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Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks

The concept of the internet of things (IoT) motivates us to connect bulk isolated heterogeneous devices to automate report generation without human interaction. Energy-efficient routing algorithms help to prolong the network lifetime of these energy-restricted smart devices that are connected by mea...

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Autores principales: Bilal, Muhammad, Munir, Ehsan Ullah, Alarfaj, Fawaz Khaled
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9331321/
https://www.ncbi.nlm.nih.gov/pubmed/35897974
http://dx.doi.org/10.3390/s22155471
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author Bilal, Muhammad
Munir, Ehsan Ullah
Alarfaj, Fawaz Khaled
author_facet Bilal, Muhammad
Munir, Ehsan Ullah
Alarfaj, Fawaz Khaled
author_sort Bilal, Muhammad
collection PubMed
description The concept of the internet of things (IoT) motivates us to connect bulk isolated heterogeneous devices to automate report generation without human interaction. Energy-efficient routing algorithms help to prolong the network lifetime of these energy-restricted smart devices that are connected by means of wireless sensor networks (WSNs). Current vendor-level advancements enable algorithm-level flexibility to design protocols to concurrently collect multiple application data while enforcing the reduction of energy expenditure to gain commercial success in the industrial stage. In this paper, we propose a hybrid clustering and routing algorithm with threshold-based data collection for heterogeneous wireless sensor networks. In our proposed model, homogeneous and heterogeneous nodes are deployed within specific regions. To reduce unnecessary data transmission, threshold-based conditions are presented to prevent unnecessary transmission when minor or no change is observed in the simulated and real-world applications. We further extend our proposed multi-hop model to achieve more network stability in dense and larger network areas. Our proposed model shows enhancement in terms of load balancing and end-to-end delay as compared to the other threshold-based energy-efficient routing protocols, such as the threshold-sensitive stable election protocol (TSEP), threshold distributed energy-efficient clustering (TDEEC), low-energy adaptive clustering hierarchy (LEACH), and energy-efficient sensor network (TEEN).
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spelling pubmed-93313212022-07-29 Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks Bilal, Muhammad Munir, Ehsan Ullah Alarfaj, Fawaz Khaled Sensors (Basel) Article The concept of the internet of things (IoT) motivates us to connect bulk isolated heterogeneous devices to automate report generation without human interaction. Energy-efficient routing algorithms help to prolong the network lifetime of these energy-restricted smart devices that are connected by means of wireless sensor networks (WSNs). Current vendor-level advancements enable algorithm-level flexibility to design protocols to concurrently collect multiple application data while enforcing the reduction of energy expenditure to gain commercial success in the industrial stage. In this paper, we propose a hybrid clustering and routing algorithm with threshold-based data collection for heterogeneous wireless sensor networks. In our proposed model, homogeneous and heterogeneous nodes are deployed within specific regions. To reduce unnecessary data transmission, threshold-based conditions are presented to prevent unnecessary transmission when minor or no change is observed in the simulated and real-world applications. We further extend our proposed multi-hop model to achieve more network stability in dense and larger network areas. Our proposed model shows enhancement in terms of load balancing and end-to-end delay as compared to the other threshold-based energy-efficient routing protocols, such as the threshold-sensitive stable election protocol (TSEP), threshold distributed energy-efficient clustering (TDEEC), low-energy adaptive clustering hierarchy (LEACH), and energy-efficient sensor network (TEEN). MDPI 2022-07-22 /pmc/articles/PMC9331321/ /pubmed/35897974 http://dx.doi.org/10.3390/s22155471 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bilal, Muhammad
Munir, Ehsan Ullah
Alarfaj, Fawaz Khaled
Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks
title Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks
title_full Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks
title_fullStr Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks
title_full_unstemmed Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks
title_short Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks
title_sort hybrid clustering and routing algorithm with threshold-based data collection for heterogeneous wireless sensor networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9331321/
https://www.ncbi.nlm.nih.gov/pubmed/35897974
http://dx.doi.org/10.3390/s22155471
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