How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0
Abstract
:1. Introduction
2. Industry 4.0 Background
2.1. Ubiquitous Internet Access
- Since they are not limited by a cable, they are quick and easy to deploy, even for hard to reach and remote locations.
- They offer more flexibility and easily adapt to changes in the network configuration, thus improving scalability.
- They enable device mobility, providing real-time Internet access anywhere and anytime.
- They are available in personal smart devices such as smartphones, tablets, and smartwatches, which are equipped with a large number of sensors. Therefore, they can provide relevant information about the owner, enabling applications related to, for example, employee monitoring or customer feedback collection.
- Interference and path loss: industrial environments are characterised by challenging conditions, due to the presence of dust, vibrations, heat, obstacles, critical temperatures and humidity levels. Furthermore, the presence of motors, metal obstacles and other wireless communications introduces severe signal interference.
- Latency: industrial processes are typically required to have real-time or quasi-real-time performance, hence latency has to be kept low. Nevertheless, higher energy amounts and costs are needed to reduce latency, and this might affect nodes’ lifetime.
- Changing location: industrial processes may be location-dependent, meaning that node location may be a parameter for the process to be carried out. Therefore, node location should be always known, even when they are mobile. Accordingly, localization and tracing mechanisms need to be introduced.
- Security and privacy: since wireless signals can be easily intercepted, they are much more susceptible to security and privacy attacks (e.g., jamming, spoofing, packet sniffing) with respect to wired signals. For this reason, specific security and privacy-preservation technologies have been developed, so that industrial processes and employee/customer data are not threatened.
2.2. Machine-to-Machine Communication
2.3. Advanced Analytics
3. Application Domains
3.1. Asset Utilization
3.2. Quality Control in Manufacturing
3.3. Supply Chain Management
3.4. Product Monitoring
3.5. Workplace Safety
4. Industry-Related Challenges and Opportunities
- reduce latencies and ensure accuracy independently from the physical medium: the majority of industrial applications require real-time and deterministic responsiveness. This is already ensured by typical communication standards used in industrial scenarios (e.g., PROFINET, HART), but with the introduction of wireless communication standards and devices with heterogeneous characteristics, this performance might degrade. Indeed, existing communication standards that are not conceived for industrial applications need to be adapted in order to take these requirements into account, particularly introducing mechanisms for prioritization, time slot allocation and synchronization. Furthermore, the use of different communication standards needs to be performed seamlessly. These requirements need to be fulfilled even considering an increase in demand;
- perform fault tolerance without additional hardware: reliability of data flows needs to be ensured even in the event of faults and failures, without installing additional hardware that would nullify the benefit of using the existing hardware to perform the applications. To this aim, the IEEE TSN WG (Time-Sensitive Networking Working Group) is defining mechanisms that allow replication and redundant transmission of data over several disjunctive paths;
- support higher security, safety and privacy: with the introduction of heterogeneous communication media and protocols that are not conceived to work in an industrial environment, particularly wireless, it is necessary to study new mechanisms that ensure safety and security. It should be ensured that the production facilities and product itself do not threaten people and the environment. Product misuse and unauthorized access to production facilities need to be prevented. Furthermore, the acquisition of personal data about customers and workers introduces privacy issues that need to be taken into account when designing and implementing an Industry 4.0 application [46];
- provide interoperability of solutions from different manufacturers: existing industrial hardware mainly uses proprietary solutions to work and communicate with other devices. Indeed, one of the major known issues related to the use of heterogeneous devices is interoperability between newly installed devices and existing devices, particularly when existing devices are extremely efficient, reliable and expensive, and their replacement would be difficult. Therefore, a set of uniform standards needs to be developed so that a network between different factories and companies can be connected and integrated.
