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1
Genetic algorithm‐based content distribution strategy for F‐RAN architectures

Xujie Li,Ziya Wang,Ying Sun,Siyuan Zhou,Yanli Xu,Guoping Tan 한국전자통신연구원 2019 ETRI Journal Vol.41 No.3
- 원문보기 2
  ScienceON
  
  KCI
Fog radio access network (F‐RAN) architectures provide markedly improved performance compared to conventional approaches. In this paper, an efficient genetic algorithm‐based content distribution scheme is proposed that improves the throughput and reduces the transmission delay of a F‐RAN. First, an F‐RAN system model is presented that includes a certain number of randomly distributed fog access points (F‐APs) that cache popular content from cloud and other sources. Second, the problem of efficient content distribution in F‐RANs is described. Third, the details of the proposed optimal genetic algorithm‐based content distribution scheme are presented. Finally, simulation results are presented that show the performance of the proposed algorithm rapidly approaches the optimal throughput. When compared with the performance of existing random and exhaustive algorithms, that of the proposed method is demonstrably superior.
2
Task offloading under deterministic demand for vehicular edge computing

Haotian Li,Xujie Li,Fei Shen Electronics and Telecommunications Research Instit 2023 ETRI Journal Vol.45 No.4
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In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0-1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility.
3
Next-generation sequencing for the genetic characterization of Maedi/Visna virus isolated from the northwest of China

Ling Zhao,Liang Zhang,Xiaona Shi,Xujie Duan,Huiping Li,Shuying Liu 대한수의학회 2021 Journal of Veterinary Science Vol.22 No.6
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  ScienceON
  
  DBpia
Background: Maedi/Visna virus (MVV) is a contagious viral pathogen that causes considerable economic losses to the sheep industry worldwide. Objectives: In China, MVV has been detected in several regions, but its molecular characteristics and genetic variations were not thoroughly investigated. Methods: Therefore, in this study, we conducted next-generation sequencing on an MVV strain obtained from northwest China to reveal its genetic evolution via phylogenetic analysis. Results: A MVV strain obtained from Inner Mongolia (NM) of China was identified. Sequence analysis indicated that its whole-genome length is 9193 bp. Homology comparison of nucleotides between the NM strain and reference strains showed that the sequence homology of gag and env were 77.1%–86.8% and 67.7%–75.5%, respectively. Phylogenetic analysis revealed that the NM strain was closely related to the reference strains isolated from America, which belong to the A2 type. Notably, there were 5 amino acid insertions in variable region 4 and a highly variable motif at the C-terminal of the surface glycoprotein (SU5). Conclusions: The present study is the first to show the whole-genome sequence of an MVV obtained from China. The detailed analyses provide essential information for understanding the genetic characteristics of MVV, and the results enrich the MVV library.