Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Artificial Intelligence at the Edge in the Blockchain of Things

  • Conference paper
  • First Online:
Wireless Mobile Communication and Healthcare (MobiHealth 2019)

Abstract

Traditional cloud-centric architectures for Internet-of-Things applications are being replaced by distributed approaches. The Edge and Fog computing paradigms crystallize the concept of moving computation towards the edge of the network, closer to where the data originates. This has important benefits in terms of energy efficiency, network load optimization and latency control. The combination of these paradigms with embedded artificial intelligence in edge devices, or Edge AI, enables further improvements. In turn, the development of blockchain technology and distributed architectures for peer-to-peer communication and trade allows for higher levels of security. This can have a significant impact on data-sensitive and mission-critical applications in the IoT. In this paper, we discuss the potential of an Edge AI capable system architecture for the Blockchain of Things. We show how this architecture can be utilized in health monitoring applications. Furthermore, by analyzing raw data directly at the edge layer, we inherently avoid the possibility of breaches of sensitive information, as raw data is never stored nor transferred outside of the local network.

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
$34.99 /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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Al-Fuqaha, A., et al.: Internet of Things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347–2376 (2015)

    Article  Google Scholar 

  2. Gia, T.N., et al.: Edge AI in smart farming IoT: CNNs at the edge and fog computing with lora. In: IEEE AFRICON-2019 (2019)

    Google Scholar 

  3. Moosavi, S.R., et al.: Session resumption-based end-to-end security for healthcare Internet-of-Things. In: 2015 IEEE CIT, pp. 581–588. IEEE (2015)

    Google Scholar 

  4. Gubbi, J., et al.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)

    Article  Google Scholar 

  5. Moosavi, S.R., et al.: Sea: a secure and efficient authentication and authorization architecture for IoT-based healthcare using smart gateways. Procedia Comput. Sci. 52, 452–459 (2015)

    Article  Google Scholar 

  6. Moosavi, S.R., et al.: End-to-end security scheme for mobility enabled healthcare Internet of Things. Future Gener. Comput. Syst. 64, 108–124 (2016)

    Article  Google Scholar 

  7. Fernandes, E., et al.: Security analysis of emerging smart home applications. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 636–654 (May 2016)

    Google Scholar 

  8. Apthorpe, N., Reisman, D., Feamster, N.: A smart home is no castle: privacy vulnerabilities of encrypted IoT traffic. arXiv preprint arXiv:1705.06805 (2017)

  9. Ali, M., et al.: Intelligent autonomous elderly patient home monitoring system. In: ICC 2019–2019 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2019)

    Google Scholar 

  10. Gia, T.N., et al.: Edge AI in smart farming IoT: CNNs at the edge and fog computing with lora (2019)

    Google Scholar 

  11. Dastjerdi, A.V., Buyya, R.: Fog computing: helping the Internet of Things realize its potential. Computer 49(8), 112–116 (2016)

    Article  Google Scholar 

  12. Gia, T.N., et al.: Energy efficient fog-assisted iot system for monitoring diabetic patients with cardiovascular disease. Future Gener. Comput. Syst. 93, 198–211 (2019)

    Article  Google Scholar 

  13. Ali, M., et al.: Autonomous patient/home health monitoring powered by energy harvesting. In: GLOBECOM 2017–2017 IEEE Global Communications Conference, pp. 1–7. IEEE (2017)

    Google Scholar 

  14. Sarker, V.K., et al.: A survey on lora for IoT: integrating edge computing. In: 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC), pp. 295–300. IEEE (2019)

    Google Scholar 

  15. Queralta, J.P., et al.: Edge-AI in lora-based health monitoring: fall detection system with fog computing and LSTM recurrent neural networks. In: 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), pp. 601–604. IEEE (2019)

    Google Scholar 

  16. Metwaly, A., et al.: Edge computing with embedded AI: thermal image analysis for occupancy estimation in intelligent buildings. In: INTelligent Embedded Systems Architectures and Applications, INTESA@ESWEEK 2019. ACM (2019)

    Google Scholar 

  17. Roman, R., Lopez, J., Mambo, M.: Mobile edge computing, fog et al.: a survey and analysis of security threats and challenges. Future Gener. Comput. Syst. 78, 680–698 (2018)

