- Title
- MSCET : a multi-scenario offloading schedule for biomedical data processing and analysis in cloud-edge-terminal collaborative vehicular networks
- Creator
- Ni, Zhichen; Chen, Honglong; Li, Zhe; Wang, Xiaomeng; Yan, Na; Liu, Weifeng; Xia, Feng
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197008
- Identifier
- vital:18794
- Identifier
-
https://doi.org/10.1109/TCBB.2021.3131177
- Identifier
- ISSN:1545-5963 (ISSN)
- Abstract
- With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more challenges to the biometric monitoring of drivers. Edge computing is a new paradigm to solve these challenges by offloading tasks from the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs). However, most of the traditional offloading schedules for vehicular networks concentrate on the edge, while some tasks may be too complex for ESs to process. To this end, we consider a collaborative vehicular network in which the cloud, edge and terminal can cooperate with each other to accomplish the tasks. The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge. We further construct the virtual resource pool which can integrate the resource of multiple ESs since some regions may be covered by multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule for biomedical data processing and analysis in Cloud-Edge-Terminal collaborative vehicular networks called MSCET. The parameters of the proposed MSCET are optimized to maximize the system utility. We also conduct extensive simulations to evaluate the proposed MSCET and the results illustrate that MSCET outperforms other existing schedules. © 2004-2012 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 20, no. 4 (2023), p. 2376-2386
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 IEEE
- Rights
- Open Access
- Subject
- 31 Biological sciences; 46 Information and computing sciences; 49 Mathematical sciences; Biomedical data processing and analysis; Cloud-edge-terminal collaborative vehicular networks; Optimization; Resource allocation; Task offloading
- Full Text
- Reviewed
- Funder
- This work was supported in part by the Shandong Provincial Natural Science Foundation, China under Grant ZR2022YQ61, NSFC under Grants 61772551 and 62111530052, the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-003, and the Fundamental Research Funds for the Central Universities under Grant 22CX01003A-9.
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