The global path planning for vehicular communication using ant colony algorithm in emerging wireless cloud computing.
Saved in:
| Title: | The global path planning for vehicular communication using ant colony algorithm in emerging wireless cloud computing. |
|---|---|
| Authors: | Huo, Lina1 (AUTHOR) huolina@yulinu.edu.cn |
| Source: | Wireless Networks (10220038). Feb2023, Vol. 29 Issue 2, p833-842. 10p. |
| Subjects: | Ant algorithms, Communication planning, Virtual machine systems, Cloud computing, Software as a service, Traffic speed |
| Abstract: | With the usage of a pay-per-use business model, cloud computing gives clients Infrastructure as a Service, Software as a Service, and Platform as a Service to provide compute, network, and storage capabilities. Wireless communication may frequently occur in cloud computing to ensure smoot data transmission for storage and processing purposes. In view of the characteristics of frequent changes in traffic speed and many alternative paths in urban areas, the traditional algorithm cannot effectively converge due to a large amount of calculation when selecting a path, and the search efficiency in 3D path planning is low, and it is informal to collapse into stagnation and local optimal problems. Machine learning techniques, in particular, are the driving force behind the cloud backend for emerging paradigms. By resolving issues with job scheduling, resource provisioning and allocation, offloading, load balancing, migration of Virtual Machines (VMs), VM mapping, workload prediction, energy optimization, device monitoring, etc., and these learning techniques essentially improve how these paradigms are used. In this paper, we suggest a process and mechanism to enhance the path selection process that can be utilized in emerging wireless cloud computing environments. To increase the search efficiency in the early stages of the algorithm, the initial pheromone is first dispersed unevenly based on the traits of the best path. Secondly, a dual-objective mixed-integer programming model is established, and dynamically adjusting the values of the weight factors a and of the pheromone and heuristic function accelerates the convergence of the algorithm while taking into account the minimum total distribution cost and maximisation of overall customer satisfaction. The local optimum of the algorithm is reached. The simulation results demonstrate the utility and reference value of the proposed model and enhanced algorithm for planning the distribution of vehicles on complex roads. [ABSTRACT FROM AUTHOR] |
| Copyright of Wireless Networks (10220038) is the property of Springer Nature 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 161486302 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: The global path planning for vehicular communication using ant colony algorithm in emerging wireless cloud computing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huo%2C+Lina%22">Huo, Lina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> huolina@yulinu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Wireless+Networks+%2810220038%29%22">Wireless Networks (10220038)</searchLink>. Feb2023, Vol. 29 Issue 2, p833-842. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Ant+algorithms%22">Ant algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Communication+planning%22">Communication planning</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+machine+systems%22">Virtual machine systems</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Software+as+a+service%22">Software as a service</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+speed%22">Traffic speed</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the usage of a pay-per-use business model, cloud computing gives clients Infrastructure as a Service, Software as a Service, and Platform as a Service to provide compute, network, and storage capabilities. Wireless communication may frequently occur in cloud computing to ensure smoot data transmission for storage and processing purposes. In view of the characteristics of frequent changes in traffic speed and many alternative paths in urban areas, the traditional algorithm cannot effectively converge due to a large amount of calculation when selecting a path, and the search efficiency in 3D path planning is low, and it is informal to collapse into stagnation and local optimal problems. Machine learning techniques, in particular, are the driving force behind the cloud backend for emerging paradigms. By resolving issues with job scheduling, resource provisioning and allocation, offloading, load balancing, migration of Virtual Machines (VMs), VM mapping, workload prediction, energy optimization, device monitoring, etc., and these learning techniques essentially improve how these paradigms are used. In this paper, we suggest a process and mechanism to enhance the path selection process that can be utilized in emerging wireless cloud computing environments. To increase the search efficiency in the early stages of the algorithm, the initial pheromone is first dispersed unevenly based on the traits of the best path. Secondly, a dual-objective mixed-integer programming model is established, and dynamically adjusting the values of the weight factors a and of the pheromone and heuristic function accelerates the convergence of the algorithm while taking into account the minimum total distribution cost and maximisation of overall customer satisfaction. The local optimum of the algorithm is reached. The simulation results demonstrate the utility and reference value of the proposed model and enhanced algorithm for planning the distribution of vehicles on complex roads. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Wireless Networks (10220038) is the property of Springer Nature 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=161486302 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11276-022-03152-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 833 Subjects: – SubjectFull: Ant algorithms Type: general – SubjectFull: Communication planning Type: general – SubjectFull: Virtual machine systems Type: general – SubjectFull: Cloud computing Type: general – SubjectFull: Software as a service Type: general – SubjectFull: Traffic speed Type: general Titles: – TitleFull: The global path planning for vehicular communication using ant colony algorithm in emerging wireless cloud computing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huo, Lina IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 10220038 Numbering: – Type: volume Value: 29 – Type: issue Value: 2 Titles: – TitleFull: Wireless Networks (10220038) Type: main |
| ResultId | 1 |
