SpringerLink
Log in

Integration of IoT and Edge Computing in Industrial Systems

  • Chapter
  • First Online:
Advances in Computational Mathematics for Industrial System Reliability and Maintainability

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

  • 119 Accesses

Abstract

The ever-increasing demand for real-time processing, low latency, and seamless connectivity in industrial systems has paved the way for integrating the Internet of Things (IoT) and Edge Computing. This chapter goes into the revolutionary amalgamation of these two domains and how their convergence fosters significant improvements in industrial processes and operations. IoT, characterized by its myriad of interconnected devices, sensors, and actuators, has been pivotal in transforming the industrial landscape by enabling continuous monitoring, predictive maintenance, and real-time data acquisition. However, the vast amount of data generated by these devices necessitates efficient processing and analytics capabilities, which, when performed on distant cloud servers, could introduce latencies detrimental to real-time industrial applications. This is where Edge Computing plays a crucial role. By positioning data processing closer to the data source, that is, on the edge of the network, it mitigates the latency issues, reduces the load on bandwidth, and ensures faster decision-making processes. Integrating Edge Computing with IoT devices in industrial systems allows for real-time analytics, local data processing, and swift actuation, crucial for applications like autonomous robotic operations, safety systems, and instantaneous quality checks. Furthermore, this chapter discusses the architectural frameworks, benefits, and challenges accompanying this integration. It elaborates on use cases demonstrating significant enhancements in efficiency, reliability, and productivity in various industrial sectors. The findings suggest that the fusion of IoT and Edge Computing is not merely a technological advancement, but a paradigm shift poised to redefine the future of industrial automation and digital transformation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (France)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 74.89
Price includes VAT (France)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 94.94
Price includes VAT (France)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. binti Mohamad Noor M, Hassan WH (2019) Current research on Internet of Things (IoT) security: a survey. Comput Netw 148:283–294. https://doi.org/10.1016/j.comnet.2018.11.025

  2. Madakam S, Lake V, Lake V, Lake V (2015) Internet of Things (IoT): a literature review. J Comput Commun 3:164

    Article  Google Scholar 

  3. Singh D, Verma A (2018) Inventory management in supply chain. Mater Today Proc 5:3867–3872

    Article  Google Scholar 

  4. Cachon GP (1999) Competitive supply chain inventory management. In: Quantitative models for supply chain management. Springer, pp 111–146

    Google Scholar 

  5. Jones P, Clarke-Hill C, Shears P, Comfort D, Hillier D (2004) Radio frequency identification in the UK: opportunities and challenges. Int J Retail Distrib Manag 32:164–171

    Article  Google Scholar 

  6. Hozdić E (2015) Smart factory for industry 4.0: a review. Int J Mod Manuf Technol 7:28–35

    Google Scholar 

  7. Pasricha S, Ayoub R, Kishinevsky M, Mandal SK, Ogras UY (2020) A survey on energy management for mobile and IoT devices. IEEE Des Test 37:7–24

    Article  Google Scholar 

  8. Al-Ali A-R, Zualkernan IA, Rashid M, Gupta R, AliKarar M (2017) A smart home energy management system using IoT and big data analytics approach. IEEE Trans Consum Electron 63:426–434

    Article  Google Scholar 

  9. Ruan J, Wang Y, Chan FTS, Hu X, Zhao M, Zhu F, Shi B, Shi Y, Lin F (2019) A life cycle framework of green IoT-based agriculture and its finance, operation, and management issues. IEEE Commun Mag 57:90–96

    Article  Google Scholar 

  10. Parvez B, Haidri RA, Verma JK (2020) IoT in agriculture. In: 2020 International conference on computational performance evaluation. IEEE, pp 844–847

    Google Scholar 

  11. Nastic S, Rausch T, Scekic O, Dustdar S, Gusev M, Koteska B, Kostoska M, Jakimovski B, Ristov S, Prodan R (2017) A serverless real-time data analytics platform for edge computing. IEEE Internet Comput 21:64–71

    Article  Google Scholar 

  12. Li H, Yazdi M (2022) Advanced decision-making neutrosophic fuzzy evidence-based best–worst method. In: Li H, Yazdi M (eds) Advanced decision-making methods and applications in system safety and reliability problems: approaches, case studies, multi-criteria decision-making, multi-objective decision-making, fuzzy risk-based models. Springer International Publishing, Cham, pp 153–184. https://doi.org/10.1007/978-3-031-07430-1_9

