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Evidence of power-law behavior in cognitive IoT applications

  • Sujit Bebortta
    ,
  • Dilip Senapati
    ,
  • Nikhil Kumar Rajput
    ,
  • Amit Kumar Singh
    ,
  • Vipin Kumar Rathi
    ,
  • Hari Mohan Pandey
  • Biju Patnaik University of Technology
    ,
  • Ravenshaw University
    ,
  • University of Delhi
    ,
  • Edge Hill University
    ,
  • Technical University of Denmark
    ,
  • University of Padua
Research Output: Contribution to journal Article Peer-review

Abstract

The motivations induced due to the presence of scale-free characteristics of neural systems governed by the well-known power-law distribution of neuronal activities have led to its convergence with the Internet of things (IoT) framework. The IoT is one such framework, where the self-organization of the connected devices is a momentous aspect. The devices involved in these networks inherently relate to the collection of several consolidated devices like the sensory devices, consumer appliances, wearables, and other associated applications, which facilitate a ubiquitous connectivity among the devices. This is one of the most significant prerequisites of IoT systems as several interconnected devices need to be included in the convolution for the uninterrupted execution of the services. Thus, in order to understand the scalability and the heterogeneity of these interconnected devices, the exponent of power-law plays a significant role. In this paper, an analytical framework to illustrate the ubiquitous power-law behavior of the IoT devices is derived. An emphasis regarding the mathematical insights for the characterization of the dynamic behavior of these devices is conceptualized. The observations made in this direction are illustrated through simulation results. Further, the traits of the wireless sensor networks, in context with the contemporary scale-free architecture, are discussed.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 16043-16055

Journal (Volume, Issue Number)

Neural Computing and Applications (Volume 32, Issue 20)

Publication milestones

  • Accepted/In press - 07/01/2020
  • Published - 03/02/2020

Publication status

Published - 03/02/2020

ISSN

0941-0643

External Publication IDs

  • handle.net: 10547/624072
  • Scopus: 85078962427

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