An optimal task scheduling method in IoT-Fog-Cloud network using multi-objective moth-flame algorithm

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dc.authoridAbualigah, Laith/0000-0002-2203-4549
dc.authorwosidAbualigah, Laith/ABC-9695-2020
dc.contributor.authorSalehnia, Taybeh
dc.contributor.authorSeyfollahi, Ali
dc.contributor.authorRaziani, Saeid
dc.contributor.authorNoori, Azad
dc.contributor.authorGhaffari, Ali
dc.contributor.authorAlsoud, Anas Ratib
dc.contributor.authorAbualigah, Laith
dc.date.accessioned2024-05-19T14:40:10Z
dc.date.available2024-05-19T14:40:10Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractNowadays, cloud and fog computing have been leveraged to enhance Internet of Things (IoT) performance. The outstanding potential of cloud platforms accelerates the processing and storage of aggregated big data from IoT equipment. Emerging fog-based schemes can improve service quality to IoT applications and mitigate excessive delays and security challenges. Also, since energy consumption can directly cause CO2 emissions from fog and cloud nodes, an efficient task scheduling method reduces energy consumption. In this regard, the growing need for an efficient task scheduling mechanism considering the optimal management of IoT resources is increasingly felt. IoT's task scheduling based on fog-cloud computing plays a crucial role in responding to users' requests. Optimal task scheduling can improve system performance. Therefore, this study uses an IoT task request scheduling method on resources by the Multi-Objective Moth-Flame Optimization (MOMFO) algorithm. It enhances the quality of IoT services based on fog-cloud computing to reduce task requests' completion and system throughput times and energy consumption. If energy consumption is diminished, the percentage of CO2 emissions is also reduced. Then, the proposed scheduling method to solve the task scheduling problem is evaluated using the datasets. A comparison between the proposed scheme and Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Salp Swarm Algorithms (SSA), Harris Hawks Optimizer (HHO), and Artificial Bee Colony (ABC) is performed to assess the performance. According to experiments, the proposed solution has reduced the completion time of IoT tasks and throughput time, thus cutting down the delay due to the processing of tasks, energy consumption, and CO2 emissions and increasing the system's performance rate.en_US
dc.identifier.doi10.1007/s11042-023-16971-w
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.scopus2-s2.0-85172168935en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s11042-023-16971-w
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4916
dc.identifier.wosWOS:001073965700005en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofMultimedia Tools and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectCloud Computingen_US
dc.subjectFog Computingen_US
dc.subjectInternet Of Thingsen_US
dc.subjectMulti-Objective Moth-Flame Optimizationen_US
dc.subjectTask Schedulingen_US
dc.titleAn optimal task scheduling method in IoT-Fog-Cloud network using multi-objective moth-flame algorithmen_US
dc.typeArticleen_US

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