- the improvement of industrial process management will lead to an improvement in the whole production, mainly given by the reduction in production time. The increased level of integration and data exchange will lead to an increase in the complexity of business processes and in the level of automation ensured. Flexibility will be improved, as processes will be made more agile. Dynamic and adaptive optimized decision-taking will improve productivity and efficiency: the lowest amount of resources will be used to produce the highest volume of products, while minimizing emissions due to production processes;
- improved working conditions will be ensured, both taking into account an improvement in security and safety, and in working time, work organisation, and work-life balance. Indeed, thanks to the improvements provided by the previous objective and to new technologies such as MCS and big data analysis, new innovative services will be provided. This will not only create new value opportunities but also enable diverse and flexible career paths that will allow people to work and remain productive for longer. Furthermore, this new flexibility will enable more flexible work organisation models, which will gradually meet the growing need of employees to strike a better balance between their work and private lives;
- new technologies, particularly with reference to MCS and advanced analysis, will be key not only to improve production and working conditions but also for an improvement in customer satisfaction. First off, optimized production processes and supply chain management will lead to a supply that perfectly fits the demand. Furthermore, individual, customer-specific criteria will be included in the design, configuration, ordering, planning, manufacturing and operation phases, also incorporating last-minute changes, indeed enabling mass customization to be implemented.
5. Using MCS in Industry 4.0 Scenarios
5.1. Role of MCS in Industry 4.0
- assets and environmental monitoring. It is based on objective measurements performed by sensors, mainly static or with a limited mobility range;
- products and workers monitoring. It is based on objective measurements performed by sensors, mainly mobile and geolocated;
- workers and customers’ feedback. It is based on the acquisition of subjective feedback, mainly mobile and geolocated.
5.2. MCS Architecture for Industry 4.0
- MCS-enabled end device: it is a mobile device that is provided with communication interfaces and enough computation capabilities to run the services that enable MCS in them. In particular, specific interfaces to communicate with the components residing in the cloud are needed. Furthermore, context-aware functionalities need to be implemented, so that data are collected depending on whenever the context (e.g., position, time) is relevant.
- MCS-enabled gateway: since not all the end devices can be MCS-enabled, the MCS-enabled gateway needs to provide the MCS functionalities to common nodes that do not have them. Hence, this gateway provides interfaces so that common nodes can communicate with the MCS components in the cloud, and augment data coming from these nodes with context-aware information.
- Semantic information manager: it is responsible for the management of the semantic data model thanks to which a common semantic description language is used to provide context-awareness to network nodes.
- Big data analysis manager: it receives the data collected by MCS-enabled nodes and gateways and analyses them in order to provide relevant information for the application that needs to be performed by the system.
- Security manager: provides security and privacy functionalities so that system nodes can communicate without any threat, and personal information is kept safe from malicious attacks.
5.3. Challenges and Open Issues
6. Conclusions
Acknowledgments
Conflicts of Interest
References
- Atzori, L.; Iera, A.; Morabito, G. The internet of things: A survey. Comput. Netw. 2010, 54, 2787–2805. [Google Scholar] [CrossRef]
- Stankovic, J.A. Research directions for the internet of things. IEEE Internet Things J. 2014, 1, 3–9. [Google Scholar] [CrossRef]
- Evans, P.C.; Annunziata, M. Industrial Internet: Pushing the Boundaries of Minds and Machines. General Electric Reports. 2012. Available online: http://futureview.itrm.ru/documents/50bcf5f13ed696cd87000001.pdf (accessed on 27 February 2018).
