Evidence of power-law behavior in cognitive IoT applications
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S.I. : APPLYING ARTIFICIAL INTELLIGENCE TO THE INTERNET OF THINGS
Evidence of power-law behavior in cognitive IoT applications Sujit Bebortta1 • Dilip Senapati2 • Nikhil Kumar Rajput3 • Amit Kumar Singh3 • Vipin Kumar Rathi3 Hari Mohan Pandey4 • Amit Kumar Jaiswal5 • Jia Qian6 • Prayag Tiwari7
•
Received: 15 October 2019 / Accepted: 7 January 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Internet of things Wireless sensor networks Power-law Scalability Interconnectivity
1
Department of Computer Science and Engineering, College of Engineering and Technology, Bhubaneshwar 751003, India
& Hari Mohan Pandey [email protected]
2
Department of Computer Science, Ravenshaw University, Cuttack 753003, India
& Prayag Tiwari [email protected]
3
Department of Computer Science, Ramanujan College, University of Delhi, New Delhi 110019, India
Sujit Bebortta [email protected]
4
Department of Computer Science, Edge Hill University, Ormskirk, UK
Nikhil Kumar Rajput [email protected]
5
Institute for Research in Applicable Computing, University of Bedfordshire, Luton, UK
Amit Kumar Singh [email protected]
6
Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark
Vipin Kumar Rathi [email protected]
7
Department of Information Engineering, University of Padova, Padua, Italy
& Dilip Senapati [email protected]
Amit Kumar Jaiswal [email protected] Jia Qian [email protected]
123
Neural Computing and Applicat
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