  13. Liu F, Shi Y (2014) Research on the neurology-based internet architecture. Procedia Comput Sci 30:34–38. https://doi.org/10.1016/j.procs.2014.05.378

    Article  Google Scholar 

  14. Yazdi M (2020) Ignorance-aware safety and reliability analysis: a heuristic approach. Qual Reliab Eng Int 36. https://doi.org/10.1002/qre.2597

  15. Li H, Peng W, Adumene S, Yazdi M (2023) Intelligent reliability and maintainability of energy infrastructure assets. Springer Nature Switzerland

    Google Scholar 

  16. Liang C-JM, Liu J, Luo L, Terzis A, Zhao F (2009) Racnet: a high-fidelity data center sensing network. In: Proceedings of the 7th ACM conference on embedded networked sensor systems , 2009, pp 15–28

    Google Scholar 

  17. Vulimiri A, Michel O, Godfrey PB, Shenker S (2012) More is less: reducing latency via redundancy. In: Proceedings of the 11th ACM workshop on hot topics in networks, pp 13–18

    Google Scholar 

  18. Gu Y, **ang X, Li W, He X (2013) Mode-adaptive decentralized control for renewable DC microgrid with enhanced reliability and flexibility. IEEE Trans Power Electron 29:5072–5080

    Article  Google Scholar 

  19. Gholamizadeh K, Zarei E, Omidvar M, Yazdi M (2022) Fuzzy sets theory and human reliability: review, applications, and contributions. In: Yazdi M (ed) Linguistic methods under fuzzy information in system safety and reliability analysis. Springer International Publishing, Cham, pp 91–137. https://doi.org/10.1007/978-3-030-93352-4_5

  20. Li H, Yazdi M, Huang H-Z, Huang C-G, Peng W, Nedjati A, Adesina KA (2023) A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment. Complex Intell Syst. https://doi.org/10.1007/s40747-023-01002-w

    Article  Google Scholar 

  21. Peddi S, Lanka K, Gopal PRC (2023) Modified FMEA using machine learning for food supply chain. Mater Today Proc. https://doi.org/10.1016/j.matpr.2023.04.353

  22. Chen S-C (2015) Customer value and customer loyalty: is competition a missing link? J Retail Consum Serv 22:107–116. https://doi.org/10.1016/j.jretconser.2014.10.007

  23. Li H, Yazdi M (2022) Develo** failure modes and effect analysis on offshore wind turbines using two-stage optimization probabilistic linguistic preference relations. In: Li H, Yazdi M (eds) Advanced decision-making methods and applications in system safety and reliability problems: approaches. Springer International Publishing, Cham, pp 47–68. https://doi.org/10.1007/978-3-031-07430-1_4

  24. Salvador CB, Arzaghi E, Yazdi M, Jahromi HAF, Abbassi R (2022) A multi-criteria decision-making framework for site selection of offshore wind farms in Australia. Ocean Coast Manag 224:106196. https://doi.org/10.1016/j.ocecoaman.2022.106196

  25. Yang S, Aravind MR, Kaminski J, Pepin H (2018) Opportunities for Industry 4.0 to support remanufacturing. Appl Sci 8. https://doi.org/10.3390/app8071177

  26. Nazim R, A new approach to supplier selection problem: an introduction of AHP-SCOR integrated model. Int J Recent Innov Trends Comput Commun 3:338–346. https://doi.org/10.17762/ijritcc2321-8169.150169

  27. Yazdi M, Soltanali H (2018) Knowledge acquisition development in failure diagnosis analysis as an interactive approach. Int J Interact Des Manuf. https://doi.org/10.1007/s12008-018-0504-6

    Article  Google Scholar 

  28. Shamayleh A, Awad M, Farhat J (2020) IoT based predictive maintenance management of medical equipment. J Med Syst 44:1–12

    Article  Google Scholar 

  29. Mobley RK (2002) An introduction to predictive maintenance. Elsevier

    Google Scholar 

  30. Yazdi M, Khan F, Abbassi R (2023) A dynamic model for microbiologically influenced corrosion (MIC) integrity risk management of subsea pipelines. Ocean Eng 269:113515. https://doi.org/10.1016/j.oceaneng.2022.113515

