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The State and Future of Ambient Intelligence in Industrial IoT Environments

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Guide to Ambient Intelligence in the IoT Environment

Part of the book series: Computer Communications and Networks ((CCN))

Abstract

The advent of the Fourth Industrial Revolution has brought about the drive toward integrating systems and processes through the Internet of Things (IoT) in industrial environments. The major objective of introducing IoT into these environments is to realize dynamic optimization of productivity and efficiency and to mitigate the risks affecting financial loss and safety of personnel. Recent proliferation of sensors and smart embedded devices capable of communicating with each other offers industry the possibility of ubiquitous computing. Other distributed computing paradigms such as cloud computing are also helping to achieve the said objective. The next evolutionary step for ubiquitous computing in relation to industrial environments is the deployment of ambient intelligence (AmI) within the smart devices to bring about seamless integration of the personnel and environmental factors as well as equipment in the workplace. In this chapter, we look at how the application of AmI in the industrial sector has sought progress, and attempt to extricate the past and current challenges in terms to context, architecture , security, and uptake of relevant technologies. A bottom-up approach is taken in reviewing the key technological constituencies of AmI in Industrial IoT (IIoT) from the sensing technologies and communication to overall architectural developments and limitations. Some examples of future application scenarios of AmI in mining, manufacturing, and construction are also presented that offer high-level depictions of how the different aspects of AmI could potentially be brought together to benefit these industrial settings. Ultimately, this work aims to provide stakeholders with an understanding of the possibilities of AmI in IIoT environments through equipping them with knowledge of the state-of-the-art, technological limitations/barriers, and future developments encompassing this application area.

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References

  1. Mukhopadhyay SC, Suryadevara NK (2014). Internet of things: challenges and opportunities. In: Internet of things. Springer, Cham, pp 1–17

    Chapter  Google Scholar 

  2. Jeschke S, Brecher C, Meisen T, Özdemir D, Eschert T (2017) Industrial internet of things and cyber manufacturing systems. In: Industrial internet of things. Springer, Cham, pp 3–19

    Google Scholar 

  3. Robinson DC, Sanders DA, Mazharsolook E (2015) Ambient intelligence for optimal manufacturing and energy efficiency. Assem Autom 35(3):234–248

    Article  Google Scholar 

  4. Sammarco JJ, Paddock R, Fries EF, Karra VK (2007) A technology review of smart sensors with wireless networks for applications in hazardous work environments. Information Circular 9496

    Google Scholar 

  5. Irani Z, Kamal MM (2014) Intelligent systems research in the construction industry. Expert Syst Appl 41(4):934–950

    Article  Google Scholar 

  6. Cook BS, Le T, Palacios S, Traille A, Tentzeris MM (2013) Only skin deep: Inkjet-printed zero-power sensors for large-scale RFID-integrated smart skins. IEEE Microw Mag 14(3):103–114

    Article  Google Scholar 

  7. Liu L, Han G, Chan S, Guizani M (2018) An SNR-assured anti-jamming routing protocol for reliable communication in industrial wireless sensor networks. IEEE Commun Mag 56(2):23–29

    Article  Google Scholar 

  8. Silva I, Guedes LA, Portugal P, Vasques F (2012) Reliability and availability evaluation of wireless sensor networks for industrial applications. Sensors 12(1):806–838

    Article  Google Scholar 

  9. Henniger O, Damer N, Braun A (2017) Opportunities for biometric technologies in smart environments. In: European conference on ambient intelligence. Springer, Cham, pp 175–182

    Google Scholar 

  10. Wang YWA, Hazel T, Hjornevik R, Fjeld O (2017) Equipment monitoring using thermography cameras: remote detection of temperature-related failures. IEEE Ind Appl Mag 23(4):35–44

    Article  Google Scholar 

  11. Narain Singh A, Doorsamy W, Cronje WA (2018) Thermal instability analysis of a synchronous generator rotor using direct mapping. SAIEE Afr Res J 109(1):4–14

    Google Scholar 

  12. Shimada S, Tanaka H, Hasebe K, Hayashi N, Ochi Y, Matsui T, Nishizaki I, Matsumoto Y, Tanaka Y, Nakamura H, Mizuno Y (2016) Ultrasonic welding of polymer optical fibres onto composite materials. Electron Lett 52(17):1472–1474

    Article  Google Scholar 

  13. Wu W, Zheng Y, Chen K, Wang X, Cao N (2018) A visual analytics approach for equipment condition monitoring in smart factories of process industry. In: Pacific visualization symposium (PacificVis). IEEE, pp 140–149

    Google Scholar 

  14. Zhgoon SA, Shvetsov AS, Sakharov SA, Elmazria O (2018) High-temperature SAW resonator sensors: electrode design specifics. IEEE Trans Ultrason Ferroelectr Freq Control 65(4):657–664

    Article  Google Scholar 

  15. Ajith R, Tewari A, Gupta D, Tallur S (2017) Low-cost vibration sensor for condition-based monitoring manufactured from polyurethane foam. IEEE Sens Lett 1(6):1–4

    Article  Google Scholar 

  16. Yuan X, Wang Y, Yang C, Ge Z, Song Z, Gui W (2018) Weighted linear dynamic system for feature representation and soft sensor application in nonlinear dynamic industrial processes. IEEE Trans Ind Electron 65(2):1508–1517

