Authors
Bebortta, SujitSenapati, Dilip
Rajput, Nikhil Kumar
Singh, Amit Kumar
Rathi, Vipin Kumar
Pandey, Hari Mohan
Jaiswal, Amit Kumar
Qian, Jia
Tiwari, Prayag
Affiliation
College of Engineering and Technology, BhubaneshwarRavenshaw University
University of Delhi
Edge Hill University
University of Bedfordshire
Technical University of Denmark
University of Padova
Issue Date
2020-02-03Subjects
Internet of things
Metadata
Show full item recordAbstract
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.Citation
Bebortta S, Senapati D, Rajput NK, Singh AK, Rathi VK, Pandey HM, Jaiswal AK, Qian J, Tiwari P (2020) 'Evidence of power-law behavior in cognitive IoT applications', Neural Computing and Applications, 32 (20), pp.16043-16055.Publisher
SpringerAdditional Links
https://link.springer.com/article/10.1007/s00521-020-04705-0Type
ArticleLanguage
enISSN
0941-0643EISSN
1433-3058ae974a485f413a2113503eed53cd6c53
10.1007/s00521-020-04705-0