Optimal Task Scheduling on Agri-IoT with optimal Clustering and Multi-Cast Routing.
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| Title: | Optimal Task Scheduling on Agri-IoT with optimal Clustering and Multi-Cast Routing. |
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| Authors: | Kalli, Sivanagireddy1 sivanagireddykalli@gmail.com, Aouthu, Sriakshmi2, Raju, Vysyaraju Lokesh3, Saravanan, V.4, Pushpavalli, R.5, Nalini, M.6 |
| Source: | Journal of Engineering Science & Technology Review. 2025, Vol. 18 Issue 3, p259-272. 14p. |
| Subjects: | Archimedes' principle, Virtual machine systems, End-to-end delay, Optimization algorithms, Resource allocation |
| Abstract: | A cutting-edge resource allocation framework for smart farming and the Challenges regarding energy consumption and latency are addressed by the Agro-Internet of Things (Agri-IOT) optimal resource allocation model and task scheduling. The Internet of Things (IoT) sensor-equipped agriculture, optimal clustering, multi-cast routing, cloud-based data storage, and optimal task scheduling are the five steps that make up this strategy. When it comes to selecting the perfect cluster head (CH) for data aggregation and transmission, the optimal clustering stage makes use of the novel Archimedes principle that has been updated with the hunger game optimization (APUHGO) model. This choice was made after considering a number of factors, such as the reduction of energy consumption, the central hub distance, the cluster radius, the packet delivery ratio, the end-to-end delay, and the throughput. A further use of the APUHGO model is to ascertain the most efficient route between the CH and the sink node, which is the CH that is located closest to the base station (BS), with trust, energy, and bandwidth being given the highest priority. When it comes to task scheduling, the optimal virtual machine (VM) is selected based on eight different parameters. These factors are waiting time, execution time, trust, priority, work completion time, pan, quality of service, and throughput. In order to achieve improved precision agriculture task scheduling, the newly developed Improved Dragonfly Optimization algorithm is responsible for selecting the most suitable virtual machine (VM). Additionally, in order to demonstrate the usefulness of the suggested model, its performance was compared to that of previously developed models. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering Science & Technology Review is the property of Technological Education Institute of Kavala and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 186704377 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimal Task Scheduling on Agri-IoT with optimal Clustering and Multi-Cast Routing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kalli%2C+Sivanagireddy%22">Kalli, Sivanagireddy</searchLink><relatesTo>1</relatesTo><i> sivanagireddykalli@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Aouthu%2C+Sriakshmi%22">Aouthu, Sriakshmi</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Raju%2C+Vysyaraju+Lokesh%22">Raju, Vysyaraju Lokesh</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Saravanan%2C+V%2E%22">Saravanan, V.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Pushpavalli%2C+R%2E%22">Pushpavalli, R.</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Nalini%2C+M%2E%22">Nalini, M.</searchLink><relatesTo>6</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+Science+%26+Technology+Review%22">Journal of Engineering Science & Technology Review</searchLink>. 2025, Vol. 18 Issue 3, p259-272. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Archimedes'+principle%22">Archimedes' principle</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+machine+systems%22">Virtual machine systems</searchLink><br /><searchLink fieldCode="DE" term="%22End-to-end+delay%22">End-to-end delay</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A cutting-edge resource allocation framework for smart farming and the Challenges regarding energy consumption and latency are addressed by the Agro-Internet of Things (Agri-IOT) optimal resource allocation model and task scheduling. The Internet of Things (IoT) sensor-equipped agriculture, optimal clustering, multi-cast routing, cloud-based data storage, and optimal task scheduling are the five steps that make up this strategy. When it comes to selecting the perfect cluster head (CH) for data aggregation and transmission, the optimal clustering stage makes use of the novel Archimedes principle that has been updated with the hunger game optimization (APUHGO) model. This choice was made after considering a number of factors, such as the reduction of energy consumption, the central hub distance, the cluster radius, the packet delivery ratio, the end-to-end delay, and the throughput. A further use of the APUHGO model is to ascertain the most efficient route between the CH and the sink node, which is the CH that is located closest to the base station (BS), with trust, energy, and bandwidth being given the highest priority. When it comes to task scheduling, the optimal virtual machine (VM) is selected based on eight different parameters. These factors are waiting time, execution time, trust, priority, work completion time, pan, quality of service, and throughput. In order to achieve improved precision agriculture task scheduling, the newly developed Improved Dragonfly Optimization algorithm is responsible for selecting the most suitable virtual machine (VM). Additionally, in order to demonstrate the usefulness of the suggested model, its performance was compared to that of previously developed models. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Engineering Science & Technology Review is the property of Technological Education Institute of Kavala and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.25103/jestr.183.25 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 259 Subjects: – SubjectFull: Archimedes' principle Type: general – SubjectFull: Virtual machine systems Type: general – SubjectFull: End-to-end delay Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Resource allocation Type: general Titles: – TitleFull: Optimal Task Scheduling on Agri-IoT with optimal Clustering and Multi-Cast Routing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kalli, Sivanagireddy – PersonEntity: Name: NameFull: Aouthu, Sriakshmi – PersonEntity: Name: NameFull: Raju, Vysyaraju Lokesh – PersonEntity: Name: NameFull: Saravanan, V. – PersonEntity: Name: NameFull: Pushpavalli, R. – PersonEntity: Name: NameFull: Nalini, M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 17912377 Numbering: – Type: volume Value: 18 – Type: issue Value: 3 Titles: – TitleFull: Journal of Engineering Science & Technology Review Type: main |
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