A Hybrid Delay Aware Clustered Routing Approach Using Aquila Optimizer and Firefly Algorithm in Internet of Things
Protocols for clustering and routing in the Internet of Things ecosystem should consider minimizing power consumption. Existing approaches to cluster-based routing issues in the Internet of Things environment often face the challenge of uneven power consumption. This study created a clustering metho...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/22/4331 |
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| author | Mehdi Hosseinzadeh Liliana Ionescu-Feleaga Bogdan-Ștefan Ionescu Mahyar Sadrishojaei Faeze Kazemian Amir Masoud Rahmani Faheem Khan |
| author_facet | Mehdi Hosseinzadeh Liliana Ionescu-Feleaga Bogdan-Ștefan Ionescu Mahyar Sadrishojaei Faeze Kazemian Amir Masoud Rahmani Faheem Khan |
| author_sort | Mehdi Hosseinzadeh |
| collection | DOAJ |
| description | Protocols for clustering and routing in the Internet of Things ecosystem should consider minimizing power consumption. Existing approaches to cluster-based routing issues in the Internet of Things environment often face the challenge of uneven power consumption. This study created a clustering method utilising swarm intelligence to obtain a more even distribution of cluster heads. In this work, a firefly optimization method and an aquila optimizer algorithm are devised to select the intermediate and cluster head nodes required for routing in accordance with the NP-Hard nature of clustered routing. The effectiveness of this hybrid clustering and routing approach has been evaluated concerning the following metrics: remaining energy, mean distances, number of hops, and node balance. For assessing Internet of things platforms, metrics like network throughput and the number of the living node are crucial, as these systems rely on battery-operated equipment to regularly capture environment data and transmit specimens to a base station. Proving effective, the suggested technique has been found to improve system energy usage by at least 18% and increase the packet delivery ratio by at least 25%. |
| first_indexed | 2024-03-09T18:10:27Z |
| format | Article |
| id | doaj.art-03c66e9adfcb47a2ab35db07bf2e5ce5 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T18:10:27Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-03c66e9adfcb47a2ab35db07bf2e5ce52023-11-24T09:09:51ZengMDPI AGMathematics2227-73902022-11-011022433110.3390/math10224331A Hybrid Delay Aware Clustered Routing Approach Using Aquila Optimizer and Firefly Algorithm in Internet of ThingsMehdi Hosseinzadeh0Liliana Ionescu-Feleaga1Bogdan-Ștefan Ionescu2Mahyar Sadrishojaei3Faeze Kazemian4Amir Masoud Rahmani5Faheem Khan6Institute of Research and Development, Duy Tan University, Da Nang 550000, VietnamDepartment of Accounting and Audit, Bucharest University of Economic Studies, 010374 Bucharest, RomaniaDepartment of Management Information System, Bucharest University of Economic Studies, 010374 Bucharest, RomaniaFaculty of Industry, University of Applied Science and Technology (UAST), Tehran 11369, IranDepartment of Computer Science, University of Applied Science and Technology (UAST), Tehran 11369, IranFuture Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, TaiwanDepartment of Computer Engineering, Gachon University, Seongnam 13120, Republic of KoreaProtocols for clustering and routing in the Internet of Things ecosystem should consider minimizing power consumption. Existing approaches to cluster-based routing issues in the Internet of Things environment often face the challenge of uneven power consumption. This study created a clustering method utilising swarm intelligence to obtain a more even distribution of cluster heads. In this work, a firefly optimization method and an aquila optimizer algorithm are devised to select the intermediate and cluster head nodes required for routing in accordance with the NP-Hard nature of clustered routing. The effectiveness of this hybrid clustering and routing approach has been evaluated concerning the following metrics: remaining energy, mean distances, number of hops, and node balance. For assessing Internet of things platforms, metrics like network throughput and the number of the living node are crucial, as these systems rely on battery-operated equipment to regularly capture environment data and transmit specimens to a base station. Proving effective, the suggested technique has been found to improve system energy usage by at least 18% and increase the packet delivery ratio by at least 25%.https://www.mdpi.com/2227-7390/10/22/4331internet of thingsclustered routingaquila optimizerfirefly algorithmenergy efficientlifespan |
| spellingShingle | Mehdi Hosseinzadeh Liliana Ionescu-Feleaga Bogdan-Ștefan Ionescu Mahyar Sadrishojaei Faeze Kazemian Amir Masoud Rahmani Faheem Khan A Hybrid Delay Aware Clustered Routing Approach Using Aquila Optimizer and Firefly Algorithm in Internet of Things Mathematics internet of things clustered routing aquila optimizer firefly algorithm energy efficient lifespan |
| title | A Hybrid Delay Aware Clustered Routing Approach Using Aquila Optimizer and Firefly Algorithm in Internet of Things |
| title_full | A Hybrid Delay Aware Clustered Routing Approach Using Aquila Optimizer and Firefly Algorithm in Internet of Things |
| title_fullStr | A Hybrid Delay Aware Clustered Routing Approach Using Aquila Optimizer and Firefly Algorithm in Internet of Things |
| title_full_unstemmed | A Hybrid Delay Aware Clustered Routing Approach Using Aquila Optimizer and Firefly Algorithm in Internet of Things |
| title_short | A Hybrid Delay Aware Clustered Routing Approach Using Aquila Optimizer and Firefly Algorithm in Internet of Things |
| title_sort | hybrid delay aware clustered routing approach using aquila optimizer and firefly algorithm in internet of things |
| topic | internet of things clustered routing aquila optimizer firefly algorithm energy efficient lifespan |
| url | https://www.mdpi.com/2227-7390/10/22/4331 |
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