- Da Xu, L.; He, W.; Li, S. Internet of things in industries: A survey. IEEE Trans. Ind. Inform. 2014, 10, 2233–2243. [Google Scholar]
- Maglaras, L.; Shu, L.; Maglaras, A.; Jiang, J.; Janicke, H.; Katsaros, D.; Cruz, T.J. Industrial Internet of Things (I2oT). Mob. Netw. Appl. 2017, 1–3. [Google Scholar] [CrossRef]
- Lee, J.; Bagheri, B.; Kao, H.A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 2015, 3, 18–23. [Google Scholar] [CrossRef]
- Zhou, K.; Liu, T.; Zhou, L. Industry 4.0: Towards future industrial opportunities and challenges. In Proceedings of the IEEE 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China, 15–17 August 2015; pp. 2147–2152. [Google Scholar]
- Jazdi, N. Cyber physical systems in the context of Industry 4.0. In Proceedings of the 2014 IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, Romania, 22–24 May 2014; pp. 1–4. [Google Scholar]
- Brettel, M.; Friederichsen, N.; Keller, M.; Rosenberg, M. How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective. Int. J. Mech. Ind. Sci. Eng. 2014, 8, 37–44. [Google Scholar]
- Shrouf, F.; Ordieres, J.; Miragliotta, G. Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. In Proceedings of the 2014 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bandar Sunway, Malaysia, 9–12 December 2014; pp. 697–701. [Google Scholar]
- Wang, Z.; Chen, C.; Guo, B.; Yu, Z.; Zhou, X. Internet plus in China. IT Prof. 2016, 18, 5–8. [Google Scholar] [CrossRef]
- Shu, L.; Chen, Y.; Huo, Z.; Bergmann, N.; Wang, L. When mobile crowd sensing meets traditional industry. IEEE Access 2017, 5, 15300–15307. [Google Scholar] [CrossRef]
- Kagermann, H.; Helbig, J.; Hellinger, A.; Wahlster, W. Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry; Final Report of the Industrie 4.0 Working Group; Acatech: München, Germany, 2013. [Google Scholar]
- Da Silva, G.C.; Kaminski, P.C. From Embedded Systems (ES) to Cyber-Physical Systems (CPS): An Analysis of Transitory Stage of Automotive Manufacturing in the Industry 4.0 Scenario; Technical Report, SAE Technical Paper; SAE International: Warrendale, PA, USA, 2016. [Google Scholar]
- Weyrich, M.; Schmidt, J.P.; Ebert, C. Machine-to-machine communication. IEEE Softw. 2014, 31, 19–23. [Google Scholar] [CrossRef]
- Varghese, A.; Tandur, D. Wireless requirements and challenges in Industry 4.0. In Proceedings of the IEEE 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, India, 27–29 November 2014; pp. 634–638. [Google Scholar]
- Li, X.; Li, D.; Wan, J.; Vasilakos, A.V.; Lai, C.F.; Wang, S. A review of industrial wireless networks in the context of industry 4.0. Wirel. Netw. 2017, 23, 23–41. [Google Scholar] [CrossRef]
- Gorecky, D.; Schmitt, M.; Loskyll, M.; Zühlke, D. Human-machine-interaction in the Industry 4.0 era. In Proceedings of the 2014 12th IEEE International Conference on Industrial Informatics (INDIN), Porto Alegre, Brazil, 27–30 July 2014; pp. 289–294. [Google Scholar]
- Seo, D.; Jeon, Y.B.; Lee, S.H.; Lee, K.H. Cloud computing for ubiquitous computing on M2M and IoT environment mobile application. Clust. Comput. 2016, 19, 1001–1013. [Google Scholar] [CrossRef]
- Pilloni, V.; Atzori, L.; Mallus, M. Dynamic involvement of real world objects in the IoT: A consensus-based cooperation approach. Sensors 2017, 17, 484. [Google Scholar] [CrossRef] [PubMed]
- Bonomi, F.; Milito, R.; Zhu, J.; Addepalli, S. Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland, 17 August 2012; pp. 13–16. [Google Scholar]
- Fogliatto, F.S.; Da Silveira, G.J.; Borenstein, D. The mass customization decade: An updated review of the literature. Int. J. Prod. Econ. 2012, 138, 14–25. [Google Scholar] [CrossRef]