    Article  Google Scholar 

  18. Conoscenti, M., Vetró, A., De Martin, J.C.: Blockchain for the Internet of Things: a systematic literature review. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), pp. 1–6 (November 2016)

    Google Scholar 

  19. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. White Paper (2008)

    Google Scholar 

  20. Shafagh, H., et al.: Towards blockchain-based auditable storage and sharing of IoT data. In: Proceedings of the 2017 on Cloud Computing Security Workshop, CCSW 2017, pp. 45–50. ACM, New York (2017)

    Google Scholar 

  21. Huh, S., Cho, S., Kim, S.: Managing IoT devices using blockchain platform. In: 2017 19th International Conference on Advanced Communication Technology (ICACT), pp. 464–467. IEEE (2017)

    Google Scholar 

  22. Novo, O.: Blockchain meets IoT: an architecture for scalable access management in IoT. IEEE Internet Things J. 5(2), 1184–1195 (2018)

    Article  Google Scholar 

  23. Tang, B., et al.: A hierarchical distributed fog computing architecture for big data analysis in smart cities. In: Proceedings of the ASE BigData & SocialInformatics 2015, p. 28. ACM (2015)

    Google Scholar 

  24. Dorri, A., et al.: Blockchain for IoT security and privacy: the case study of a smart home. In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 618–623. IEEE (2017)

    Google Scholar 

  25. Christidis, K., Devetsikiotis, M.: Blockchains and smart contracts for the Internet of Things. IEEE Access 4, 2292–2303 (2016)

    Article  Google Scholar 

  26. Kshetri, N.: Can blockchain strengthen the Internet of Things? IT Prof. 19(4), 68–72 (2017)

    Article  Google Scholar 

  27. Nawaz, A., et al.: Edge AI and blockchain for privacy-critical and data-sensitive applications. In: The 12th International Conference on Mobile Computing and Ubiquitous Networking (ICMU) (2019)

    Google Scholar 

  28. Ndibanje, B., Lee, H.-J., Lee, S.-G.: Security analysis and improvements of authentication and access control in the Internet of Things. Sensors 14(8), 14786–14805 (2014)

    Article  Google Scholar 

  29. Bahga, A., Madisetti, V.: Internet of Things: A Hands-on Approach. VPT, New York (2014)

    Google Scholar 

  30. Li, M., Yu, S., Ren, K., Lou, W.: Securing personal health records in cloud computing: patient-centric and fine-grained data access control in multi-owner settings. In: Jajodia, S., Zhou, J. (eds.) SecureComm 2010. LNICST, vol. 50, pp. 89–106. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16161-2_6

    Chapter  Google Scholar 

  31. Mandl, K.D., et al.: Public standards and patients’ control: how to keepelectronic medical records accessible but private. BMJ 322(7281), 283–287 (2001)

    Article  Google Scholar 

  32. Mamoshina, P., et al.: Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget 9(5), 5665 (2018)

    Article  Google Scholar 

  33. Peterson, K., et al.: A blockchain-based approach to health information exchange networks. In: Proceedings of NIST Workshop Blockchain Healthcare, vol. 1, pp. 1–10 (2016)

    Google Scholar 

  34. Irving, G., Holden, J.: How blockchain-timestamped protocols could improve the trustworthiness of medical science. F1000Research 5, 22 (2016)

    Article  Google Scholar 

  35. Dwivedi, A.D., et al.: A decentralized privacy-preserving healthcare blockchain for IoT. Sensors 19(2), 326 (2019)

    Article  Google Scholar 

  36. Simić, M., et al.: A case study IoT and blockchain powered healthcare. In: International Conference on Engineering and Technology (ICET-2017) (June 2017)

    Google Scholar 

  37. Pham, H.L., Tran, T.H., Nakashima, Y.: A secure remote healthcare system for hospital using blockchain smart contract. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–6. IEEE (2018)

    Google Scholar 

  38. Apthorpe, N., et al.: Spying on the smart home: privacy attacks and defenses on encrypted IoT traffic. arXiv preprint arXiv:1708.05044 (2017)

  39. Hernandez, G., et al.: Smart nest thermostat: a smart spy in your home. Black Hat USA, pp. 1–8 (2014)

    Google Scholar 

  40. Albino, V., Berardi, U., Dangelico, R.M.: Smart cities: definitions, dimensions, performance, and initiatives. J. Urban Technol. 22(1), 3–21 (2015)

    Article  Google Scholar 

  41. Lasi, H., et al.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014)

    Article  Google Scholar 

  42. Qingqing, L., et al.: Edge computing for mobile robots: multi-robot feature-based lidar odometry with FPGAs. In: The 12th International Conference on Mobile Computing and Ubiquitous Networking (ICMU) (2019)

    Google Scholar 

  43. Qingqing, L., et al.: Visual odometry offloading in Internet of vehicles with compression at the edge of the network. In: The 12th International Conference on Mobile Computing and Ubiquitous Networking (ICMU) (2019)

    Google Scholar 

  44. Gia, T.N., et al.: Fog computing approach for mobility support in Internet-of-Things systems. IEEE Access 6, 36064–36082 (2018)

    Article  Google Scholar 

  45. Jiang, M., et al.: IoT-based remote facial expression monitoring system with sEMG signal. In: 2016 IEEE Sensors Applications Symposium (SAS), pp. 1–6. IEEE (2016)

    Google Scholar 

  46. Gia, T.N., et al.: Fog computing in healthcare Internet of Things: a case study on ECG feature extraction. In: 2015 IEEE CIT, pp. 356–363. IEEE (2015)

    Google Scholar 

  47. Palacios-Enriquez, A., Ponomaryov, V.: Feature extraction based on wavelet transform using ECG signal. In: 2013 International Kharkov Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves, pp. 632–634. IEEE (2013)

    Google Scholar 

  48. Gia, T.N., et al.: Fog computing in body sensor networks: an energy efficient approach. In: Proceedings of IEEE International Body Sensor Networks Conference (BSN), pp. 1–7 (2015)

    Google Scholar 

  49. Gia, T.N., et al.: Customizing 6LoWPAN networks towards Internet-of-Things based ubiquitous healthcare systems. In: 2014 Norchip, pp. 1–6. IEEE (2014)

    Google Scholar 

  50. Steinberg, C., et al.: A novel wearable device for continuous ambulatory ECG recording: proof of concept and assessment of signal quality. Biosensors 9(1), 17 (2019)

    Article  Google Scholar 

  51. Sarker, V.K., et al.: Portable multipurpose bio-signal acquisition and wireless streaming device for wearables. In: 2017 IEEE Sensors Applications Symposium (SAS), pp. 1–6. IEEE (2017)

    Google Scholar 

  52. Carreiras, C., et al.: BioSPPy: biosignal processing in Python, 2015. Accessed Aug 2019

    Google Scholar 

  53. Jun, T.J., et al.: ECG arrhythmia classification using a 2-D convolutional neural network. arXiv preprint arXiv:1804.06812 (2018)

  54. Dhaou, I.B., et al.: Low-latency hardware architecture for cipher-based message authentication code. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–4. IEEE (2017)

    Google Scholar 

  55. Gia, T.N., et al.: Low-cost fog-assisted health-care IoT system with energy-efficient sensor nodes. In: 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1765–1770. IEEE (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Turku Intelligent Embedded and Robotic Systems (TIERS) Group, Department of Future Technologies, University of Turku, Turku, Finland

    Tuan Nguyen Gia, Anum Nawaz, Jorge Peña Querata, Hannu Tenhunen & Tomi Westerlund

  2. Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China

    Anum Nawaz

Authors
  1. Tuan Nguyen Gia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anum Nawaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jorge Peña Querata
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hannu Tenhunen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tomi Westerlund
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jorge Peña Querata .

Editor information

Editors and Affiliations

  1. School of Computer Science and Informatics, University College Dublin, Dublin 4, Ireland

    Gregory M.P. O'Hare

  2. School of Computer Science and Informatics, University College Dublin, Dublin, Dublin, Ireland

    Michael J. O'Grady

  3. Malawi eHealth Research Center, Cork, Ireland

    John O’Donoghue

  4. University College Cork, Cork, Ireland

    Patrick Henn

Rights and permissions

Reprints and permissions

Copyright information

© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nguyen Gia, T., Nawaz, A., Peña Querata, J., Tenhunen, H., Westerlund, T. (2020). Artificial Intelligence at the Edge in the Blockchain of Things. In: O'Hare, G., O'Grady, M., O’Donoghue, J., Henn, P. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-030-49289-2_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-49289-2_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49288-5

  • Online ISBN: 978-3-030-49289-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation