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Putting Intelligence into Things: An Overview of Current Architectures

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Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops (AIAI 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 677))

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Abstract

In the era of the Internet of Things (IoT), billions of sensors collect data from their environment and process it to enable intelligent decisions at the right time. However, transferring massive amounts of disparate data in complex environments is a challenging issue. The convergence of Artificial Intelligence (AI) and the Internet of Things has breathed new life into IoT operations and human-machine interaction. Resource-constrained IoT devices typically need more data storage and processing capacity to build modern AI models. The intuitive solution integrates cloud computing technology with AIoT and leverages cloud-side servers’ powerful and flexible processing and storage capacity. This paper briefly introduces IoT and AIoT architectures in the context of cloud computing, fog computing and more. Finally, an overview of the NEMO [1] concept is presented. The NEMO project aims to establish itself as the “game changer” of AIoT-Edge-Cloud Continuum by bringing intelligence closer to data, making AI-as-a-Service an integral part of self-organizing networks orchestrating micro-service execution.

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References

  1. NEMO, Horizon EU - funded project, GA No.101070118. https://meta-os.eu/

  2. Peres, R.S., Jia, X., Lee, J., Sun, K.A.W.J.: Industrial artificial intelligence in industry 4.0 - systematic review, challenges and outlook. IEEE Access 8, 220121–220139 (2020). https://doi.org/10.1109/ACCESS.2020.3042874

  3. Ramamurthy, H., Prabhu, B.S., Gadh, R., Madni, A.M.: Wireless industrial monitoring and control using a smart sensor platform. IEEE Sens. J. 7(5), 611–618 (2007) https://doi.org/10.1109/JSEN.2007.894135

  4. The European Cloud, Edge and IoT Continuum Initiative (2023). https://eucloudedgeiot.eu/the-eu-vision-on-the-cei-continuum/

  5. Shi, W., Dustdar, S.: The promise of edge computing. Computer 49(5), 78–81 (2016). https://doi.org/10.1109/MC.2016.145

    Article  Google Scholar 

  6. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)

    Article  Google Scholar 

  7. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the ACM 1st Ed. of the MCC Workshop on Mobile Cloud Computing (MCC’12), pp. 13–16. ACM (2012)

    Google Scholar 

  8. Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE Internet Things J. 5(1), 450–465 (2018)

    Article  Google Scholar 

  9. Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Network. 24(5), 2795–2808 (2016). https://doi.org/10.1109/TNET.2015.2487344

    Article  Google Scholar 

  10. Liu, J., Mao, Y., Zhang, J., Letaief, K.B.: Delay-optimal computation task scheduling for mobile-edge computing systems. In: Proceedings of the 2016 IEEE International Symposium on Information Theory (ISIT’16), pp. 1451–1455. IEEE (2016)

    Google Scholar 

  11. Yang, L., Liu, B., Cao, J., Sahni, Y., Wang, Z.: Joint computation partitioning and resource allocation for latency sensitive applications in mobile edge clouds. In: Proceedings of the 2017 IEEE 10th International Conference on Cloud Computing (CLOUD’17), pp. 246–253. IEEE (2017)

    Google Scholar 

  12. Yousefpour, A., Ishigaki, G., Jue, J.P.: Fog Computing: towards minimizing delay in the internet of things. In: Proceedings of the IEEE International Conference on Edge Computing (EDGE’17), pp. 17–24. IEEE (2017)

    Google Scholar 

  13. Zhang, H., Xiao, Y., Bu, S., Niyato, D., Yu, F.R., Han, Z.: Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining stackelberg game and matching. IEEE Internet Things J. 4(5), 1204–1215 (2017)

    Article  Google Scholar 

  14. Liu, J., Zhang, A.Q.: Offloading schemes in mobile edge computing for ultra-reliable low latency communications. IEEE Access 6, 12825–12837 (2018)

    Article  Google Scholar 

  15. Li, L., Guan, Q., Jin, L., Guo, M.: Resource allocation and task offloading for heterogeneous real-time tasks with uncertain duration time in a fog queueing system. IEEE Access 7, 9912–9925 (2019)

    Article  Google Scholar 

  16. Mashal, I., Alsaryrah, O., Chung, T.Y., Yang, C.Z., Kuo, W.H., Agrawal, D.P.: Choices for interaction with things on internet and underlying issues. Ad Hoc Netw. 28, 68–90 (2015)

    Article  Google Scholar 

  17. International Telecommunication Union – Telecommunication Standardization Sector (ITU-T): Recommendation Y4000/Y.2060 (06/12): Overview of the Internet of Things (2012). https://www.itu.int/rec/T-REC-Y.2060-201206-I

  18. Burhan, M., Rehman, R.A., Khan, B., Kim, B.-S.: IoT elements, layered architectures and security issues: a comprehensive survey. Sensors (MDPI) 18(9), 2796. https://doi.org/10.3390/s18092796

  19. Antão, L., Pinto, R., Reis, J., Gonçalves, G.: Requirements for testing and validating the industrial internet of things. In: Proceedings of the 2018 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW’18), pp. 110–115. IEEE (2018)

    Google Scholar 

  20. Sanzgiri, A., Dasgupta, D.: Classification of insider threat detection techniques. In: Proceedings of the 11th Annual Cyber and Information Security Research Conference (CISRC’16), pp. 1–4. ACM (2016). https://doi.org/10.1145/2897795.2897799

  21. Sethi, P., Sarangi, S.R.: Internet of things: architectures, protocols, and applications. J. Electr. Comput. Eng. 1–25 (2017). https://doi.org/10.1155/2017/9324035

  22. Viel, F., Silva, L.A., Valderi Leithardt, R.Q., Zeferino, C.A.: Internet of things: concepts, architectures and technologies. In: Proceedings of the 2018 13th IEEE International Conference on Industry Applications (INDUSCON’18), pp. 909–916. IEEE (2018). https://doi.org/10.1109/INDUSCON.2018.8627298

  23. Sopelsa Neto, N.F., et al.: A study of multilayer perceptron networks applied to classification of ceramic insulators using ultrasound. Appl. Sci. 11(4), 1592 (2021). https://doi.org/10.3390/app11041592

  24. Leithardt, V., Santos, D., Silva, L., Viel, F., Zeferino, C., Silva, J.: A solution for dynamic management of user profiles in IoT environments. IEEE Lat. Am. Trans. 18(7), 1193–1199 (2020). https://doi.org/10.1109/TLA.2020.9099759

    Article  Google Scholar 

  25. Stefenon, S.F., Kasburg, C., Nied, A., Klaar, A.C.R., Ferreira, F.C.S., Branco, N.W.: Hybrid deep learning for power generation forecasting in active solar trackers. IET Gener. Transm. Distrib. 14(23), 5667–5674 (2020)

    Article  Google Scholar 

  26. Kasburg, C., Stefenon, S.F.: Deep learning for photovoltaic generation forecast in active solar trackers. IEEE Lat. Am. Trans. 17(12), 2013–2019 (2019)

    Article  Google Scholar 

  27. Guo, T., Yu, K., Aloqaily, M., Wan, S.: Constructing a prior-dependent graph for data clustering and dimension reduction in the edge of AIoT. Futur. Gener. Comput. Syst. 128, 381–394 (2021)

    Article  Google Scholar 

  28. Xiong, Z., Cai, Z., Takabi, D., Li, W.: Privacy threat and defense for federated learning with non-i.i.d. data in AIoT. IEEE Trans. Ind. Inform. 18(2), 1310–1321 (2022). https://doi.org/10.1109/TII.2021.3073925

  29. Muniz, R.N., et al.: Tools for measuring energy sustainability: a comparative review. Energies (MDPI) 13(9), 2366 (2020). https://doi.org/10.3390/en13092366

