A Survey on Emerging Trends, Research Challenges And

AIS (iosart2x 2017/02/20 v1.1.2) ais432.tex 2017/03/09 12:39 [research-article] p. 1/12 PRETITLE: Research Article

Journal of Ambient Intelligence and Smart Environments 00 (20xx) 1–12 1 DOI 10.3233/AIS-170432 IOS Press 1 52 2 53 3 54 4 The intelligent industry of the future: 55 5 56 6 A survey on emerging trends, research 57 7 58 8 59 9 challenges and opportunities in Industry 4.0 60 10 61 11 62 a,* b 12 Davy Preuveneers and Elisabeth Ilie-Zudor 63 a 13 imec-DistriNet, Department of Computer Science, KU Leuven, Leuven, Belgium 64 b 14 MTA SZTAKI, Hungarian Academy of Sciences, Budapest, Hungary 65 15 66 16 67 17 68 18 69 Abstract. Strongly rooted in the Internet of Things and Cyber-Physical Systems-enabled manufacturing, disruptive paradigms 19 70 like the Factory of the Future and Industry 4.0 envision knowledge-intensive industrial intelligent environments where smart 20 71 personalized products are created through smart processes and procedures. The 4th industrial revolution will be based on Cyber- 21 Physical Systems that will monitor, analyze and automate business processes, transforming production and logistic processes 72 22 into smart factory environments where big data capabilities, cloud services and smart predictive decision support tools are used 73 23 to increase productivity and efficiency. This survey provides insights into the latest developments in these domains, and identifies 74 24 relevant research challenges and opportunities to shape the future of intelligent manufacturing environments. 75 25 76 26 Keywords: Factory of the future, smart manufacturing, trends, challenges, survey 77 27 78 28 79 29 80 30 1. Introduction homes [7,51], offices and work places [1,3], smart 81 31 cities [13] to applications for assisted living [45] and 82 32 With Intelligent Environments [5], we typically en- enhanced healthcare management [16], etc. – to add 83 33 vision algorithms and software embedded in every- value to their offerings for intelligent environments. 84 34 day objects that collectively aim to make our sur- These technological advances and innovations are fur- 85 35 roundings smart and support us with our daily activ- ther accelerated by new emerging trends, such as the 86 36 ities by accommodating us to the given situation at Internet of Things (IoT) [4,18,49] and Cyber-Physical 87 37 hand. Indeed, technological breakthroughs in minia- Systems (CPS) [28,46]. These computing paradigms 88 38 turization and wireless communication have enabled envision a future in 2020 with an estimated 50 bil- 89 39 sensors, mobile devices and applications to communi- lion devices around the globe connected to the Internet. 90 40 cate with one another and be continuously in ours and Information and intelligent services will be invisibly 91 41 each others’ interaction range. Sophisticated context- embedded in every environment around us, and large 92 42 aware applications and services have been built on top amounts of data will circulate in order to create smart 93 43 of these wireless sensors and actuators [34] to moni- and proactive environments that will significantly en- 94 44 tor their users’ presence and adapt autonomously to an hance both the work and leisure experiences of people. 95 45 always evolving context, while remaining sensitive to A similar digital transformation is taking place in 96 46 changing needs and preferences. As a result, the hard- the 4th Generation Industrial Revolution (Industry 4.0) 97 47 ware and software trends of the last decade have en- [29,30]. A brief history of the industrial revolutions 98 48 abled various vertical domains – ranging from smart will help us understand how the industry has evolved 99 49 over the past few centuries and will advance into next 100 50 101 *Corresponding author. E-mail: generation intelligent environments. Mechanical pro- 51 [email protected]. duction with water power and the steam engine gave 102

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22 Fig. 1. The four industrial revolutions leading to the smart Factory of the Future and Cyber-Physical Production Systems. 73 23 74 24 rise to the 1st industrial revolution in the 18th cen- tervention. The digital transformation will enhance the 75 25 tury (see Fig. 1). Later on, electrically powered mass transparency of the production process, even across the 76 26 production based on division of labor characterized organizational boundaries of the manufacturing enter- 77 27 the 2nd revolution at the start of the 20th century, prise. As such, similar to the classical intelligent en- 78 28 whereas the 3rd was marked with the introduction of vironments (i.e. the smart homes, smart offices and 79 29 the first programmable logical controller only a few smart cities), there is a trend of transforming produc- 80 30 decades ago that enabled IT-based manufacturing au- tion and logistic processes into smart factory environ- 81 31 tomation. The 4th industrial revolution will build upon ments where big data capabilities and smart predictive 82 32 the emerging Internet of Things and Cyber-Physical decision support tools are used to increase productivity 83 33 Systems paradigms to monitor, analyze and automate and efficiency [21]. 84 34 business processes at large. These technological rev- This survey reviews relevant system- and user- 85 35 olutions will transform production and logistic pro- oriented research tracks of intelligent environments, 86 36 cesses into smart factory environments that will in- and discusses non-trivial challenges specifically re- 87 37 crease productivity and efficiency. lated to the design, the implementation and the evalu- 88 38 89 Indeed, Industry 4.0 and the related Factory of the ation of smart industry applications: 39 Future (FoF) [11,24] paradigm envision a future of cre- 90 40 ating smart products through smart processes and pro- 1. Transparent networked production with indus- 91 41 cedures, strongly rooted in the Internet of Things and trial wireless networks. 92 42 Services and Cyber-Physical Systems-enabled manu- 2. Enhanced knowledge management with context- 93 43 facturing [36], with applications in the area of energy, aware industrial systems. 94 44 logistics, sustainable mobility, etc. The widespread 3. Data-driven planning and optimization with Big 95 45 adoption of Cyber-Physical Systems and Big Data an- Data analytics in the cloud. 96 46 alytics technologies by manufacturing companies will 4. Creating insights with machine learning and data 97 47 lead to the 4th Industrial Revolution where Cyber- mining for Industry 4.0 applications. 98 48 Physical Production Systems (CPPS) [35] are blurring 5. Human computer interaction aspects for different 99 49 the boundaries between the real world and the virtual types of stakeholders. 100 50 world. Smart products will plan, control and optimize 6. Security and privacy threats as well as compli- 101 51 their own production process with minimal human in- ance regulations for personalized manufacturing. 102 AIS (iosart2x 2017/02/20 v1.1.2) ais432.tex 2017/03/09 12:39 [research-article] p. 3/12

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1 52 2 53 3 54 4 55 5 56 6 57 7 58 8 59 9 60 10 61 11 62 12 Fig. 2. Intelligent industrial IoT networks linking sensors, mobile devices and cloud services. 63 13 64 14 The survey highlights related work and contribu- 65 15 tions on recent developments and experiments with 66 16 such applications in simulated smart environments as 67 17 well as successful deployments in the real world. 68 18 Section 2 highlights ongoing research in wireless 69 19 sensor networks that provide the foundation for the In- 70 20 dustrial Internet of Things (IIoT). The application of 71 21 context-awareness in this area is the subject of Sec- 72 22 tion 3. Section 4 discusses the importance of enabling 73 23 technologies of cloud computing and big data, whereas 74 24 Section 5 highlights how machine learning and data 75 25 mining can help to make factories smarter. The role of 76 26 the user within such an ecosystem is further explored 77 27 in Section 6, while security and privacy concerns are 78 28 discussed in Section 7. In each of these sections, we 79 29 will discuss how the techniques and best practices have 80 30 been applied in the domain of Industry 4.0 and the Fac- 81 31 tory of the Future. We end this survey with some criti- 82 32 cal reflections and concluding thoughts in Section 8. 83 33 84 34 Fig. 3. Collaborative networked production systems and services in 85 a Business Process Model and Notation (BPMN) diagram. 35 2. End-to-end production transparency with 86 36 industrial wireless networks 87 37 sor networks is that they are much more flexible and 88 38 Industrial companies are shifting from traditional cost-effective to install for temporary and incremen- 89 39 wired infrastructures towards wireless sensor networks tal collection of additional data points. This enables a 90 40 (see in Fig. 2) to continuously monitor the perfor- swifter end-to-end integration into existing industrial 91 41 mance of their plants, as the former are often too com- production networks that may consist of IoT networks, 92 42 plex to expand and too rigid to quickly adapt to evolv- mobile applications and cloud services. 93 43 ing production market dynamics. Wireless sensor tech- Li et al. [31] review how wireless sensor networks 94 44 nology offers clear advantages to monitor production (WSNs) have played a pivotal role to build industrial 95 45 assets and the corresponding networked manufactur- wireless networks (IWNs) for Industry 4.0 applica- 96 46 ing or logistics business processes (a typical orchestra- tions. While WSNs and IWNs share many similari- 97 47 tion of such processes is depicted in Business Process ties and features, the authors argue that the latter im- 98 48 Model and Notation (BPMN) in Fig. 3) in order to gain pose new constraints and requirements for realizing ef- 99 49 real-time insights in how to boost productivity, opti- ficient industrial intelligent environments. First of all, 100 50 mize resource efficiency, reduce interruptions, or min- when monitoring and controlling industry or produc- 101 51 imize down time. The intrinsic value of wireless sen- tion systems in real-time, having a low network latency 102 AIS (iosart2x 2017/02/20 v1.1.2) ais432.tex 2017/03/09 12:39 [research-article] p. 4/12

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1 is a key concern, which is often realized at the expense erogeneity of IoT networks as non-trivial challenges, 52 2 of a higher energy consumption. This is in contrast to as well as a commonly accepted service description 53 3 the traditional wireless sensor networks as found in the language for IoT services to ensure compatibility and 54 4 smart home and office environments where a higher la- interoperability, including integration with legacy sys- 55 5 tency is usually not a concern, but where maximizing tems. 56 6 the battery life-time is usually the goal for increased Additional insights, trends and research challenges 57 7 user convenience. Second, in production and manu- in wireless communication, industrial protocols, pro- 58 8 facturing environments, there are many more mobile cess automation, monitoring and control applications 59 9 components that need to be monitored, such as mobile and industrial distributed environments are offered in 60 10 devices, robots, automated guided vehicles (AGVs), other surveying articles [10,15,19,20,47]. 61 11 which results in wireless networks with many more 62 12 mobile sensor nodes, compared to the more station- 63 13 ary networks in more classic intelligent environments. 3. Information management in context-aware 64 14 Third, the industrial environment in which the sensors industrial systems 65 15 are deployed may also be more challenging, due to 66 16 interference of other obstacles, dust, vibration, higher Context-aware behavior is instrumental in industrial 67 17 temperatures and humidity, etc. These continuously production environments to build smart manufactur- 68 18 changing circumstances may influence the reliability ing systems and applications on top of industrial wire- 69 19 and efficiency of communication in production net- less networks. Such systems mainly focus on track- 70 20 works. Last but not least, due to the many tasks of sen- ing and tracing, zero-defect manufacturing and proac- 71 21 sor nodes in an industrial wireless network, they come tive maintenance. As confirmed in the final report of 72 22 with a higher capacity for data storage and processing the Industrie 4.0 Working Group [23] on “Recommen- 73 23 compared to their consumer-oriented equivalents. dations for implementing the strategic initiative IN- 74 24 Similar research on the wireless challenges in DUSTRIE 4.0”, smart products will be uniquely iden- 75 25 Industry 4.0 was conducted by Varghese et al. [52], tifiable and may be located at all times. Furthermore, 76 26 raising deterministic low network latencies, increased it will be possible in the future to incorporate indi- 77 27 battery longevity, scalable connectivity, reliable vidual customer-specific features into the design. As 78 28 machine-to-machine communication, high data rates, Fig. 4 depicts, typical end-to-end Industry 4.0 integra- 79 29 and seamless connectivity as important requirements. tion scenarios involve many people with different roles 80 30 In particular, they investigate to what extent the 5G and information needs. Effective and reliable data col- 81 31 communication standard can address these require- lection is key to implement context-adaptive decision 82 32 ments. They conclude that a single wireless standard, support systems that continuously monitor and control 83 33 such as WiFi, will not be able to address all the require- manufacturing and production processes with humans 84 34 ments in the Industry 4.0 era, and that a combination in the loop. Context-awareness also helps to create a 85 35 of various technologies will be necessary to realize an holistic view of assets, people and inventory, and can 86 36 efficient industrial wireless network. reduce human errors of production line engineers and 87 37 Xu et al. [57] conducted an extensive literature operators by reducing their cognitive load. 88 38 review on the growing interest in using IoT tech- With billions of sensors connected to the Internet, 89 39 nologies in various industries, highlighting compara- context-awareness is an important research area within 90 40 ble important challenges and opportunities. They offer the IoT. Perera et al. [40] surveyed a broad range 91 41 an in-depth review of the applicability of service- of techniques, methods, models, functionalities, sys- 92 42 oriented architecture (SOA) and other enabling tech- tems, applications, and middleware solutions related 93 43 nologies for IoT, and how such IoT systems are being to context awareness and IoT. Their analysis includes 94 44 adopted in different industries. The vertical domains a historic overview of how the area of context-aware 95 45 that they cover, include the healthcare service industry, computing has evolved over the last two decades. Ad- 96 46 the food supply chain, mine safety, transportation, lo- ditionally, the authors review an extensive list of 50 97 47 gistics and firefighting. Important complementary re- academic and industrial project initiatives. While not 98 48 search challenges and future trends that the authors focusing on industrial IoT applications, the challenges 99 49 highlight include standardization (e.g. communication that the authors highlight are of high importance to 100 50 and identification), security and privacy. From a tech- this domain. First of all, they proclaim the automated 101 51 nical perspective, they consider the scalability and het- configuration of sensors as a necessity due to the 102 AIS (iosart2x 2017/02/20 v1.1.2) ais432.tex 2017/03/09 12:39 [research-article] p. 5/12

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1 52 2 53 3 54 4 55 5 56 6 57 7 58 8 59 9 60 10 61 11 62 12 63 13 64 14 65 15 66 16 67 17 68 18 69 19 70 20 71 21 72 22 Fig. 4. Multiple stakeholders in Industry 4.0 related business processes. 73 23 74 24 sheer scale of industrial wireless networks. Other well- times redundant functionalities. The middleware is 75 25 known challenges being mentioned, include the dis- built around a context module and a scalable context- 76 26 covery, modeling, distribution, and sharing of context based search engine that leverages the semantic char- 77 27 information, as well as the ability to reason upon con- acteristics of devices to help users interact with them. 78 28 text information and select sensors in a sensing-as-a- Preliminary performance experiments show accept- 79 29 service model. Last but not least, the authors also men- able indexing and querying response time results. The 80 30 tion security, privacy and trust as major considerations authors also argue that COBASEN can be an essen- 81 31 that have been there since the beginning of context- tial tool to improve the development of Industrial IoT 82 32 aware computing, concerns that will be here to stay in (IIoT) applications, by offering support not only for 83 33 the foreseeable future. those people that deploy middleware, but also the ones 84 34 Dhuieba et al. [12] highlight the importance of ad- that implement the IIoT applications and industrial 85 35 equate knowledge management in manufacturing ap- processes. 86 36 plications. With digital factories integrating a variety Alexopoulos et al. [2] present a context-aware infor- 87 37 of IT-based systems – from product lifecycle manage- mation distribution system to support users in indus- 88 38 ment (PLM) systems, manufacturing execution sys- trial environments. Their context-aware manufacturing 89 39 tems (MES), enterprise resource planning (ERP) sys- information system, called CA-MIS, is event-based 90 40 tems to manufacturing process management (MPM) and data driven. The problem that they address, fo- 91 41 systems, etc. – knowledge is omnipresent, a necessity cuses on the fact that contemporary information infras- 92 42 to support different factory stakeholders with achiev- tructures often fail to successfully aggregate and man- 93 43 ing their daily design, planning and optimization activ- age data from factory-wide sensor networks as well as 94 44 ities. The authors investigate how context-awareness from various data sources, such as MES and ERP sys- 95 45 can support knowledge reuse in multiple production tems. Additionally, these systems often do not analyze 96 46 contexts and factory environments while reducing in- the data and deliver it to different users in a context- 97 47 formation overload. based manner. Their proposed solution builds on top 98 48 Lunardi et al. [32] present COBASEN, a middle- of NFC, RFID, RDF and HTML5 enabling technolo- 99 49 ware to simplify the discovery, search, selection, and gies to provide the right information, to the right peo- 100 50 interaction with devices in large Industrial IoT net- ple at the right time on display devices, static or mo- 101 51 works with devices having overlapping and some- bile, available at the shop-ﬂoor. It targets the develop- 102 AIS (iosart2x 2017/02/20 v1.1.2) ais432.tex 2017/03/09 12:39 [research-article] p. 6/12

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1 ment and deployment of services and applications to breadth-first review of big data technologies in man- 52 2 support decision making for users working at the manufacturing focuses on the systematic mapping of big 53 3 ufacturing shop-floor. data technologies in manufacturing. They investigate 5 54 4 fundamental research questions: (1) what are the pop- 55 5 ular publication fora for big data research in manufac- 56 6 4. Optimizing industry processes with Big Data turing, (2) what kind of formal or practical research 57 7 and Cloud Computing is being carried out, (3) what kind of contributions – 58 8 ranging from systems and tools to optimization meth- 59 9 While the Internet of Things paradigm and recent ods – are being made in this area as results from these 60 10 advances in Machine-to-Machine (M2M) communica- research efforts, (4) what kind of analytics are being 61 11 tion enable real-time monitoring of smart factories, the used, and (5) which areas within manufacturing are be- 62 12 effective optimization of business processes and re- ing targeted the most. 63 13 source consumption often rely on fairly data intensive Pisching et al. [43] investigate how the domains of 64 14 processes for which the computational resources avail- both Internet of Things and Cyber-Physical Systems 65 15 able on-site are not sufficient. are converging within smart factories to the Internet 66 16 In that sense are cloud computing and Big Data of Services paradigm. The authors present a survey 67 17 critical enabling technologies for the Industry 4.0 about service composition in a cloud-based manufac- 68 18 paradigm. It is not only the place where the bulk of turing setting, and summarize advancements made in 69 19 industrial device data and decision critical informa- this area for Industry 4.0 as a collaborative and in- 70 20 tion is ingested and analyzed, it offers the flexibility tegrated manufacturing environment. These services 71 21 to scale on demand to diverse workloads to automate could represent manufacturing processes, possibly of- 72 22 and optimize business processes (see Fig. 5). It enables fered by virtual enterprises. Customers could then re- 73 23 data analytics with predictable performance, even with quest a particular virtual service, which is then com- 74 24 growing industrial wireless networks of interconnected posed out of existing cloud services to meet the cus- 75 25 things, resulting into a cost-efficient supply chain. tomer’s specific demand. 76 26 O’Donovan et al. [39] discuss the complexity that Wang et al. [54] present a framework that incor- 77 27 manufacturing facilities are faced with when having to porates industrial wireless networks, cloud, and fixed 78 28 manage exponentially increasing amounts of data, and or mobile terminals with smart artifacts such as ma- 79 29 analyze those datasets to extract meaning in order to chines, products, and conveyors. Their objective is the 80 30 make well-informed business critical decisions. Their realization of a vertical integration to implement a flex- 81 31 ible and reconfigurable smart factory. They also elabo- 82 32 rate on the ability of such ecosystems to self-organize, 83 33 assisted with feedback from big data analytics building 84 34 blocks running in the cloud. Beyond these technical as- 85 35 pects, the authors also highlight important challenges 86 36 that related to intelligent decision making and nego- 87 37 tiation, high speed industrial wireless network proto- 88 38 cols, special features of manufacturing for big data an- 89 39 alytics, system modeling and analysis, cyber and prop- 90 40 erty security, and modularization with flexible physical 91 41 artifacts. The authors conclude that self-organization, 92 42 coordination and big data based feedback are key to 93 43 ensure that smart machines and products can commu- 94 44 nicate and negotiate with each other to reconfigure 95 45 themselves for flexible production of multiple types of 96 46 products. 97 47 Kehoe et al. [25] specifically survey over 150 works 98 48 in the area of cloud robotics and automation, citing ex- 99 49 amples where the cloud can enhance automation by fa- 100 50 cilitating access to data sets, models, designs, simula- 101 51 Fig. 5. Cloud-enabled networked production and manufacturing. tion tools and other software. While moving robotics 102 AIS (iosart2x 2017/02/20 v1.1.2) ais432.tex 2017/03/09 12:39 [research-article] p. 7/12

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1 and automation algorithms into the cloud may miti- cern, they research the applicability of combining clus- 52 2 gate the long delays when waiting for complex data tering and supervised machine learning as a possible 53 3 analytics processes to return results, the authors also way to improve quality monitoring. While their ap- 54 4 raise a range of security and privacy concerns related proach is promising, the authors also highlight chal- 55 5 to cloud-connected robots and sensors that may collect lenges emerging from the application of the proposed 56 6 sensitive information, such as corporate trade secrets, technique in industrial manufacturing environments. 57 7 as well as other regulatory, accountability and legal is- These relate to the complexity of collecting data and 58 8 sues of remotely connected systems. the integrity of that data, the creating of learning data 59 9 for supervised learning methods, and possible risks of 60 10 over-fitting. 61 11 5. Smarter manufacturing and production with A more recent by West et al. [56] re-explored the 62 12 machine learning and data mining advantages, current challenges and contemporary ex- 63 13 amples of successful applications of machine learn- 64 14 More and more software and intelligence are being in manufacturing. The same concerns remain, be- 65 15 ing integrated into industrial production and manu- ing a growing complexity, highly dynamic produc- 66 16 facturing systems to reduce the cost efficiency and tion environments, and high dimensionality data with 67 17 improve the quality, efficiency and flexibility of pro- chaotic structures. Their work resulted in a mapping of 68 18 duction. The application of machine learning in man- unsupervised machine learning, reinforcement learn- 69 19 ufacturing is hardly a novel theme. Already more than ing, and supervised machine learning algorithms and 70 20 two decades ago, Monostori et al. [37] surveyed var- matching applications. The authors conclude that su- 71 21 ious machine learning techniques that seemed appli- pervised learning is a good fit for most manufacturing 72 22 cable for realizing manufacturing systems with intel- applications given that labeled data is often available 73 23 ligent behavior to handle the growing complexity of for manufacturing applications. However, they also ac- 74 24 such dynamic systems in fast changing production en- knowledge that due to the growing availability of data 75 25 vironments full with uncertainties. and sensor technologies, unsupervised methods may 76 26 Pham et al. [42] discuss in their survey how machine gain importance in the future. 77 27 learning is widely applied in different areas of man- 78 28 ufacturing, highlighting example applications for op- 79 29 timization, control, and troubleshooting. The focus of 6. Human computer and machine interaction for 80 30 their work is on supervised classification, covering al- problem solving and decision making 81 31 gorithms such as decision tree induction, rule induc- 82 32 tion, instance-based learning, neural networks, genetic While automation and optimization of business pro- 83 33 algorithms and Bayesian approaches. The key prob- cesses are major challenges within Industry 4.0, it does 84 34 lems that they identified back in 2014 dealt with the ne- not mean that human beings are taken out of the pic- 85 35 cessity to scale up these machine learning algorithms ture completely. The growing complexity of manufac- 86 36 for significantly larger data sets, and the necessity to turing and production means that adequate solutions 87 37 learn multiple models in parallel. must be provisioned to handle the growing amount of 88 38 Mekid et al. [33] discuss zero-defect workpieces and information. That is why the way humans interact with 89 39 just-in-time production as important challenges of in- such systems for problem solving or decision making 90 40 telligent reconfigurable manufacturing systems. They is a challenge which cannot be ignored. 91 41 address machine learning approaches for parameter Gorecky et al. [17] address intelligent user inter- 92 42 optimization through self-learning as key enablers for faces that offer technological assistance of workers by 93 43 process monitoring and control strategies in next gen- means of representing the cyber-physical world such 94 44 eration intelligent manufacturing systems. that they can realize their full potential as strategic 95 45 West et al. [55] investigate the problem of time- decision-makers and flexible problem solvers. They 96 46 consuming rework and scrapage due to slight varia- investigate to what extent the established interaction 97 47 tions of the product state during production. They ar- technologies and metaphors from the consumer goods 98 48 gue that traditional methods based on modeling of market seem to be promising for the field of In- 99 49 cause-effect relations can no longer handle the industry 4.0. They analyze approaches for the acquisi- 100 50 creasing complexity and high-dimensionality of mod- tion, aggregation, visualization, and re-use of data and 101 51 ern manufacturing programmes. To address this con- information within mobile context-aware interaction 102 AIS (iosart2x 2017/02/20 v1.1.2) ais432.tex 2017/03/09 12:39 [research-article] p. 8/12

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1 for the development of manufacturer-independent and tages are the augmented reality subsystem does not re- 52 2 multi-platform user interfaces. quire any conﬁguration, as it learns its environment 53 3 Pfeiffer et al. [41] also investigate how to empower as it goes. Additional experiments show that the sys- 54 4 users to understand, monitor, and control automated tem can handle a large amount of sticky nodes with- 55 5 processes within Industry 4.0. They propose a user- out jeopardizing the stability of the tracking or require 56 6 centered design (UCD) process with methods for us- large amounts of storage space. 57 7 ability and user experience engineering that should be 58 8 fully integrated into the development process. Exist- 59 9 ing concepts within human computer interaction (HCI) 7. Growing concerns of security and privacy 60 10 and human machine interaction (HMI) should evolve threats and new compliance regulations 61 11 not only to deal with more data and new machines, but 62 12 also the account for the growing adoption of mobile Given the generation and aggregation of high vol- 63 13 devices for which existing traditional user interfaces umes of volatile data and sensitive user information, it 64 14 65 are no longer adequate. Key challenges for an Indus- is clear that there are a multitude of security [22] and 15 66 try 4.0 enabled user interface include a.o. the ability privacy challenges caused by sensors and smart de- 16 67 to handle large amounts of data with different levels vices continuously monitoring the environment. Em- 17 68 of abstraction, offering explanation for automatic deci- bedded in the infrastructure and the products them- 18 69 sion systems, the tension between standardized versus selves, these sensors not only track business and man- 19 70 personalized user interfaces within collaborative pro- ufacturing processes, but also customer behavior after 20 71 duction systems, and training. the production phase, and sometimes in a way that is 21 72 Flatt et al. [14] employ context-awareness to assist not visible to them. Indeed, the attack surface for se- 22 73 with maintenance activities in smart factories. They curity and privacy threats grows, as depicted in Fig. 6. 23 74 present a tablet-based software solution that leverages That is why a Defense-in-Depth strategy for all pro- 24 75 augmented reality and indoor localization to place vir- duction processes to protect both physical access and 25 76 tual sticky notes at production modules. The objec- digital access on every network layer (as shown in 26 77 tive is that this system can exchange information about Fig. 7) is paramount. 27 78 static and dynamic maintenance information and re- Cyber-security remains a clear challenge for the 28 79 curring maintenance tasks that show a worker how to roll-out of the smart factories of the future. Many of 29 80 execute particular maintenance steps. Next to textual the systems, technologies and protocols that exist to- 30 81 31 information (e.g. plant process data or manuals), their day and that will become constituents of Industry 4.0 82 32 solution also supports audio and video. Key advan- were never designed with networked production and 83 33 84 34 85 35 86 36 87 37 88 38 89 39 90 40 91 41 92 42 93 43 94 44 95 45 96 46 97 47 98 48 99 49 100 50 101 51 Fig. 6. Growing incentives for a hacker to attack distributed Industry 4.0 applications. 102 AIS (iosart2x 2017/02/20 v1.1.2) ais432.tex 2017/03/09 12:39 [research-article] p. 9/12

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1 security in the era of Big Data [9]. The general princi- 52 2 ples can be summarized as follows: 53 3 54 Proactive not Reactive; Preventative not Reme- 4 1. 55 dial 5 56 The goal is to employ proactive rather than reac- 6 57 tive measures in order to anticipate and prevent 7 58 privacy-invasive events from occurring. 8 59 2. Privacy as the Default Setting 9 60 Personal information must be automatically pro- 10 61 tected in any software system. Even if the indi- 11 62 vidual does not undertake any action, his or her 12 63 privacy is preserved. 13 64 3. Privacy Embedded into Design 14 65 Fig. 7. Security best practices for networked applications. Privacy must be embedded into the design and 15 66 architecture of any software system and business 16 large scale connectivity in mind. This can be witnessed 67 process from the start, and should never be an 17 from recent successful attacks on SCADA systems by 68 afterthought. 18 dangerous malware like Stuxnet, Duqu, Flame, and 69 4. Full Functionality – Positive-Sum, Not Zero-Sum 19 70 Gauss [6,27]. In [38], Nicholson et al. survey ongoing The functionality of a software system should 20 71 research and present an overview of risks, threats and only be extended if at the same time the system’s 21 72 mitigation strategies in the area of SCADA security. privacy awareness is improved. 22 73 Sadeghi et al. [48] discuss various security and pri- 5. End-to-End Security – Full Lifecycle Protection 23 74 vacy challenges in the industrial Internet of Things, Strong security measures are essential to protect 24 75 and offer an outlook on possible solutions towards a personal information. Security and privacy mea- 25 76 holistic security framework for Industrial IoT systems. sures go hand in hand during the entire lifecycle 26 77 They argue that contemporary security solutions are of the data involved. 27 78 inadequate due to scalability issues to handle large 6. Visibility and Transparency – Keep it Open 28 79 networks of heterogeneous devices and cyber-physical All stakeholders are assured that all business 29 80 systems, as well as concerns of constrained resources practices and technologies are operating accord- 30 81 and the necessity to not jeopardize the fulfillment of ing to the stated promises, subject to independent 31 82 real-time requirements. verification. 32 83 Preuveneers et al. [44] present a framework that 7. Respect for User Privacy – Keep it User-Centric 33 84 builds on top of Big Data enabling technologies to ad- Keep the interests of the individual uppermost by 34 85 dress various security and privacy challenges in the offering strong privacy defaults, appropriate no- 35 86 IoT. Their solution is inherently an attribute-based ac- tice, empowering individuals with user-friendly 36 87 cess control solution built on top of the Apache Spark options. 37 Streaming framework to enforce access control poli- 88 38 cies on incoming and intermediate data streams. Their To address these concerns, the EU General Data 89 1 39 solution also incorporates data protection safeguards Protection Regulation (GDPR) [53] – put forward by 90 40 to protect the disclosure of sensitive data and quasi- the European Commission back in January 2012 – was 91 41 identifiers using a.o. k-anonimity [50] and l-diversity finally agreed upon in February 2016. The GDPR aims 92 42 privacy strategies. to harmonize the current data protection laws in place 93 43 With personalized manufacturing and product in- across the EU member states and directly applies to all 94 44 dividualization being important objectives of Indus- EU member states after a period of two years without a 95 45 try 4.0, it is no surprise that new regulations and di- need for implementing legislation at the national level. 96 46 rectives will be put in place to protect the privacy of As such, the GDPR will significantly affect businesses 97 47 individuals. The seven core principles of Privacy by in all industry sectors, including those outside the EU 98 48 Design (PbD) [8,26], as proposed by Cavoukian in the that process data of EU citizens as a service to support 99 49 90’s in response to the growing impact of ICT and the effective operation of smart factories. 100 50 large-scale networked data systems, appear more often 101 51 these days in discussions on data protection and data 1http://www.eugdpr.org/more-resources.html. 102 AIS (iosart2x 2017/02/20 v1.1.2) ais432.tex 2017/03/09 12:39 [research-article] p. 10/12

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1 8. Summary and reflection on the state of the art – Risks with shifting the intelligence: A strategic 52 2 risk with the application of machine learning in 53 3 This survey on emerging trends and research chal- industrial applications, is that several classifica- 54 4 lenges on the industry of the future covered a wide va- tion and clustering techniques work very well, 55 5 riety of topics relevant for the design of intelligent en- but do not offer any insights into why a model 56 6 vironments. By no means did we aim to explore in- comes to these conclusions in a way that is under- 57 7 depth the state of the art on each of these topics. Our standable for human beings. Rather than reducing 58 8 goal was to give a cross-disciplinary flavor of how on- the cognitive load from operators and engineers, 59 9 going research is shaping the future of intelligent man- these enabling technologies may actually hamper 60 10 ufacturing environments, while identifying important user convenience and usability. 61 11 opportunities for further research. To get more details – Compliance regulations and legal implications: 62 12 and insights on a particular topic, we highly encourage Industry 4.0 is heavily data driven. Each of the 63 13 the audience to read the comprehensive surveys we re- above objectives leverages context information 64 14 ferred to. from systems, customers and other assets. With 65 15 However, reflecting back on the surveyed works, data minimization being imposed in future pri- 66 16 we can conclude that making Industry 4.0 applications vacy regulations, the question emerges what kind 67 17 smart requires a holistic approach that further compli- of trade-offs exists between purposefully reduc- 68 18 cates the development process. We identified the fol- 69 ing the amount and/or quality of sensitive infor- 19 lowing opportunities for further research on industrial- 70 mation being collected and the ability to meet 20 level intelligent environments to smoothen the transi- 71 the above goals. Even as researchers, we will no 21 tion from a proof-of-concept in the lab into tangible 72 longer be exempt from new compliance obliga- 22 deployments in the real world: 73 tions and regulations when controlling or process- 23 – Guaranteeing predictable system behavior: In- ing sensitive information. 74 24 dustry 4.0 applications do not operate in isola- 75 25 tion, but often rely on and interact with services Last but not least, we highly recommend the subse- 76 26 and components from third parties, which make quent research articles in this thematic issue. In their 77 27 them harder to test. With an increased focus on own way, they each advance the fields towards the re- 78 28 networked production, failures are bound to hap- alization of truly intelligent industrial environments. 79 29 pen. Given that the competitiveness of a com- 80 30 pany resolves around cost efficiency, mitigating 81 31 unknown risks due to external influences early Acknowledgements 82 32 in the design phase is key. Applying a design 83 33 for failure methodology for distributed intelligent This research is partially funded by the Research 84 34 manufacturing systems may improve the relia- Fund KU Leuven and by the European Commis- 85 35 bility and robustness against unforeseen circum- sion through the H2020 project EXCELL (http:// 86 36 stances to guarantee zero-defect manufacturing. excell-project.eu/) under grant No. 691829. 87 37 However, the literature seems to be focused more 88 38 on avoiding errors rather than having contingency 89 39 plans in place to be prepared for failures. References 90 40 – Quality assurance for context-aware behavior: 91 41 Heavily built on top of industrial wireless sensor [1] R. Ahmad and D. Kim, A collaboration based context pre- 92 42 networks, context-awareness plays a key role in diction in smart office, Journal of Ambient Intelligence and 93 43 strategic decision making (for troubleshooting or Smart Environments 7(6) (2015), 805–815. doi:10.3233/AIS- 94 44 150348. 95 process optimization). The scientific literature on [2] K. Alexopoulos, S. Makris, V. Xanthakis, K. Sipsas and 45 96 context-aware computing includes several works G. Chryssolouris, A concept for context-aware computing in 46 on how to effectively deal with subpar context in- manufacturing: The white goods case, Int. J. Comput. Integr. 97 47 formation quality. However, there is limited work Manuf. 29(8) (2016), 839–849. doi:10.1080/0951192X.2015. 98 48 in this area for smart factories, meaning that there 1130257. 99 49 [3] S. Aly, M. Pelikan and I. 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