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BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones
Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordina...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308907/ https://www.ncbi.nlm.nih.gov/pubmed/32492971 http://dx.doi.org/10.3390/s20113134 |
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author | Ganesan, Rajesh Raajini, X. Mercilin Nayyar, Anand Sanjeevikumar, Padmanaban Hossain, Eklas Ertas, Ahmet H. |
author_facet | Ganesan, Rajesh Raajini, X. Mercilin Nayyar, Anand Sanjeevikumar, Padmanaban Hossain, Eklas Ertas, Ahmet H. |
author_sort | Ganesan, Rajesh |
collection | PubMed |
description | Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm. |
format | Online Article Text |
id | pubmed-7308907 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73089072020-06-25 BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones Ganesan, Rajesh Raajini, X. Mercilin Nayyar, Anand Sanjeevikumar, Padmanaban Hossain, Eklas Ertas, Ahmet H. Sensors (Basel) Article Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm. MDPI 2020-06-01 /pmc/articles/PMC7308907/ /pubmed/32492971 http://dx.doi.org/10.3390/s20113134 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ganesan, Rajesh Raajini, X. Mercilin Nayyar, Anand Sanjeevikumar, Padmanaban Hossain, Eklas Ertas, Ahmet H. BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones |
title | BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones |
title_full | BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones |
title_fullStr | BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones |
title_full_unstemmed | BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones |
title_short | BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones |
title_sort | bold: bio-inspired optimized leader election for multiple drones |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308907/ https://www.ncbi.nlm.nih.gov/pubmed/32492971 http://dx.doi.org/10.3390/s20113134 |
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