IoT Network Model with Multimodal Node Distribution and Data-Collecting Mechanism Using Mobile Clustering Nodes-Reference-Cited by-同舟云学术

IoT Network Model with Multimodal Node Distribution and Data-Collecting Mechanism Using Mobile Clustering Nodes

Published:2023-03-16 Issue:6 Volume:12 Page:1410
ISSN:2079-9292
Container-title:Electronics
language:en
Short-container-title:Electronics

Author:

Vorobyova Darya¹,Muthanna Ammar¹²^ORCID,Paramonov Alexander¹^ORCID,Markelov Oleg A.³^ORCID,Koucheryavy Andrey¹,Ali Gauhar⁴^ORCID,ElAffendi Mohammed⁴^ORCID,Abd El-Latif Ahmed A.⁴⁵^ORCID

Affiliation:

1. Department of Communication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, St. Petersburg 193232, Russia

2. Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya, Moscow 117198, Russia

3. Centre for Digital Telecommunication Technologies, Saint Petersburg Electrotechnical University “LETI”, 5F Professor Popov Street, St. Petersburg 197022, Russia

4. EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia

5. Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt

Abstract

In this paper, the novel study of an Internet of Things (IoT) network model with multimodal node distribution and a data-collecting mechanism using mobile clustering nodes is presented. The aim of this work is to introduce the problem of organizing the mobile cluster head IoT network with a heterogeneous distribution node in the service area with multimodal distribution nodes. A new method for clustering a heterogeneous network is proposed, which makes it possible to efficiently identify clusters that differ in terms of the density of nodes. This makes it possible to choose the speed of the mobile cluster head in accordance with the density in each cluster. The proposed method uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm. One of the benefits of our proposed model is the increase in the efficiency of using a mobile cluster head. The new solution can be used to organize data collection in the IoT.

Funder

Prince Sultan University

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Link

https://www.mdpi.com/2079-9292/12/6/1410/pdf

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