, and changes the behavior ofthe physical environment through actuator devices. Traditionaldata distribution schemes are usually implemented over rele-vant industrial protocols and standards, like WirelessHART,802.15.4e and 6TiSCH. Those entirely centralized and offlinecomputations regarding data distribution scheduling, can be-come inefficient in terms of end to end latency. Additionally,in industrial environments, the topology and connectivity ofthe network may vary due to link and sensor-node failures.Also, very dynamic conditions, which make communicationperformance much different from when the central schedulewas computed, possibly causing sub-optimal performance,may result in not guaranteeing energy requirements. Thesedynamic network topologies may cause a portion of industrialnodes to malfunction. With the increasing number of involvedbattery-powered devices, industrial networks may consumesubstantial amounts of energy; more than needed if local,distributed computations were used. In order to address thoseemerging challenges of the I4.0, novel data management layershave to be engineered over the device and networking planesof the industrial deployments. Those layers have to operateindependently from and to complement the routing process,targeting at distributing the data in the networks in a decen-tralized manner, while at the same time respecting the strictI4.0 requirements. In fact, not all data need to be transferredto central network controllers prior to delivery to the dataconsumers (as traditional industrial routing approaches usuallyimpose); in fact, data can be also stored managed locally atselected data cache nodes (Fig. 12), exploiting, when needed,additional levels of information.
B. Data distribution in local and mobile clouds
As shown in Table IX the most common current approachfor collecting and processing large volumes of data for cloud Data cache nodeRegular nodeIndustrial nodeRole assignment Data distributionCentral network controllerData Management LayerNetworking Layer
Fig. 12. Conceptual design of a data management layer over an industrialnetwork. manufacturing purposes is based on the assumption that somenetwork infrastructure is able to support the collection anddelivery of all these data toward the cloud, which is intendedto be the back-end aimed at processing and getting value fromsuch data. In general IIoT/ICPS environments, this backboneis a wideband cellular network such as LTE. In the caseof manufacturing environments this may also be the case,or more localized wideband infrastructures such as WiFimay be used. In any case, an approach relying exclusivelyon global cloud providers to provide holistic industrial dataservices has limitations from two main standpoints. On the onehand, wideband wireless networks may not provide sufficientbandwidth so support the data traffic demand. On the otherhand, relying only on global clouds deployed may makemanufacturing stakeholders to loose control on their data, asdata will be transferred to data centers without any controlof the data owner. In addition, meeting the manufacturingstakeholders requirements in terms of storage and computationcapacity may have a significant impact on the cost incurredby the stakeholders for ICT services, which, if reduced, couldbe more profitably invested in the core production process.In order to overcome these issues there is a need of aparadigm shift in the way the gathered data is managed andprocessed. To this end, the employment of local and mobilecloud technologies as a way to implement a multi-layer cloudinfrastructure would be necessary (Fig. 13). This will enablethe exploitation of not only global cloud services, but also localresources available at the stationary and mobile devices ofthe industrial deployments. In such environment, a number ofmobile devices (e.g. the devices of various operators workingat the manufacturing premises) are available, and typicallytheir computation and storage resources are underutilized.Instead of relying exclusively on storage and computation
Field level Industrial raw dataLocal and mobile clouds level Aggregate dataGlobal cloud level Owned by the industrial stakeholder Owned by the global cloud provider
Fig. 13. Conceptual design of a multi-layer cloud platform. services provided by a global cloud provider, the storage andthe computation tasks can be distributed among those localdevices, that will therefore form a local (and in some casesmobile) cloud. In this paradigm, global cloud services can beused only when (i) global information is needed in order tobetter analyze the status of the production process, or (ii) localresources are saturated and additional capacity is needed. Forexample, storage available at local devices would be enoughonly for storing information about parts produced in a limitedtime window in the past. Older data may be stored on a globalcloud storage service, possibly in an encrypted form. However,data related to most recently produced parts would still beavailable locally, and could be accessed without transferringback and forth them between local devices and global clouddata centers. The resulting solution will be a multi-layer cloudplatform, whereby global resources and local resources will beused elastically and in a synergic way, depending on the needof the virtual metrology service.
