G
Gaocai Wang
Researcher at Guangxi University
Publications - 5
Citations - 28
Gaocai Wang is an academic researcher from Guangxi University. The author has contributed to research in topics: Mobile edge computing & Energy consumption. The author has an hindex of 3, co-authored 5 publications receiving 13 citations.
Papers
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Journal ArticleDOI
Study on Dynamic Service Migration Strategy with Energy Optimization in Mobile Edge Computing
TL;DR: The optimization strategy proposed can effectively reduce the energy consumption of dynamic service migration in different simulation environments and can improve the dynamic migration performance.
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Study on Stochastic Differential Game Model in Network Attack and Defense
TL;DR: Compared with other kinds of literature, the return on security investment of this model is better, and the strategy selection of the attackers and defenders in the model is more suitable for actual network attack and defense.
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An Attribute-Based Access Control Policy Retrieval Method Based on Binary Sequence
Ruijie Pan,Gaocai Wang,Man Wu +2 more
TL;DR: Wang et al. as mentioned in this paper proposed an attribute access control policy retrieval method based on the binary sequence, which uses binary identification and binary code to express access control requests and policies, and the appropriate group is selected through the logical operation of the access control request and the policy binary identification.
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Risk Situation Assessment Model Based on Interdomain Interaction in Cloud Computing Environment
Gaocai Wang,Ning Yu +1 more
TL;DR: The experimental results show that the evaluation method proposed in this paper improves the accuracy of the evaluation results and meets the security requirements of multidomain interaction.
Journal ArticleDOI
Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing
TL;DR: In this paper, the authors proposed a task migration energy optimization strategy with resource caching by combining optimal stopping theory with migration decision-making, and proved an optimal stopping rule's existence, obtained the optimal processing energy consumption threshold, and compared it with device energy consumption.