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A Framework of Learning and Communication with IoT-Enabled Ecosystem

  • Jay R. BhatnagarEmail author
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 174)

Abstract

Internet of Things or IoT is fast emerging as the ubiquitous data-directed solution for autonomous all-machine networks. In this article we propose an IoT-enabled framework that performs two interlinked data-driven roles—communicating intelligence and intelligent communication. The first role points to harvesting multi-variate data which varies in space-time for knowledge and features whereas the latter role deals with control and inference derived from the sensed data. We integrate these roles in smart architecture and apply it to probe critical problems in domains such as Transport, Energy, Environment and Telecom. The discussion on IoT-enabled system proposes novelties such as digital divide of supply versus demand and workflow; graph-based learning of state-space and formulates energy efficiency of IoT node. The case study for vehicular traffic reveals that IoT-enabled system offers reliable, easy to scale, AI integrated and efficient communication that can complement performance with the existing networks.

Keywords

IoT network Deep learning Graphical model Mega city Vehicular traffic Energy efficiency Environment Communication theory 

Notes

Acknowledgements

In writing of the chapter gratitude is due to suggestions of the Editors of this volume. This article treats communication theory at graduate level showcasing bits of logic, architecture and switching methods that can be useful in IoT-enabled solution. Sincere thanks are due to Professor R G. Gallager for unmatched insights and inspirational work in data and communication sciences. Author thanks learning in ecosystems over the years with Communication Sciences Institute USC; GCATT, Georgia Tech. and EE & Statistics, UC Riverside. Author sincerely acknowledges the generous help of Er. Ravindra Prakash Bhatnagar in the preparing of manuscript.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science and EngineeringPresidency UniversityBengaluruIndia

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