  31. Yazdi M (2019) Footprint of knowledge acquisition improvement in failure diagnosis analysis. Qual Reliab Eng Int 35. https://doi.org/10.1002/qre.2408

  32. Basseville M, Benveniste A, Gach-Devauchelle B, Goursat M, Bonnecase D, Dorey P, Prevosto M, Olagnon M (1993) In situ damage monitoring in vibration mechanics: diagnostics and predictive maintenance. Mech Syst Signal Process 7:401–423

    Article  Google Scholar 

  33. Sehrawat D, Gill NS (2019) Smart sensors: analysis of different types of IoT sensors. In: 2019 3rd International conference on trends in electronics and informatics, IEEE, pp 523–528

    Google Scholar 

  34. Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2:160

    Article  Google Scholar 

  35. Chen D, Zhao H (2012) Data security and privacy protection issues in cloud computing. In: 2012 International conference on computer science and electronics engineering, IEEE, pp 647–651

    Google Scholar 

  36. Abdalla M, Bellare M, Neven G (2010) Robust encryption. In: Theory cryptography. 7th theory of cryptography conference TCC 2010, Zurich, Switzerland, 9–11 Feb 2010. Proceedings 7, Springer, 2010, pp 480–497

    Google Scholar 

  37. Costin A, Zarras A, Francillon A (2016) Automated dynamic firmware analysis at scale: a case study on embedded web interfaces. In: Proceedings of the 11th ACM Asia conference on computer and communications security, pp 437–448

    Google Scholar 

  38. Zhao Q, Sang X, Metmer H, Lu J, Initiative ADN (2019) Functional segregation of executive control network and frontoparietal network in Alzheimer’s disease. Cortex 120:36–48

    Article  Google Scholar 

  39. Sharafaldin I, Lashkari AH, Hakak S, Ghorbani AA (2019) Develo** realistic distributed denial of service (DDoS) attack dataset and taxonomy. In: 2019 International Carnahan conference on security technology, IEEE, pp 1–8

    Google Scholar 

  40. Mallikarjunan KN, Muthupriya K, Shalinie SM (2016) A survey of distributed denial of service attack. In: 2016 10th International conference on intelligent systems and control, IEEE, pp 1–6

    Google Scholar 

  41. Sjödin DR, Parida V, Leksell M, Petrovic A (2018) Smart factory implementation and process innovation: a preliminary maturity model for leveraging digitalization in manufacturing moving to smart factories presents specific challenges that can be addressed through a structured approach focused on people, processes, and technologies. Res Manag 61:22–31

    Google Scholar 

  42. Tscharntke T, Grass I, Wanger TC, Westphal C, Batáry P (2021) Beyond organic farming–harnessing biodiversity-friendly landscapes. Trends Ecol Evol 36:919–930

    Article  Google Scholar 

  43. Meyer NI (2007) Learning from wind energy policy in the EU: lessons from Denmark, Sweden and Spain. Eur Environ 17:347–362

    Article  Google Scholar 

  44. Mey F, Diesendorf M (2018) Who owns an energy transition? Strategic action fields and community wind energy in Denmark, Energy Res. Soc Sci 35:108–117

    Google Scholar 

  45. de Farias FAC, Dagostini CM, de A. Bicca Y, Falavigna VF, Falavigna A (2020) Remote patient monitoring: a systematic review, Telemed. e-Health 26:576–583

    Google Scholar 

  46. Abdolkhani R, Gray K, Borda A, DeSouza R (2019) Patient-generated health data management and quality challenges in remote patient monitoring. JAMIA Open 2:471–478

    Article  Google Scholar 

  47. Schonberger RJ (2007) Japanese production management: an evolution—with mixed success. J Oper Manag 25:403–419

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering, Macquarie University, Sydney, NSW, Australia

    Mohammad Yazdi

Authors
  1. Mohammad Yazdi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Yazdi .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Yazdi, M. (2024). Integration of IoT and Edge Computing in Industrial Systems. In: Advances in Computational Mathematics for Industrial System Reliability and Maintainability. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-53514-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-53514-7_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53513-0

  • Online ISBN: 978-3-031-53514-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation