Task and resource allocation in the internet of things based on an improved version of the moth-flame optimization algorithm

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dc.authoridGhaffari, Ali/0000-0001-5407-8629
dc.authorwosidMirzaei, Abbas/AAM-2772-2021
dc.authorwosidGhaffari, Ali/AAV-3651-2020
dc.contributor.authorNematollahi, Masoud
dc.contributor.authorGhaffari, Ali
dc.contributor.authorMirzaei, A.
dc.date.accessioned2024-05-19T14:42:46Z
dc.date.available2024-05-19T14:42:46Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe Internet of Things (IoT) technology is used to develop a wide range of applications and services, including intelligent healthcare systems and virtual reality applications. Low processing power limits IoT devices' capabilities. It's common practice to use cloud services to do operations that would otherwise require a user's device to be overloaded with data. High latency, high traffic, and high energy consumption remain, though. Given the above concerns, Fog Computing (FC) should be applied in the IoT to speed up time-sensitive data processing and management. In this study, a novel architecture for offloading jobs and allocating resources in the IoT is presented. Sensors, controllers, and FC servers are all part of the upgraded system. The second layer uses the subtask pool approach to offload work and the Moth-Flame Optimization (MFO) algorithm combined with Opposition-based Learning (OBL) to distribute resources. This combination is known as OBLMFO. A stack cache approach is used to complete resource allocation in the second layer to avoid system load imbalance. In addition, the second layer relies on the blockchain to guarantee the accuracy of transaction data. Another way to put it is that the proposed architecture utilizes blockchain advantages to optimize resource distribution in the IoT. The evaluation of the OBLMFO model was done through the Python 3.9 environment, which contains a large variety of distinct jobs. The results show that the OBLMFO model reduced the delay factor by 12.18% and the energy consumed by 6.22%.en_US
dc.identifier.doi10.1007/s10586-023-04041-7
dc.identifier.endpage1797en_US
dc.identifier.issn1386-7857
dc.identifier.issn1573-7543
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85160849772en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1775en_US
dc.identifier.urihttps://doi.org10.1007/s10586-023-04041-7
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5282
dc.identifier.volume27en_US
dc.identifier.wosWOS:001000752600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofCluster Computing-The Journal of Networks Software 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.subjectInternet Of Thingsen_US
dc.subjectFog Computingen_US
dc.subjectTask Offloadingen_US
dc.subjectResource Allocationen_US
dc.subjectEfficiencyen_US
dc.titleTask and resource allocation in the internet of things based on an improved version of the moth-flame optimization algorithmen_US
dc.typeArticleen_US

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