- Guo, B.; Yu, Z.; Zhou, X.; Zhang, D. From participatory sensing to mobile crowd sensing. In Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), Budapest, Hungary, 24–28 March 2014; pp. 593–598. [Google Scholar]
- Peng, J.; Zhu, Y.; Shu, W.; Wu, M.Y. When data contributors meet multiple crowdsourcers: Bilateral competition in mobile crowdsourcing. Comput. Netw. 2016, 95, 1–14. [Google Scholar] [CrossRef]
- Rüßmann, M.; Lorenz, M.; Gerbert, P.; Waldner, M.; Justus, J.; Engel, P.; Harnisch, M. Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries; Boston Consulting Group: Boston, MA, USA, 2015; Volume 9. [Google Scholar]
- Karre, H.; Hammer, M.; Kleindienst, M.; Ramsauer, C. Transition towards an Industry 4.0 state of the LeanLab at Graz University of Technology. Procedia Manuf. 2017, 9, 206–213. [Google Scholar] [CrossRef]
- Robert, J.; Lindner, T.; Milosiu, H. Sub 10 μW wake-up-receiver based indoor/outdoor asset tracking system. In Proceedings of the 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), Luxembourg, 8–11 September 2015; pp. 1–3. [Google Scholar]
- Reid, M.; Cook, B. The Application of Smart, Connected Power Plant Assets for Enhanced Condition Monitoring and Improving Equipment Reliability. In Proceedings of the ASME 2016 Power Conference Collocated with the ASME 2016 10th International Conference on Energy Sustainability and the ASME 2016 14th International Conference on Fuel Cell Science, Engineering and Technology, Charlotte, NC, USA, 26–30 June 2016; American Society of Mechanical Engineers (ASME): New York, NY, USA, 2016; p. V001T05A006. [Google Scholar]
- Reis, M.S.; Gins, G. Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis. Processes 2017, 5, 35. [Google Scholar] [CrossRef]
- Smajic, H.; Wessel, N. Remote Control of Large Manufacturing Plants Using Core Elements of Industry 4.0. In Online Engineering & Internet of Things; Springer: Cham, Switzerland, 2018; pp. 546–551. [Google Scholar]
- Qian, F.; Zhong, W.; Du, W. Fundamental Theories and Key Technologies for Smart and Optimal Manufacturing in the Process Industry. Engineering 2017, 3, 154–160. [Google Scholar] [CrossRef]
- Evans, J.R.; Lindsay, W.M. An Introduction to Six Sigma and Process Improvement; Cengage Learning: Boston, MA, USA, 2014. [Google Scholar]
- Lee, J.; Kao, H.A.; Yang, S. Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp 2014, 16, 3–8. [Google Scholar] [CrossRef]
- Li, J.; Tao, F.; Cheng, Y.; Zhao, L. Big data in product lifecycle management. Int. J. Adv. Manuf. Technol. 2015, 81, 667–684. [Google Scholar] [CrossRef]
- Barreto, L.; Amaral, A.; Pereira, T. Industry 4.0 implications in logistics: An overview. Procedia Manuf. 2017, 13, 1245–1252. [Google Scholar] [CrossRef]
- Mallus, M.; Colistra, G.; Atzori, L.; Murroni, M.; Pilloni, V. Dynamic Carpooling in Urban Areas: Design and Experimentation with a Multi-Objective Route Matching Algorith. Sustainability 2017, 9, 254. [Google Scholar] [CrossRef]
- Zhong, R.Y.; Xu, X.; Wang, L. IoT-enabled Smart Factory Visibility and Traceability using Laser-scanners. Procedia Manuf. 2017, 10, 1–14. [Google Scholar] [CrossRef]
- Scheuermann, C.; Verclas, S.; Bruegge, B. Agile factory—An example of an industry 4.0 manufacturing process. In Proceedings of the 2015 IEEE 3rd International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA), Hong Kong, China, 19–21 August 2015; pp. 43–47. [Google Scholar]
- Yan, J.; Meng, Y.; Lu, L.; Li, L. Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance. IEEE Access 2017, 5, 23484–23491. [Google Scholar] [CrossRef]
