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An Internet of Agents Architecture for Training and Deployment of Deep Convolutional Models

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Abstract

It is a fact that Artificial Intelligence is having an ever-growing impact on society. That is not just because of advances in computational power and in machine learning models, such as deep neural networks, but also because of the availability of a large volume of heterogeneous data from diverse sources. The Internet of Things (IoT) paradigm is helping gather massive amounts of data from sensor networks that can be used to train and generate complex AI models. However, the training of these models needs not only the data but has high computational requirements. In this scenario there has appeared a new paradigm, called the Internet of Agents (IoA), which allows the inclusion of intelligence and autonomy in IoT devices and networks. This paper presents an IoA architecture that allows the continual and distributed generation and exploitation of convolutional neural networks. Specific protocols for the safe and efficient transmission of models and training pictures are designed. The convolutional model is trained in the cloud and, once reduced, it is distributed and executed in agents located in embedded devices with low computational resources. The architecture has been tested using a convolutional model for the recognition of handwritten character digits based on the MNIST database.

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References

  1. Aguilar, J., & Exposito, E. (2018). Fog computing for the integration of agents and web services in an autonomic reflexive middleware. Service Oriented Computing and Applications, 12(3-4), 333–347.

    Article  Google Scholar 

  2. Al-Turjman, F., & Malekloo, A. (2019). Smart Parking in IoT-enabled Cities: A Survey. Sustainable Cities and Society, 49, 101608. https://doi.org/10.1016/j.scs.2019.101608.

    Article  Google Scholar 

  3. Aref, A., & Tran, T. (2019). An integrated trust establishment model for the Internet of Agents. Knowledge and Information Systems, 62(1), 1–27.

    Google Scholar 

  4. Bartolomeu, P., Alam, M., Ferreira, J., & Fonseca, J. (2016). Survey on low power real-time wireless MAC protocols. Journal of Network and Computer Applications, 75(C November 2016), 293–316.

    Article  Google Scholar 

  5. Bui Khac Hoai, N., & Jung, J. (2017). Internet of Agents framework for connected vehicles: A case study on distributed traffic control system. Journal of Parallel and Distributed Computing., 116, 89–95.

    Google Scholar 

  6. Chi, T., & Chen, M. (2019). A frequency hopping method for spatial RFID/wifi/bluetooth scheduling in agricultural IoT. Wireless Networks, 25(2), 805–817.

    Article  Google Scholar 

  7. Chooruang, K. (2016). Komkrit and p. Mangkalakeeree wireless heart rate monitoring system using MQTT. Procedia Computer Science, 86, 160–163.

    Article  Google Scholar 

  8. Deng, L., Li, D., & Yao, X. (2018). Mobile network intrusion detection for IoT system based on transfer learning algorithm. Cluster Computing, 22(4), 9889–9904.

    Google Scholar 

  9. Dong, S., Gao, Z., Pirbhulal, S., & et al. (2019). IoT-based 3D convolution for video salient object detection. Neural Computing and Applications, 32(3), 735–746.

    Article  Google Scholar 

  10. Dourado, C., & et al. (2019). A new approach for mobile robot localization based on an online IoT system. Future Generation Computer Systems, 100, 859–881.

    Article  Google Scholar 

  11. Erlank, A., & Bridges, C. (2017). A hybrid real-time agent platform for fault-tolerant, embedded applications. Autonomous Agents and Multi-Agent Systems, 32(2), 252–274.

    Article  Google Scholar 

  12. Hu, P. (2015). A system architecture for software-defined industrial internet of things. In 2015 IEEE International conference on ubiquitous wireless broadband. IEEE, pp 1–5.

  13. Hwang, H.-C., Park, J., & Jin, J.S. (2016). Design and implementation of a reliable message transmission system based on MQTT protocol in IoT. Wireless Personal Communications, 91, 1765–1777.

    Article  Google Scholar 

  14. Kim, H., & Kim, J. (2017). A load balancing scheme based on deep-learning in IoT. Cluster Computing, 20, 873–878.

    Article  Google Scholar 

  15. Kizilkaya, B., Caglar, M., Al-Turjman, F., & Ever, E. (2018). Binary search tree based hierarchical placement algorithm for IoT based smart parking applications. Internet of Things, 5, 71–83.

    Article  Google Scholar 

  16. Lohachab, A. (2019). ECC based inter-device authentication and authorization scheme using MQTT for IoT networks. Journal of Information Security and Applications, 46, 1–12. https://doi.org/10.1016/j.jisa.2019.02.005.

    Article  Google Scholar 

  17. O’Brien, P.D., & Nicol, R.C. (1998). FIPA — Towards a standard for software agents. BT Technology Journal, 16(3), 51–59.

    Article  Google Scholar 

  18. Ozawa, S., Ban, T., Hashimoto, N., Nakazato, J., & Shimamura, J. (2019). A study of IoT malware activities using association rule learning for darknet sensor data, (Vol. 19.

  19. Pico-Valencia, P., Holgado-Terriza, J., & Senso, J. (2019). Towards an internet of agents model based on linked open data approach. Autonomous Agents and Multi-Agent Systems, 33, 84–131.

    Article  Google Scholar 

  20. Pitt, J., & Mamdani, A. (1999). Some remarks on the semantics of FIPA’s agent communication language. Autonomous Agents and Multi-Agent Systems, 2(4), 333–356.

    Article  Google Scholar 

  21. Prada, M., Reguera, P., Alonso, S., & et al. (2016). Communication with resource-constrained devices through MQTT for control education. IFAC-PapersOnLine, 49, 150–155. https://doi.org/10.1016/j.ifacol.2016.07.169.

    Article  MathSciNet  Google Scholar 

  22. Pramudianto, F., Simon, J., Eisenhauer, M., Khaleel, H., Pastrone, C., & et al. (2013). Prototyping the Internet of Things for the future factory using a SOA-based middleware and reliable WSNs, 2013 IEEE 18th Conference on Emerging Technologies and Factory Automation, p. 14.

  23. Priyan, M.K., & Gandhi, U. (2017). A novel three-tier internet of things architecture with machine learning algorithm for early detection of heart diseases. Computers and Electrical Engineering, 65, 222–235.

    Google Scholar 

  24. Rawal, S. (2017). Iot based smart irrigation system. International Journal of Computer Applications., 159, 7–11.

    Article  Google Scholar 

  25. Sanchez, L.B., Line, & Aguilar, J. (2019). An approach to support traffic engineering in IPv6 networks based on IPv6 facilities. Telecommunication Systems, 72(1), 11–27. https://doi.org/10.1007/s11235-018-00543-7.

    Article  Google Scholar 

  26. Satpathy, S., Mohan, P., Das, S., & et al. (2019). A new healthcare diagnosis system using an IoT-based fuzzy classifier with FPGA. The Journal of Supercomputing.

  27. Shapsough, S., Takrouri, M., Dhaouadi, R., & Zualkernan, I. (2018). Using IoT and smart monitoring devices to optimize the efficiency of large-scale distributed solar farms. Wireless Networks.

  28. Sharma, N., & Singh, A. (2019). Diabetes Detection and Prediction Using Machine Learning/IoT: A Survey: Second International Conference, ICAICR 2018, Shimla, India, July 14–15, 2018, Revised Selected Papers, Part I.

  29. Terzidou, T., Tsiatsos, T., & Apostolidis, H. (2018). Architecture and interaction protocol for pedagogical-empathic agents in 3D virtual learning environments. Multimedia Tools and Applications, 77(20), 27661–27684.

    Article  Google Scholar 

  30. Wang, D., Guo, Q., Song, Y., Gao, S., & Li, Y. (2019). Application of multiscale learning neural network based on CNN in bearing fault diagnosis. Journal of Signal Processing Systems, 91(10), 1205–1217.

    Article  Google Scholar 

  31. Yacchirema, D., Puga, J., Palau, C., & Esteve, M. (2019). Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Personal and Ubiquitous Computing, 23(5-6), 801–817.

    Article  Google Scholar 

  32. Zeybek, T., Hwa Chang, C., & Yang, Z. (2019). An IoT implementation for manufacturing using Wi-Fi, 6loWPAN, and MQTT. In Proceedings of the 2019 international conference on embedded wireless systems and networks (EWSN ’19). Junction Publishing, USA, pp 362–366.

  33. Zhang, H., Lu, Z., Xu, K., & et al. (2019). Artificial Intelligence Platform for Mobile Service Computing. Journal of Signal Processing Systems, 91(10), 1179–1189.

    Article  Google Scholar 

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Authors and Affiliations

  1. Escuela Superior de Informatica, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain

    Luis Rodriguez-Benitez, Carlos Córdoba Ruiz, Luis Cabañero Gómez, Ramón Hervás & Luis Jimenez-Linares

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  1. Luis Rodriguez-Benitez
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  2. Carlos Córdoba Ruiz
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  3. Luis Cabañero Gómez
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Correspondence to Luis Rodriguez-Benitez.

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Rodriguez-Benitez, L., Ruiz, C.C., Gómez, L.C. et al. An Internet of Agents Architecture for Training and Deployment of Deep Convolutional Models. J Sign Process Syst 94, 283–291 (2022). https://doi.org/10.1007/s11265-020-01602-6

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  • DOI: https://doi.org/10.1007/s11265-020-01602-6

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