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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| Publicado en: | Aircraft Engineering and Aerospace Technology vol. 94, no. 8 (2022), p. 1344-1356 |
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Emerald Group Publishing Limited
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1108/AEAT-08-2021-0264 |2 doi | |
| 035 | |a 2697272914 | ||
| 045 | 2 | |b d20220101 |b d20221231 | |
| 084 | |a 46129 |2 nlm | ||
| 100 | 1 | |a Seyed Salar Sefati |u Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, Bucharest, Romania | |
| 245 | 1 | |a Cluster selection for load balancing in flying <i>ad hoc</i> networks using an optimal low-energy adaptive clustering hierarchy based on optimization approach | |
| 260 | |b Emerald Group Publishing Limited |c 2022 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Global positioning systems--GPS | ||
| 653 | |a Communication | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Clustering | ||
| 653 | |a Optimization | ||
| 653 | |a Ad hoc networks | ||
| 653 | |a Network latency | ||
| 653 | |a Algorithms | ||
| 653 | |a Literature reviews | ||
| 653 | |a Surveillance | ||
| 653 | |a Ant colony optimization | ||
| 653 | |a Energy consumption | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Load balancing | ||
| 653 | |a Protocol | ||
| 700 | 1 | |a Halunga, Simona |u Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, Bucharest, Romania | |
| 700 | 1 | |a Roya Zareh Farkhady |u Department of Computer Engineering, Institute of Higher Education Roshdiyeh, Tabriz, Iran | |
| 773 | 0 | |t Aircraft Engineering and Aerospace Technology |g vol. 94, no. 8 (2022), p. 1344-1356 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2697272914/abstract/embedded/KOLE7RPJVUKQAXRX?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2697272914/fulltext/embedded/KOLE7RPJVUKQAXRX?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2697272914/fulltextPDF/embedded/KOLE7RPJVUKQAXRX?source=fedsrch |