Abstract
Recent researches in IOT bring out smartness with the basis of machine learning techniques. IOT architecture describes the gateways or fog, an analysis engine and an insight layer. These layers are embedded between the cloud and the edge devices. The insight layer employs various learning modules onto the data in the cloud. The fog layer is the most significant layer that improves the efficiency of IOT architecture. Cloud computing and Fog computing is mutually operated. Fog based application should address the issue of the data to be kept in the fog device and to identify the data present in the nearest fog device with the relevant search query. Learning helps to identify the data that are referred frequently and predict the data requirements of the near future. Agents are introduced in the IOT architecture which reacts intelligently using its learning capability and its characteristics improve the system performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barik, R., Dubey, H., Misra, C., Borthakur, D., Constant, N., Sasane, S., Lenka, R., Mishra, B., Das, H., Mankodiya, K.: Fog assisted cloud computing in era of big data and internet-of-things: systems, architectures and applications. In: Cloud Computing for Optimization: Foundations, Applications, Challenges. Springer, Berlin (2018)
Onoriode, U., Gerald, K.: IoT architectural framework: connection and integration framework for iot systems. In: First Workshop on Architectures, Languages and Paradigms for IoT EPTCS, vol. 264, pp. 1–17 (2018)
Tadashi, O., Shinji, K., Takuo, S., Norio, S.: A multi-agent based flexible IoT edge computing architecture harmonizing its control with cloud computing. Int. J. Networking Comput. 8, 218–239 (2018)
Bowen, D., Runhe, H., Xie, Z., Jianhua, M., Weifeng, L.: KID model driven things- edge-cloud computing paradigm for traffic data as a service. IEEE Network 32, 34–41 (2018)
Ju, R., Yi, P., Andrzej, G., Beyah, R.A.: Edge computing for the internet of things. IEEE Network 6–7 (2018)
Paolo, B., Javier, B., Antonio, C., Sajal, D., Luca, F., Alessandro, Z.: A survey on fog computing for the internet of things. J. Pervasive Mobile Comput. 52, 71–99 (2019)
Deng, R., Lu, R., Lai, C., Luan, T.H., Liang, H.: Optimal workload allocation in fog-cloud computing towards balanced delay and power consumption. IEEE Internet Things J. 1171–1181 (2016)
Flavio, B., Rodolfo, M., Jiang, Z., Sateesh, A.: Fog computing and its role in the internet of things. In: ACM MCC, pp. 13–15 (2012)
Xu, Z., Hao, C., Yangchao, Z., Zhan, M., Yiling, X., Haojun, H., Hao, Y., Dapeng, O.: improving cloud gaming experience through mobile edge computing. IEEE Wireless Commun. 1–6 (2019)
Hirofumi, N., Tatsuya, D., Misao, K., Yoji, Y.: Distributed search architecture for object tracking in the internet of things. IEEE Access 99, 60152–60159 (2018)
Redowan, M., Fernando, L.K., Rajkumar, B.: Cloud-fog interoperability in IoT-enabled healthcare solutions. In: Proceedings of Distributed Computing and Networking Conference, pp. 1–10 (2018)
Debanjan, B., Harishchandra, D., Nicholas, C., Leslie, M., Kunal, M.: Smart Fog: Fog computing framework for unsupervised clustering analytics in wearable internet of things. In: 5th IEEE Global Conference on Signal and Information Processing, pp. 472–476 (2017)
Lorenzo, B., Garcia-Rois, J., Li, X., Gonzalez-Castano, J., Fang, Y.: A robust dynamic edge network architecture for the internet-of-things. J. IEEE Network 8–15 (2017)
Sardinaha, J., Milidiu, R.L., Lucena, C., Paranhos, P.: An object oriented framework for building intelligence and learning properties in software agents. J. Object Technol. 2, 85–97 (2003)
Gerhard, W.: Multi Agent Systems—A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge (1999)
Mitchell, T.M.: Machine Learning. McGrawHill, New York (2018)
Mingliu, L., Deshi, L., Gimei, C., Jixuan, Z., Kaitao, M.,, Song, Z.: Sensor Information retrieval from internet of things: representation and indexing. IEEE. Transl. Content Mining IEEE Access 36509–36521 (2018)
Herve, B., Pierre, F., Hovhannes, H., Remide, V.: The worst case behavior of randomized gossip protocols. J. Theor. Comput. Sci. 560, 108–120 (2014)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Joshva Devadas, T., Raja Subramanian, R. (2020). Paradigms for Intelligent IOT Architecture. In: Peng, SL., Pal, S., Huang, L. (eds) Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm. Intelligent Systems Reference Library, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-030-33596-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-33596-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33595-3
Online ISBN: 978-3-030-33596-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)