Development of an industrial Internet of things suite for smart factory towards re-industrialization
Received date: 2017-03-15
Revised date: 2017-11-07
Online published: 2017-12-25
Re-industrialization, which supports industrial upgrading and transformation, promotes smart production and high value-added manufacturing processes, and helps to create new momentum for the economic. Under the current situation, industrialists encounter several challenges to achieve re-industrialization. Firstly, the cost and technical thresholds for industrialists to leverage emerging technologies are high. Secondly, there are huge quantities and numerous types of Internet of things (IoT) devices in smart factories, warehouses and offices. The enormous extents of data exchange and communication, management, monitoring and control of IoT devices as well as the establishment and maintenance of a reliable cloud platform hinder industrialists to implement an integrated smart production management. Therefore, to achieve re-industrialization, an industrial Internet of things (ⅡoT) suite consisting of a micro-services-based ⅡoT cloud platform and ⅡoT-based smart hub is proposed, which helps to materialize re-industrialization and to conduct industrial upgrading and transformation to achieve smart production and high value-added manufacturing processes.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0197-2/fulltext.html
C. K. M. Lee , S. Z. Zhang , K. K. H. Ng . Development of an industrial Internet of things suite for smart factory towards re-industrialization[J]. Advances in Manufacturing, 2017 , 5(4) : 335 -343 . DOI: 10.1007/s40436-017-0197-2
1. Kollmeyer C (2009) Explaining deindustrialization:how affluence, productivity growth, and globalization diminish manufacturing employment. Am J Sociol 114:1644-1674
2. Chen J (2016) Macro-control and economic development in China. Routledge, London
3. Dixon M, Jonas S, McCaughan E et al (1982) Reindustrialization and the transnational labor force in the United States today. Contemp Marx 5:101-115
4. Taplin I, Nguyen MTT (2016) 5 from recession to re-industrialization. In:Begley J, Coffey D, Donnelly T et al (eds) Global economic crisis and local economic development:international cases and policy responses. Routledge, New York, p 78
5. Chen M, Mao S, Liu Y et al (2014) Big data:a survey. Mobile Netw Appl 19:171-209
6. Lee J, Bagheri B, Kao HA et al (2015) A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett 3:18-23
7. Tao F, Zuo Y, Xu LD et al (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Ind Inf 10:1547-1557
8. Zhang S, Zhang S, Chen X et al (2010) Analysis and research of cloud computing system instance. In:The second international conference on future networks, pp 88-92
9. Qiu M, Xue C, Shao Z et al (2006) Efficient algorithm of energy minimization for heterogeneous wireless sensor network. In:International conference on embedded and ubiquitous computing, Seoul, Korea, pp 25-34
10. Lin CC, Deng DJ, Chen ZY et al (2016) Key design of driving Industry 4.0:joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks. IEEE Commun Mag 54:46-52
11. Li X, Li D, Wan J et al (2017) A review of industrial wireless networks in the context of Industry 4.0. Wirel Netw 23(1):23-41
12. Waibel M, Beetz M, Civera J et al (2011) A world wide web for robots. IEEE Robot Autom Mag 18:69-82
13. Zhang Y, Qiu M, Tsai CW et al (2015) Health-CPS:healthcare cyber-physical system assisted by cloud and big data. IEEE Syst J 11(1):88-95
14. Pirvu BC, Zamfirescu CB, Gorecky D et al (2016) Engineering insights from an anthropocentric cyber-physical system:a case study for an assembly station. Mechatronics 34:147-159
15. Chen F, Deng P, Wan J et al (2015) Data mining for the internet of things:literature review and challenges. Int J Distrib Sens Netw 9:12
16. Xu LD, He W, Li S et al (2014) Internet of things in industries:a survey. IEEE Trans Industr Inf 10:2233-2243
17. Weyrich M, Ebert C (2016) Reference architectures for the internet of things. IEEE Softw 33:112-116
18. Wang S, Wan J, Zhang D et al (2016) Towards smart factory for Industry 4.0:a self-organized multi-agent system with big data based feedback and coordination. Comput Netw 101:158-168
19. Palattella MR, Accettura N, Grieco LA et al (2013) On optimal scheduling in duty-cycled industrial IoT applications using IEEE802.15.4e TSCH. IEEE Sens J 13(10):3655-3666
20. Gorecky D, Schmitt M, Loskyll M et al (2014) Human-machineinteraction in the Industry 4.0 era. In:The 12th IEEE international conference on industrial informatics (INDIN), Porto Alegre, Brazil, pp 289-294
21. Lee EA (2008) Cyber physical systems:design challenges. In:The 11th IEEE international symposium on object oriented realtime distributed computing (ISORC), Orlando, Florida, p 363-369
22. Kang W, Kapitanova K, Son SH et al (2012) RDDS:a real-time data distribution service for cyber-physical systems. IEEE Trans Ind Inf 8(2):393-405
23. Gossman WE, Hartmaier PJ (2001) System and method for providing data to a wireless device upon detection of activity of the device on a wireless network. US Patent 6317594 B1, 13 Nov 2001
24. Santos A, Nunes G, Gil P et al (2010) Multi-agent platform in wsan applications:a time synchronization perspective. IFAC Proc Vol 43:367-374
25. Colombo AW, Bangemann T, Karnouskos S et al (2014) Industrial cloud-based cyber-physical systems. The IMC-AESOP Approach. Springer, Berlin, pp 15-16
26. Trappey AJ, Trappey CV, Govindarajan UH et al (2016) A review of essential standards and patent landscapes for the Internet of things:a key enabler for Industry 4.0. Adv Eng Inform. https://doi.org/10.1016/j.aei.2016.11.007
27. Kagermann H, Helbig J, Hellinger A et al (2013) 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, Forschungsunion
28. Qin J, Liu Y, Grosvenor R et al (2016) A categorical framework of manufacturing for Industry 4.0 and beyond. Proc CIRP 52:173-178
29. Lee CKM, Yi C, Ng KH (2017) Big data analytics for predictive maintenance strategis. In:Chan HK, Subramanian N, Abdulrahman MDA (eds) Supply chain management in the big data era. IGI Global, Hershey, PA, pp 50-74
30. Mikusz M (2014) Towards an understanding of cyber-physical systems as industrial software-product-service systems. Proc CIRP 16:385-389
31. Monostori L (2014) Cyber-physical production systems:roots, expectations and R&D challenges. Proc CIRP 17:9-13
32. Lee J, Bagheri B, Kao HA et al (2015) A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett 3:18-23
33. Pang Z, Chen Q, Tian J et al (2013) Ecosystem analysis in the design of open platform-based in-home healthcare terminals towards the internet-of-things. In:The 15th international conference on advanced communication technology (ICACT), pp 529-534
34. Wei QP, Zhu SB, Du CQ et al (2011) Study on key technologies of Internet of things perceiving mine. Proc Eng 26(4):2326-2333
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