A Hybrid Heuristic Model for Duty Cycle Framework Optimization

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dc.contributor.authorAnsah, K.
dc.contributor.authorAppati, J.K.
dc.contributor.authorOwusu, E.
dc.contributor.authorAbdulai, J-D.
dc.date.accessioned2024-03-08T18:55:41Z
dc.date.available2024-03-08T18:55:41Z
dc.date.issued2024
dc.descriptionResearch Articleen_US
dc.description.abstractThis 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.en_US
dc.identifier.otherhttps://doi.org/10.1155/2024/9972429
dc.identifier.urihttp://ugspace.ug.edu.gh:8080/handle/123456789/41417
dc.language.isoenen_US
dc.publisherInternational Journal of Distributed Sensor Networksen_US
dc.subjectWireless sensor networks (WSNs)en_US
dc.subjectOptimizationen_US
dc.subjectseagullen_US
dc.titleA Hybrid Heuristic Model for Duty Cycle Framework Optimizationen_US
dc.typeArticleen_US

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