    Article  Google Scholar 

  17. Bidar B, Shahraki F, Sadeghi J, Khalilipour MM (2018) Soft sensor modeling based on multi-state-dependent parameter models and application for quality monitoring in industrial sulfur recovery process. IEEE Sens J 18(11):4583–4591

    Article  Google Scholar 

  18. Fourati H (2015) Multisensor data fusion: from algorithms and architectural design to applications. CRC Press

    Google Scholar 

  19. Ramos C (2007) Ambient intelligence–a state of the art from artificial intelligence perspective. In: Portuguese conference on artificial intelligence. Springer, Heidelberg, pp 285–295

    Google Scholar 

  20. Stokic D, Kirchhoff U, Sundmaeker H (2006) Ambient intelligence in manufacturing industry: control system point of view. In: The eighth IASTED international conference on control and applications, pp 24–26

    Google Scholar 

  21. Marwala T, Lagazio M (2011) Militarized conflict modeling using computational intelligence. Springer Science & Business Media, London

    Google Scholar 

  22. Xing B, Gao WJ (2016) Innovative computational intelligence: a rough guide to clever algorithms. Springer, Dordrecht, pp 105–121

    Google Scholar 

  23. Ravulakollu KK, Khan MA, Abraham A (2016) Trends in ambient intelligent systems. Springer, Cham

    Google Scholar 

  24. Farringdon J, Nashold S (2005) Continuous body monitoring. In: Ambient intelligence for scientific discovery. Springer, Heidelberg, pp 202–223

    Chapter  Google Scholar 

  25. Chien CF, Hong TY, Guo HZ (2017) A conceptual framework for “Industry 3.5” to empower intelligent manufacturing and case studies. Proc Manuf 11:2009–2017

    Article  Google Scholar 

  26. Augusto JC (2009) Past, present and future of ambient intelligence and smart environments. In: International conference on agents and artificial intelligence. Springer, Heidelberg, pp 3–15

    Google Scholar 

  27. Bibri SE (2015) The human face of ambient intelligence. Atlantis Press

    Google Scholar 

  28. Hwang J, Jeong H, Yoe H (2014) Design and implementation of the intelligent plant factory system based on ubiquitous computing. In: Ambient intelligence-software and applications. Springer, Cham, pp 89–97

    Chapter  Google Scholar 

  29. Wessling F, Gries S, Ollesch J, Hesenius M, Gruhn V (2017) Engineering a cyber-physical intersection management—an experience report. In: European conference on ambient intelligence. Springer, Cham, pp 17–32

    Google Scholar 

  30. Chen YC, Chi HL, Kang SC, Hsieh SH (2011) A smart crane operations assistance system using augmented reality technology. In: Proceedings of 28th international symposium on automation and robotics in construction, Seoul, Korea, pp 643–649

    Google Scholar 

  31. Bassan J, Srinivasan V, Tang A (2011) The augmented mine worker—applications of augmented reality in mining. In: Proceeding second international future mining conference

    Google Scholar 

  32. Sun L, Li Y, Gao J (2016) Architecture and application research of cooperative intelligent transport systems. Proc Eng 137:747–753

    Article  Google Scholar 

  33. Henriques V, Malekian R (2016) Mine safety system using wireless sensor network. IEEE Access 4:3511–3521

    Article  Google Scholar 

  34. Zhong RY, Xu X, Klotz E, Newman ST (2017) Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5):616–630

    Article  Google Scholar 

  35. Zhou J, Li P, Zhou Y, Wang B, Zang J, Meng L (2018) Toward new-generation intelligent manufacturing. Engineering 4(1):11–20

    Article  Google Scholar 

  36. Li RYM (2017) Smart construction safety in road repairing works. Proc Comput Sci 111:301–307

    Article  Google Scholar 

  37. Li RYM (2018) An economic analysis on automated construction safety. Springer, Singapore

    Book  Google Scholar 

  38. Bibri SE (2015) The shaping of ambient intelligence and the internet of things: historico-epistemic, socio-cultural, politico-institutional and eco-environmental dimensions, vol 10. Springer

    Google Scholar 

  39. Bühler C (2009) Ambient intelligence in working environments. In: International conference on universal access in human-computer interaction. Springer, Heidelberg, pp 143–149

    Chapter  Google Scholar 

  40. Lee OJ, Nguyen HL, Jung JE, Um TW, Lee HW (2017) Towards ontological approach on trust-aware ambient services. IEEE Access 5:1589–1599

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Johannesburg, Johannesburg, South Africa

    Wesley Doorsamy

  2. Institute for Intelligent Systems, University of Johannesburg, Johannesburg, South Africa

    Babu Sena Paul

Authors
  1. Wesley Doorsamy
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  2. Babu Sena Paul
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Correspondence to Wesley Doorsamy .

Editor information

Editors and Affiliations

  1. Debesis Education, Derby, UK

    Zaigham Mahmood

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Doorsamy, W., Paul, B.S. (2019). The State and Future of Ambient Intelligence in Industrial IoT Environments. In: Mahmood, Z. (eds) Guide to Ambient Intelligence in the IoT Environment. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-04173-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-04173-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04172-4

  • Online ISBN: 978-3-030-04173-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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