AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks
As the number of smart connected devices increases day by day, a massive amount of tasks are generated by various types of Internet of Things (IoT) devices. Intelligent edge computing is a promising enabler in next-generation wireless networks to execute these tasks on proximate edge servers instead...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9998539/ |
| _version_ | 1797959611893940224 |
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| author | Beste Atan Mehmet Basaran Nurullah Calik Semiha Tedik Basaran Gulde Akkuzu Lutfiye Durak-Ata |
| author_facet | Beste Atan Mehmet Basaran Nurullah Calik Semiha Tedik Basaran Gulde Akkuzu Lutfiye Durak-Ata |
| author_sort | Beste Atan |
| collection | DOAJ |
| description | As the number of smart connected devices increases day by day, a massive amount of tasks are generated by various types of Internet of Things (IoT) devices. Intelligent edge computing is a promising enabler in next-generation wireless networks to execute these tasks on proximate edge servers instead of smart devices. Additionally, regarding the execution of tasks in edge servers, smart devices could provide a low-latency environment to the end users. Within this paper, an artificial intelligence (AI)-empowered fast task execution method in heterogeneous IoT applications is proposed to reduce decision latency by taking into account different system parameters such as the execution deadline of the task, battery level of devices, channel conditions between mobile devices and edge servers, and edge server capacity. In edge computing scenarios, the number of task requests, resource constraints of edge servers, mobility of connected devices, and energy consumption are the main performance considerations. In this paper, the AI-empowered fast task decision method is proposed to solve the multi-device edge computing task execution problem by formulating it as a multi-class classification problem. The extensive simulation results demonstrate that the proposed framework is extremely fast and precise in decision-making for offloading computation tasks compared to the conventional Lyapunov optimization-based algorithm results by ensuring the guaranteed quality of experience. |
| first_indexed | 2024-04-11T00:35:10Z |
| format | Article |
| id | doaj.art-b22ccd12507f4702b8bf8dadc2442c7c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T00:35:10Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b22ccd12507f4702b8bf8dadc2442c7c2023-01-07T00:00:31ZengIEEEIEEE Access2169-35362023-01-01111324133410.1109/ACCESS.2022.32320739998539AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing NetworksBeste Atan0https://orcid.org/0000-0003-3968-318XMehmet Basaran1https://orcid.org/0000-0002-5473-1437Nurullah Calik2https://orcid.org/0000-0002-7351-4980Semiha Tedik Basaran3https://orcid.org/0000-0003-2636-143XGulde Akkuzu4Lutfiye Durak-Ata5https://orcid.org/0000-0002-4368-2967Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, TurkeyKartal Research and Development Center, Siemens Sanayi Ticaret A.S., Istanbul, TurkeyDepartment of Biomedical Engineering, Istanbul Medeniyet University, Istanbul, TurkeyDepartment of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, TurkeyDepartment of Electrical and Computer Engineering, Technical University of Munich, Munich, GermanyInformation and Communications Research Group, Informatics Institute, Istanbul Technical University, Istanbul, TurkeyAs the number of smart connected devices increases day by day, a massive amount of tasks are generated by various types of Internet of Things (IoT) devices. Intelligent edge computing is a promising enabler in next-generation wireless networks to execute these tasks on proximate edge servers instead of smart devices. Additionally, regarding the execution of tasks in edge servers, smart devices could provide a low-latency environment to the end users. Within this paper, an artificial intelligence (AI)-empowered fast task execution method in heterogeneous IoT applications is proposed to reduce decision latency by taking into account different system parameters such as the execution deadline of the task, battery level of devices, channel conditions between mobile devices and edge servers, and edge server capacity. In edge computing scenarios, the number of task requests, resource constraints of edge servers, mobility of connected devices, and energy consumption are the main performance considerations. In this paper, the AI-empowered fast task decision method is proposed to solve the multi-device edge computing task execution problem by formulating it as a multi-class classification problem. The extensive simulation results demonstrate that the proposed framework is extremely fast and precise in decision-making for offloading computation tasks compared to the conventional Lyapunov optimization-based algorithm results by ensuring the guaranteed quality of experience.https://ieeexplore.ieee.org/document/9998539/AIclassificationcomputation offloadingintelligent networksInternet of ThingsLyapunov optimization |
| spellingShingle | Beste Atan Mehmet Basaran Nurullah Calik Semiha Tedik Basaran Gulde Akkuzu Lutfiye Durak-Ata AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks IEEE Access AI classification computation offloading intelligent networks Internet of Things Lyapunov optimization |
| title | AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks |
| title_full | AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks |
| title_fullStr | AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks |
| title_full_unstemmed | AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks |
| title_short | AI-Empowered Fast Task Execution Decision for Delay-Sensitive IoT Applications in Edge Computing Networks |
| title_sort | ai empowered fast task execution decision for delay sensitive iot applications in edge computing networks |
| topic | AI classification computation offloading intelligent networks Internet of Things Lyapunov optimization |
| url | https://ieeexplore.ieee.org/document/9998539/ |
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