Cluster selection for load balancing in flying ad hoc networks using an optimal low-energy adaptive clustering hierarchy based on optimization approach.
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| Title: | Cluster selection for load balancing in flying ad hoc networks using an optimal low-energy adaptive clustering hierarchy based on optimization approach. |
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| Authors: | Sefati, Seyed Salar1 (AUTHOR) Seyedsalarsefati@gmail.com, Halunga, Simona1 (AUTHOR) simona.halunga@upb.ro, Farkhady, Roya Zareh2 (AUTHOR) Roya.farkhady@roshdiyeh.ac.ir |
| Source: | Aircraft Engineering & Aerospace Technology. 2022, Vol. 94 Issue 8, p1344-1356. 13p. |
| Subjects: | Ant algorithms, Energy consumption of buildings, Ad hoc computer networks, NP-hard problems, Drone aircraft, Metaheuristic algorithms, Wolves, Smart cities |
| Abstract: | Purpose: Flying ad hoc networks (FANETs) have a major effect in various areas such as civil projects and smart cities. The facilities of installation and low cost of unmanned aerial vehicles (UAVs) have created a new challenge for researchers. Cluster head (CH) selection and load balancing between the CH are the most critical issues in the FANETs. For CH selection and load balancing in FANETs, this study used efficient clustering to address both problems and overcome these challenges. This paper aims to propose a novel CH selection and load balancing scheme to solve the low energy consumption and low latency in the FANET system. Design/methodology/approach: This paper tried to select the CH and load balancing with the help of low-energy adaptive clustering hierarchy (LEACH) algorithm and bat algorithm (BA). Load balancing and CH selection are NP-hard problems, so the metaheuristic algorithms can be the best answer for these issues. In the LEACH algorithm, UAVs randomly generate numerical, and these numbers are sorted according to those values. To use the load balancing, the threshold of CH has to be considered; if the threshold is less than 0.7, the BA starts working and begins to find new CH according to the emitted pulses. Findings: The proposed method compares with three algorithms, called bio-inspired clustering scheme FANETs, Grey wolf optimization and ant colony optimization in the NS3 simulator. The proposed algorithm has a good efficiency with respect to the network lifetime, energy consumption and cluster building time. Originality/value: This study aims to extend the UAV group control concepts to include CH selection and load balancing to improve UAV energy consumption and low latency. [ABSTRACT FROM AUTHOR] |
| Copyright of Aircraft Engineering & Aerospace Technology is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 158333042 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Cluster selection for load balancing in flying ad hoc networks using an optimal low-energy adaptive clustering hierarchy based on optimization approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sefati%2C+Seyed+Salar%22">Sefati, Seyed Salar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Seyedsalarsefati@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Halunga%2C+Simona%22">Halunga, Simona</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> simona.halunga@upb.ro</i><br /><searchLink fieldCode="AR" term="%22Farkhady%2C+Roya+Zareh%22">Farkhady, Roya Zareh</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> Roya.farkhady@roshdiyeh.ac.ir</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Aircraft+Engineering+%26+Aerospace+Technology%22">Aircraft Engineering & Aerospace Technology</searchLink>. 2022, Vol. 94 Issue 8, p1344-1356. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Ant+algorithms%22">Ant algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption+of+buildings%22">Energy consumption of buildings</searchLink><br /><searchLink fieldCode="DE" term="%22Ad+hoc+computer+networks%22">Ad hoc computer networks</searchLink><br /><searchLink fieldCode="DE" term="%22NP-hard+problems%22">NP-hard problems</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Wolves%22">Wolves</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+cities%22">Smart cities</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: Flying ad hoc networks (FANETs) have a major effect in various areas such as civil projects and smart cities. The facilities of installation and low cost of unmanned aerial vehicles (UAVs) have created a new challenge for researchers. Cluster head (CH) selection and load balancing between the CH are the most critical issues in the FANETs. For CH selection and load balancing in FANETs, this study used efficient clustering to address both problems and overcome these challenges. This paper aims to propose a novel CH selection and load balancing scheme to solve the low energy consumption and low latency in the FANET system. Design/methodology/approach: This paper tried to select the CH and load balancing with the help of low-energy adaptive clustering hierarchy (LEACH) algorithm and bat algorithm (BA). Load balancing and CH selection are NP-hard problems, so the metaheuristic algorithms can be the best answer for these issues. In the LEACH algorithm, UAVs randomly generate numerical, and these numbers are sorted according to those values. To use the load balancing, the threshold of CH has to be considered; if the threshold is less than 0.7, the BA starts working and begins to find new CH according to the emitted pulses. Findings: The proposed method compares with three algorithms, called bio-inspired clustering scheme FANETs, Grey wolf optimization and ant colony optimization in the NS3 simulator. The proposed algorithm has a good efficiency with respect to the network lifetime, energy consumption and cluster building time. Originality/value: This study aims to extend the UAV group control concepts to include CH selection and load balancing to improve UAV energy consumption and low latency. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Aircraft Engineering & Aerospace Technology is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1108/AEAT-08-2021-0264 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1344 Subjects: – SubjectFull: Ant algorithms Type: general – SubjectFull: Energy consumption of buildings Type: general – SubjectFull: Ad hoc computer networks Type: general – SubjectFull: NP-hard problems Type: general – SubjectFull: Drone aircraft Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Wolves Type: general – SubjectFull: Smart cities Type: general Titles: – TitleFull: Cluster selection for load balancing in flying ad hoc networks using an optimal low-energy adaptive clustering hierarchy based on optimization approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sefati, Seyed Salar – PersonEntity: Name: NameFull: Halunga, Simona – PersonEntity: Name: NameFull: Farkhady, Roya Zareh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 17488842 Numbering: – Type: volume Value: 94 – Type: issue Value: 8 Titles: – TitleFull: Aircraft Engineering & Aerospace Technology Type: main |
| ResultId | 1 |
