Intelligent energy aware optimization protocol for vehicular adhoc networks
Abstract Vehicular adhoc network (VANET) plays a vital role in smart transportation. VANET includes a set of vehicles that communicate with one another via wireless links. The vehicular communication in VANET necessitates an intelligent clustering protocol to maximize energy efficiency. Since energy...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-35042-6 |
| _version_ | 1797811571225788416 |
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| author | Mohamed Elhoseny Ibrahim M. El-Hasnony Zahraa Tarek |
| author_facet | Mohamed Elhoseny Ibrahim M. El-Hasnony Zahraa Tarek |
| author_sort | Mohamed Elhoseny |
| collection | DOAJ |
| description | Abstract Vehicular adhoc network (VANET) plays a vital role in smart transportation. VANET includes a set of vehicles that communicate with one another via wireless links. The vehicular communication in VANET necessitates an intelligent clustering protocol to maximize energy efficiency. Since energy acts as an essential factor in the design of VANET, energy-aware clustering protocols depending upon metaheuristic optimization algorithms are required to be developed. This study introduces an intelligent energy-aware oppositional chaos game optimization-based clustering (IEAOCGO-C) protocol for VANET. The presented IEAOCGO-C technique aims to select cluster heads (CHs) in the network proficiently. The proposed IEAOCGO-C model constructs clusters based on oppositional-based learning (OBL) with the chaos game optimization (CGO) algorithm to improve efficiency. Besides, it computes a fitness function involving five parameters, namely throughput (THRPT), packet delivery ratio (PDR), network lifetime (NLT), end to end delay (ETED) and energy consumption (ECM). The experimental validation of the proposed model is accomplished, and the outcomes are studied in numerous aspects with existing models under several vehicles and measures. The simulation outcomes reported the enhanced performance of the proposed approach over the recent technologies. As a result, it has resulted in maximal NLT (4480), minimal ECM (65.6), maximal THRPT (81.6), maximal PDR (84.5), and minimal ETED (6.7) as average values over the other methods under all vehicle numbers. |
| first_indexed | 2024-03-13T07:24:41Z |
| format | Article |
| id | doaj.art-c7a957fa15a94dd687d4485ce23d5be4 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T07:24:41Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c7a957fa15a94dd687d4485ce23d5be42023-06-04T11:27:22ZengNature PortfolioScientific Reports2045-23222023-06-0113111410.1038/s41598-023-35042-6Intelligent energy aware optimization protocol for vehicular adhoc networksMohamed Elhoseny0Ibrahim M. El-Hasnony1Zahraa Tarek2Faculty of Computers and Information, Mansoura UniversityFaculty of Computers and Information, Mansoura UniversityFaculty of Computers and Information, Mansoura UniversityAbstract Vehicular adhoc network (VANET) plays a vital role in smart transportation. VANET includes a set of vehicles that communicate with one another via wireless links. The vehicular communication in VANET necessitates an intelligent clustering protocol to maximize energy efficiency. Since energy acts as an essential factor in the design of VANET, energy-aware clustering protocols depending upon metaheuristic optimization algorithms are required to be developed. This study introduces an intelligent energy-aware oppositional chaos game optimization-based clustering (IEAOCGO-C) protocol for VANET. The presented IEAOCGO-C technique aims to select cluster heads (CHs) in the network proficiently. The proposed IEAOCGO-C model constructs clusters based on oppositional-based learning (OBL) with the chaos game optimization (CGO) algorithm to improve efficiency. Besides, it computes a fitness function involving five parameters, namely throughput (THRPT), packet delivery ratio (PDR), network lifetime (NLT), end to end delay (ETED) and energy consumption (ECM). The experimental validation of the proposed model is accomplished, and the outcomes are studied in numerous aspects with existing models under several vehicles and measures. The simulation outcomes reported the enhanced performance of the proposed approach over the recent technologies. As a result, it has resulted in maximal NLT (4480), minimal ECM (65.6), maximal THRPT (81.6), maximal PDR (84.5), and minimal ETED (6.7) as average values over the other methods under all vehicle numbers.https://doi.org/10.1038/s41598-023-35042-6 |
| spellingShingle | Mohamed Elhoseny Ibrahim M. El-Hasnony Zahraa Tarek Intelligent energy aware optimization protocol for vehicular adhoc networks Scientific Reports |
| title | Intelligent energy aware optimization protocol for vehicular adhoc networks |
| title_full | Intelligent energy aware optimization protocol for vehicular adhoc networks |
| title_fullStr | Intelligent energy aware optimization protocol for vehicular adhoc networks |
| title_full_unstemmed | Intelligent energy aware optimization protocol for vehicular adhoc networks |
| title_short | Intelligent energy aware optimization protocol for vehicular adhoc networks |
| title_sort | intelligent energy aware optimization protocol for vehicular adhoc networks |
| url | https://doi.org/10.1038/s41598-023-35042-6 |
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