  30. da Silva, L.D.L., Pereira, T.F., Leithardt, V.R.Q., Seman, L.O., Zeferino, C.A.: Hybrid impedance-admittance control for upper limb exoskeleton using electromyography. Appl. Sci. (MDPI) 10(20), 7146 (2020). https://doi.org/10.3390/app10207146

    Article  Google Scholar 

  31. Kaur, J., Khan, M.A., Iftikhar, M., Imran, M., Emad Ul Haq, Q.: Machine learning techniques for 5G and beyond. IEEE Access 9, 23472–23488 (2021). https://doi.org/10.1109/ACCESS.2021.3051557

  32. Lopes, H., Pires, I.M., Sánchez San Blas, H., García-Ovejero, R., Leithardt, V.: PriADA: management and adaptation of information based on data privacy in public environments. Computers (MDPI) 9(4), 77 (2020). https://doi.org/10.3390/computers9040077

  33. Pinto, H., Américo, J., Leal, O., Stefenon, S.: Development of measurement device and data acquisition for electric vehicle. Revista Gestão Inovação e Tecnologias 11(11), 5809–5822 (2021). https://doi.org/10.7198/geintec.v11i1.1203

    Article  Google Scholar 

  34. Stefenon, S.F., Furtado Neto, C.S., Coelho, T.S., Nied, A., Yamaguchi, C.K., Yow, K.-C.: Particle swarm optimization for design of insulators of distribution power system based on finite element method. Electr. Eng. 104(2), 615–622 (2021). https://doi.org/10.1007/s00202-021-01332-3

    Article  Google Scholar 

  35. Permutive (2023). https://support.permutive.com/hc/en-us/articles/360012435279

  36. Xiao, Y., Jia, Y., Liu, C., Cheng, X., Yu, J., Lv, W.: Edge computing security: state of the art and challenges. Proc. IEEE 107(8), 1608–1631 (2019). https://doi.org/10.1109/JPROC.2019.2918437

    Article  Google Scholar 

  37. Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE Internet Things J. 5(1), 450–465 (2018). https://doi.org/10.1109/JIOT.2017.2750180

    Article  Google Scholar 

  38. Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: a platform for internet of things and analytics. In: Bessis, N., Dobre, C. (eds.) Big Data and Internet of Things: A Roadmap for Smart Environments. SCI, vol. 546, pp. 169–186. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05029-4_7

    Chapter  Google Scholar 

  39. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J.: Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107(8), 1738–1762 (2019). https://doi.org/10.1109/JPROC.2019.2918951

    Article  Google Scholar 

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Acknowledgements

Part of this paper has been based on the context of the “NEMO” (“Next Generation Meta Operating System”) Project. This project has received funding from the EU Horizon Europe research and innovation Programme under Grant Agreement No. 101070118.

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Authors and Affiliations

  1. Hellenic Telecommunications Organization (OTE) S.A., 99 Kifissias Avenue, 15124, Maroussi-Athens, Greece

    Maria Belesioti, Ioannis P. Chochliouros, Theodore Zahariadis, Charalabos Skianis & Enric Pages Montanera

  2. COSMOTE Mobile Telecommunications S.A., Athens, Greece

    Panagiotis Dimas, Manolis Sofianopoulos, Theodore Zahariadis, Charalabos Skianis & Enric Pages Montanera

Authors
  1. Maria Belesioti
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  2. Ioannis P. Chochliouros
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  3. Panagiotis Dimas
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  4. Manolis Sofianopoulos
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  5. Theodore Zahariadis
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  6. Charalabos Skianis
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  7. Enric Pages Montanera
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Corresponding author

Correspondence to Ioannis P. Chochliouros .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  4. Hellenic Telecom Organization OTE, Athens, Greece

    Ioannis Chochliouros

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Belesioti, M. et al. (2023). Putting Intelligence into Things: An Overview of Current Architectures. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_8

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  • DOI: https://doi.org/10.1007/978-3-031-34171-7_8

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