- Roßmann, B.; Canzaniello, A.; von der Gracht, H.; Hartmann, E. The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study. Technol. Forecast. Soc. Chang. 2017. [Google Scholar] [CrossRef]
- Xu, X.; Zhong, M.; Wan, J.; Yi, M.; Gao, T. Health monitoring and management for manufacturing workers in adverse working conditions. J. Med. Syst. 2016, 40, 222. [Google Scholar] [CrossRef] [PubMed]
- Behr, C.; Kumar, A.; Hancke, G. A smart helmet for air quality and hazardous event detection for the mining industry. In Proceedings of the 2016 IEEE International Conference on Industrial Technology (ICIT), Taipei, Taiwan, 14–17 March 2016; pp. 2026–2031. [Google Scholar]
- Kulkarni, P.; Sangam, V. Smart Helmet for Hazardous event Detection and Evaluation in mining Industries using wireless communication. J. Commun. Eng. Innov. 2017, 3, 11–16. [Google Scholar]
- Wollschlaeger, M.; Sauter, T.; Jasperneite, J. The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0. IEEE Ind. Electron. Mag. 2017, 11, 17–27. [Google Scholar] [CrossRef]
- Nitti, M.; Pilloni, V.; Colistra, G.; Atzori, L. The Virtual Object as a Major Element of the Internet of Things: A Survey. IEEE Commun. Surv. Tutor. 2015, 18, 1228–1240. [Google Scholar] [CrossRef]
- Shin, M.; Cornelius, C.; Kapadia, A.; Triandopoulos, N.; Kotz, D. Location privacy for mobile crowd sensing through population mapping. Sensors 2015, 15, 15285–15310. [Google Scholar] [CrossRef] [PubMed]
- Schmidt, R.; Möhring, M.; Härting, R.C.; Reichstein, C.; Neumaier, P.; Jozinović, P. Industry 4.0-potentials for creating smart products: Empirical research results. In Proceedings of the International Conference on Business Information Systems, Poznań, Poland, 24–26 June 2015; Springer: Cham, Switzerland, 2015; pp. 16–27. [Google Scholar]
- Basanta-Val, P. An efficient industrial big-data engine. IEEE Trans. Ind. Inform. 2017. [Google Scholar] [CrossRef]
- Lv, Z.; Song, H.; Basanta-Val, P.; Steed, A.; Jo, M. Next-generation big data analytics: State of the art, challenges, and future research topics. IEEE Trans. Ind. Inform. 2017, 13, 1891–1899. [Google Scholar] [CrossRef]
- Congosto, M.; Basanta-Val, P.; Sanchez-Fernandez, L. T-Hoarder: A framework to process Twitter data streams. J. Netw. Comput. Appl. 2017, 83, 28–39. [Google Scholar] [CrossRef]
- Shu, L.; Mukherjee, M.; Pecht, M.; Crespi, N.; Han, S.N. Challenges and Research Issues of Data Management in IoT for Large-Scale Petrochemical Plants. IEEE Syst. J. 2017. [Google Scholar] [CrossRef]
- Conti, M.; Passarella, A.; Das, S.K. The Internet of People (IoP): A new wave in pervasive mobile computing. Pervasive Mob. Comput. 2017, 41, 1–27. [Google Scholar] [CrossRef]
- Baccarelli, E.; Naranjo, P.G.V.; Scarpiniti, M.; Shojafar, M.; Abawajy, J.H. Fog of everything: Energy-efficient networked computing architectures, research challenges, and a case study. IEEE Access 2017, 5, 9882–9910. [Google Scholar] [CrossRef]
- Hu, X.; Li, X.; Ngai, E.; Leung, V.; Kruchten, P. Multidimensional context-aware social network architecture for mobile crowdsensing. IEEE Commun. Mag. 2014, 52, 78–87. [Google Scholar] [CrossRef]
- Colistra, G.; Pilloni, V.; Atzori, L. Task allocation in group of nodes in the IoT: A consensus approach. In Proceedings of the 2014 IEEE International Conference on Communications (ICC), Sydney, NSW, Australia, 10–14 June 2014; pp. 3848–3853. [Google Scholar]
- Karati, A.; Islam, S.H.; Biswas, G.; Bhuiyan, M.Z.A.; Vijayakumar, P.; Karuppiah, M. Provably Secure Identity-based Signcryption Scheme for Crowdsourced Industrial Internet of Things Environments. IEEE Internet Things J. 2017. [Google Scholar] [CrossRef]
