MSCET : a multi-scenario offloading schedule for biomedical data processing and analysis in cloud-edge-terminal collaborative vehicular networks
- Ni, Zhichen, Chen, Honglong, Li, Zhe, Wang, Xiaomeng, Yan, Na, Liu, Weifeng, Xia, Feng
- Authors: Ni, Zhichen , Chen, Honglong , Li, Zhe , Wang, Xiaomeng , Yan, Na , Liu, Weifeng , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 20, no. 4 (2023), p. 2376-2386
- Full Text:
- Reviewed:
- Description: 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.
- Authors: Ni, Zhichen , Chen, Honglong , Li, Zhe , Wang, Xiaomeng , Yan, Na , Liu, Weifeng , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 20, no. 4 (2023), p. 2376-2386
- Full Text:
- Reviewed:
- Description: 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.
Edge data based trailer inception probabilistic matrix factorization for context-aware movie recommendation
- Chen, Honglong, Li, Zhe, Wang, Zhu, Ni, Zhichen, Li, Junjian, Xu, Ge, Aziz, Abdul, Xia, Feng
- Authors: Chen, Honglong , Li, Zhe , Wang, Zhu , Ni, Zhichen , Li, Junjian , Xu, Ge , Aziz, Abdul , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: World Wide Web Vol. 25, no. 5 (2022), p. 1863-1882
- Full Text:
- Reviewed:
- Description: The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Authors: Chen, Honglong , Li, Zhe , Wang, Zhu , Ni, Zhichen , Li, Junjian , Xu, Ge , Aziz, Abdul , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: World Wide Web Vol. 25, no. 5 (2022), p. 1863-1882
- Full Text:
- Reviewed:
- Description: The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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