Prediction based computation offloading and resource allocation for multi-access ISAC enabled IoT system
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929402729725952 |
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| author | Le, Duc-Thuan |
| author_facet | Le, Duc-Thuan |
| contents | In the new era of the Internet of Things (IoT), tasks are now being migrated to edge sites closer to data generators. Mobile devices inherently encounter limitations in terms of energy and computational processing capabilities. In high mobility paradigm, ISAC provides a promising foundation for integrating deployment management within dynamic spatial settings. We are interested in applying prediction mechanism to resource allocation management by extracting data attributes, focusing on ISAC related contexts of the trajectory and velocity and making the allocating decision. We present a system design, a theoretical framework and an implementation of the ClusterMan software package. The numerical suggests that the strong clustering subset of feature may yield high accuracy up to 97\% in the prediction results. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_19806 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prediction based computation offloading and resource allocation for multi-access ISAC enabled IoT system Le, Duc-Thuan Distributed, Parallel, and Cluster Computing Emerging Technologies Networking and Internet Architecture 60-08 C.2.1 In the new era of the Internet of Things (IoT), tasks are now being migrated to edge sites closer to data generators. Mobile devices inherently encounter limitations in terms of energy and computational processing capabilities. In high mobility paradigm, ISAC provides a promising foundation for integrating deployment management within dynamic spatial settings. We are interested in applying prediction mechanism to resource allocation management by extracting data attributes, focusing on ISAC related contexts of the trajectory and velocity and making the allocating decision. We present a system design, a theoretical framework and an implementation of the ClusterMan software package. The numerical suggests that the strong clustering subset of feature may yield high accuracy up to 97\% in the prediction results. |
| title | Prediction based computation offloading and resource allocation for multi-access ISAC enabled IoT system |
| topic | Distributed, Parallel, and Cluster Computing Emerging Technologies Networking and Internet Architecture 60-08 C.2.1 |
| url | https://arxiv.org/abs/2406.19806 |