A Hybrid Heuristic Model for Duty Cycle Framework Optimization
This paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversifica...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | http://dx.doi.org/10.1155/2024/9972429 |
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| _version_ | 1832553075640893440 |
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| author | Kwabena Ansah Justice Kwame Appati Ebenezer Owusu Jamal-Deen Abdulai |
| author_facet | Kwabena Ansah Justice Kwame Appati Ebenezer Owusu Jamal-Deen Abdulai |
| author_sort | Kwabena Ansah |
| collection | DOAJ |
| description | This paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversification of varying WSNs. The proposed HSMO-DC primarily has two parts, where the first part takes care of the online cluster head selection and network communication using the seagull algorithm while the second part performs parameter optimization using the mayfly algorithm. The seagull is aimed at improving the energy distribution in the network through an effective bandwidth allocation procedure while reducing the total energy dissipation. Comparatively, with other clustering protocols, our proposed methods reveal an enhanced network lifetime with an improved network throughput and adaptability based on selected standard metric of performance measurement. |
| format | Article |
| id | doaj-art-9e74e85402a043adb982e97ab5acd582 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-9e74e85402a043adb982e97ab5acd5822025-02-03T05:57:02ZengWileyInternational Journal of Distributed Sensor Networks1550-14772024-01-01202410.1155/2024/9972429A Hybrid Heuristic Model for Duty Cycle Framework OptimizationKwabena Ansah0Justice Kwame Appati1Ebenezer Owusu2Jamal-Deen Abdulai3Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceThis paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversification of varying WSNs. The proposed HSMO-DC primarily has two parts, where the first part takes care of the online cluster head selection and network communication using the seagull algorithm while the second part performs parameter optimization using the mayfly algorithm. The seagull is aimed at improving the energy distribution in the network through an effective bandwidth allocation procedure while reducing the total energy dissipation. Comparatively, with other clustering protocols, our proposed methods reveal an enhanced network lifetime with an improved network throughput and adaptability based on selected standard metric of performance measurement.http://dx.doi.org/10.1155/2024/9972429 |
| spellingShingle | Kwabena Ansah Justice Kwame Appati Ebenezer Owusu Jamal-Deen Abdulai A Hybrid Heuristic Model for Duty Cycle Framework Optimization International Journal of Distributed Sensor Networks |
| title | A Hybrid Heuristic Model for Duty Cycle Framework Optimization |
| title_full | A Hybrid Heuristic Model for Duty Cycle Framework Optimization |
| title_fullStr | A Hybrid Heuristic Model for Duty Cycle Framework Optimization |
| title_full_unstemmed | A Hybrid Heuristic Model for Duty Cycle Framework Optimization |
| title_short | A Hybrid Heuristic Model for Duty Cycle Framework Optimization |
| title_sort | hybrid heuristic model for duty cycle framework optimization |
| url | http://dx.doi.org/10.1155/2024/9972429 |
| work_keys_str_mv | AT kwabenaansah ahybridheuristicmodelfordutycycleframeworkoptimization AT justicekwameappati ahybridheuristicmodelfordutycycleframeworkoptimization AT ebenezerowusu ahybridheuristicmodelfordutycycleframeworkoptimization AT jamaldeenabdulai ahybridheuristicmodelfordutycycleframeworkoptimization AT kwabenaansah hybridheuristicmodelfordutycycleframeworkoptimization AT justicekwameappati hybridheuristicmodelfordutycycleframeworkoptimization AT ebenezerowusu hybridheuristicmodelfordutycycleframeworkoptimization AT jamaldeenabdulai hybridheuristicmodelfordutycycleframeworkoptimization |