C. Distributed, real-time data security for industrial robotsand assembly line
As shown in Table VII, there is a lot of work alreadyimplemented in terms of data security for IIoT/ICPS, WSANs,NCS and M2M Communication. However, the absence ofsecurity mechanisms for the technological enablers of theassembly line and the industrial robots is notable. Morethan that, the decentralization of the production process,the integration with IIoT technologies (the nature of whichmakes them vulnerable) and the introduction of open andubiquitous data, leaves the assembly lines and robots furtherexposed to external threats. To date, security has not been aconcern for the (in many cases legacy) assembly lines andindustrial robots. Yet, practitioners have recognized that the open and uncontrollable nature of the M2M communicationenabler opens these systems to a variety of possible securitythreats and vulnerabilities. Security solutions will also needto be operated in a distributed manner, because centralizedsolutions require transmitting data to the central controller,which may result in data loss and delay to the threat detectiondecisions, particularly in large-scale deployments. In contrast,distributed solutions are much more agile and robust to datatransmission failures and, more importantly, scale to largersizes. For example, industrial anomaly detection for maliciousattacks (e.g., false data injection) can be performed either at thecentral controller or at local distributed devices [269]. Finally,following the same example, since real-time information iscritical and even a single abnormal security behavior maylead to a catastrophic cascade of failures throughout the wholesystem, abnormalities should be detected as early as possibleto minimize the possibility of potential damage. To achievethis, real-time data security solutions will be able to provideonline threat detection is needed. Those solutions should beable to identify the anomaly condition of each observation, assoon as the local data observations are collected. D. Convergence between industrial / automation / manufac-turing and communication / networking / computation
NCS currently provide deterministic services for the as-sembly line and the industrial robots, while the IIoT and theWSANs provide best effort services for the entire automationpyramid. Also, as it was demonstrated in Table III, the recentarchitectural trends for assembly line and industrial robotinstallments are focusing on centralized data management,while the trends for IIoT and WSANs are pushing towardsdecentralization, mostly due to the emerging data ubiquity. Ithas already been argued that a convergence should occur, andthat future converged industrial deployments should supportboth best effort and deterministic services, with very lowlatency and jitter [78]. This convergence is motivated evenmore and will be further extended with the pervasivenessand the variety of different data sources in the shop-floor.Consequently, industrial automation providers face a challengeand can significantly benefit from communication/networkingtechnologies and services. If they are not able to find powerfuland flexible computing services that would enable them tostore and process “as required” the manufacturing informationthey have generated, they will never be able to leverage onfaster and more complete control of the production processin the digital domain to gain a competitive advantage. Ifthey remain to perform the analysis as they currently haveto perform, i.e. on the physical domain, they will continuesuffering a negative impact on production yield and costs.VII. C
ONCLUSIONS
In this survey article we reviewed the recent literature(2015-2018) on data management as it applies to networked in-dustrial environments. Of particular interest to our review havebeen the data enabling technologies and the data centric ser-vices that both the Communications/Networking/Computationfield and the Industrial/Manufacturing/Automation field are providing, in order to boost the production performance andaddress the emerging I4.0 requirements. We focused the surveyat first on recent practical use cases and emerging architecturaltrends, where we made a note on the convergence that shouldoccur between the two scientific fields, so as to enable an effi-cient future data management approach. Then, we performedan exhaustive survey on the most relevant and acclaimedresearch journals and came up with a taxonomy of the recentworks in technologies and services. Finally, after this holisticresearch, we identified several interesting open challenges forthe future; energy efficient data delivery with small delays,data distribution in local and mobile clouds, distributed, real-time data security for industrial robots and assembly line, andconvergence between the two main scientific fields.R
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