Application | Acquisition Type | Data Amount | MCS | Traditional ISN | |
---|---|---|---|---|---|
Asset utilization | Asset tracking | Mobile sensors on assets | Medium | Easy thanks to mobility | Possible |
Environmental monitoring | Static and mobile sensors on walls and assets | Medium | Easy thanks to mobility | Easy using static sensors | |
Energy consumption | Static sensors on assets | Low | Not possible | Easy thanks to static sensors | |
Fault detection and predictive maintenance | Static sensors on assets; mobile sensors on workers’ equipment | Medium | Easy thanks to mobility | Easy using static sensors | |
Quality control | Real-time optimization | Static and mobile sensors on assets and workers’ equipment | Medium to large | Easy thanks to mobility and scalability | Possible thanks to static sensors |
Advanced analytics | Static and mobile sensors on assets, workers’ equipment and workers’ personal devices | Large | Easy thanks to mobility, scalability and use of personal devices | Limited to static sensors | |
Supply chain management | Real-time monitoring | Mobile sensors on products and vehicles; workers’ personal devices | Medium to large | Easy thanks to mobility and use of personal devices | Limited to workers in charge of this task |
Logistic tracking | Mobile sensors on products and vehicles; workers’ personal devices | Medium to large | Easy thanks to mobility and use of personal devices | Limited to workers in charge of this task | |
Route planning | Static sensors for roads’ condition; mobile sensors and personal devices on vehicles | Medium to large | Easy thanks to mobility and use of personal devices | Limited to static sensors | |
Quality checking | Mobile sensors on products and vehicles | Medium to large | Easy thanks to mobility | Limited to workers in charge of this task | |
Product monitoring | Real-time monitoring | Mobile sensors on products and vehicles; workers’ personal devices | Medium to large | Easy thanks to mobility and use of personal devices | Limited to workers in charge of this task |
Logistic tracking | Mobile sensors on products and vehicles; workers’ personal devices | Medium to large | Easy thanks to mobility and use of personal devices | Limited to workers in charge of this task | |
Quality checking | Mobile sensors on products | Medium to large | Easy thanks to mobility | Limited to workers in charge of this task | |
Customer feedback | Customers’ mobile personal devices | Medium to large | Easy thanks to mobility and use of personal devices | Difficult and expensive | |
Market analysis | Customers’ mobile personal devices | Large | Easy thanks to mobility and use of personal devices | Difficult, expensive and slow | |
Data-driven demand prediction | Customers’ mobile personal devices | Large | Easy thanks to mobility and use of personal devices | Difficult and slow | |
Workplace safety | Personal monitoring | Mobile workers’ personal devices and equipment | Medium | Easy thanks to mobility | Difficult |
Environmental monitoring | Static and mobile sensors on walls and assets | Medium | Easy thanks to mobility | Easy using static sensors |
© 2018 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Pilloni, V. How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0. Future Internet 2018, 10, 24. https://doi.org/10.3390/fi10030024
Pilloni V. How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0. Future Internet. 2018; 10(3):24. https://doi.org/10.3390/fi10030024
Chicago/Turabian StylePilloni, Virginia. 2018. "How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0" Future Internet 10, no. 3: 24. https://doi.org/10.3390/fi10030024