Optimized Routing by Combining Grey Wolf and Dragonfly Optimization for Energy Efficiency in Wireless Sensor Networks
The rapid development of technology has resulted in numerous sensors and devices for performing measurements in an environment. Depending on the scale and application, the coverage and size of a wireless sensor network (WSN) is decided. During the implementation, the energy consumption and life of t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/10948 |
| _version_ | 1797469236425129984 |
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| author | Hiba Apdalani Younus Cemal Koçak |
| author_facet | Hiba Apdalani Younus Cemal Koçak |
| author_sort | Hiba Apdalani Younus |
| collection | DOAJ |
| description | The rapid development of technology has resulted in numerous sensors and devices for performing measurements in an environment. Depending on the scale and application, the coverage and size of a wireless sensor network (WSN) is decided. During the implementation, the energy consumption and life of the nodes in the WSN are affected by the continuous usage. Hence, in this study, we aimed to improve the lifespan of the WSN and reduce energy consumption by the nodes during the data transfer using a hybrid approach. The hybrid approach combines Grey Wolf Optimization (GWO) and Dragonfly Optimization (DFO) for exploring a global solution and optimizing the local solution to find the optimum route for the data transfer between the target node and the control center. The results show that the proposed approach has effective energy consumption corresponding to the load applied. Our proposed system scored high in the average residual energy by the number of rounds compared to other methods such as k-means, LEACH-C, CHIRON, and Optimal-CBR. The first dead node was found after 500 rounds, showing that the proposed model has nodes with better reliability. It also showed a comparative analysis of the transmission rate of a packet concerning mobility speed among various methods. The proposed method has the highest ratio at all mobility speeds, i.e., 99.3, 99.1, 99, 98.8, and 98.6, and our proposed system has the lowest computational time of all the evaluated methods, 6 s. |
| first_indexed | 2024-03-09T19:18:31Z |
| format | Article |
| id | doaj.art-d61ad67d90ac4065bf617e57e38367bf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:18:31Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d61ad67d90ac4065bf617e57e38367bf2023-11-24T03:35:30ZengMDPI AGApplied Sciences2076-34172022-10-0112211094810.3390/app122110948Optimized Routing by Combining Grey Wolf and Dragonfly Optimization for Energy Efficiency in Wireless Sensor NetworksHiba Apdalani Younus0Cemal Koçak1Faculty of Technology, Institute of Science and Computer Engineering, Gazi University, Ankara 06560, TurkeyFaculty of Technology Computer Engineering, Gazi University, Ankara 06560, TurkeyThe rapid development of technology has resulted in numerous sensors and devices for performing measurements in an environment. Depending on the scale and application, the coverage and size of a wireless sensor network (WSN) is decided. During the implementation, the energy consumption and life of the nodes in the WSN are affected by the continuous usage. Hence, in this study, we aimed to improve the lifespan of the WSN and reduce energy consumption by the nodes during the data transfer using a hybrid approach. The hybrid approach combines Grey Wolf Optimization (GWO) and Dragonfly Optimization (DFO) for exploring a global solution and optimizing the local solution to find the optimum route for the data transfer between the target node and the control center. The results show that the proposed approach has effective energy consumption corresponding to the load applied. Our proposed system scored high in the average residual energy by the number of rounds compared to other methods such as k-means, LEACH-C, CHIRON, and Optimal-CBR. The first dead node was found after 500 rounds, showing that the proposed model has nodes with better reliability. It also showed a comparative analysis of the transmission rate of a packet concerning mobility speed among various methods. The proposed method has the highest ratio at all mobility speeds, i.e., 99.3, 99.1, 99, 98.8, and 98.6, and our proposed system has the lowest computational time of all the evaluated methods, 6 s.https://www.mdpi.com/2076-3417/12/21/10948energy efficientwireless sensor networksgrey wolf optimizationdragonfly optimization (DFO) |
| spellingShingle | Hiba Apdalani Younus Cemal Koçak Optimized Routing by Combining Grey Wolf and Dragonfly Optimization for Energy Efficiency in Wireless Sensor Networks Applied Sciences energy efficient wireless sensor networks grey wolf optimization dragonfly optimization (DFO) |
| title | Optimized Routing by Combining Grey Wolf and Dragonfly Optimization for Energy Efficiency in Wireless Sensor Networks |
| title_full | Optimized Routing by Combining Grey Wolf and Dragonfly Optimization for Energy Efficiency in Wireless Sensor Networks |
| title_fullStr | Optimized Routing by Combining Grey Wolf and Dragonfly Optimization for Energy Efficiency in Wireless Sensor Networks |
| title_full_unstemmed | Optimized Routing by Combining Grey Wolf and Dragonfly Optimization for Energy Efficiency in Wireless Sensor Networks |
| title_short | Optimized Routing by Combining Grey Wolf and Dragonfly Optimization for Energy Efficiency in Wireless Sensor Networks |
| title_sort | optimized routing by combining grey wolf and dragonfly optimization for energy efficiency in wireless sensor networks |
| topic | energy efficient wireless sensor networks grey wolf optimization dragonfly optimization (DFO) |
| url | https://www.mdpi.com/2076-3417/12/21/10948 |
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