Toward Concurrent Video Multicast Orchestration for Caching-Assisted Mobile Networks

Zhang, Xinchang; Wang, Yinglong; Yang, Meihong; Geng, Guanggang

doi:10.1109/tvt.2021.3119429

Cited by 44 publications

(16 citation statements)

References 32 publications

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“…There are many methods of data scaling, such as Min-Max Normalization, Min-Max Normalization, also known as the extreme difference method, is the simplest way to deal with the magnitude problem, which is to scale the value of a column in the data set to between 0 and 1. It is calculated as (2). A single element is denoted as X, the minimum value in the dataset is denoted as X min , and the maximum value in the data set is denoted as X max .…”

Section: ) Data Scalingmentioning

confidence: 99%

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Network Traffic Prediction Based on LSTM and Transfer Learning

Wan

Liu

et al. 2022

IEEE Access

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The increasing amount of traffic in recent years has led to increasingly complex network problems. To be able to improve overall network performance and increase network utilization, it is valuable to take measures to capture future trends in network traffic. In traditional machine learning, to guarantee the accuracy and high reliability of the models obtained through training, there are two basic assumptions: (1) the training samples used for learning and the new test samples satisfy the condition of independent identical distribution; and (2) there must be enough training samples to learn a good model. However, time-series data are not easily accessible in real life, and even after putting in a lot of time and effort to collect them, the data may be unavailable due to confidentiality. In this paper, a neural network model based on LSTM and transfer learning is proposed to address the problem of small sample size in network traffic prediction. Knowledge in the source domain is transferred to the target domain using transfer learning, and a prediction model with good performance is constructed with a small amount of target domain data. The results show that the performance of the transfer learning model improves by more than 40% over the direct training model when using the same samples for predicting 10,000 rows of data, resulting in better performance of the network traffic prediction task.INDEX TERMS LSTM, network traffic prediction, transfer learning.

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Section: ) Data Scalingmentioning

confidence: 99%

“…The increase in network traffic makes the network situation more and more complex. Therefore, a large number of solutions (e.g., [2], [3]) have been proposed to optimize the network traffic.…”

Section: Introductionmentioning

confidence: 99%

Network Traffic Prediction Based on LSTM and Transfer Learning

Wan

Liu

et al. 2022

IEEE Access

Self Cite

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“…Smart contract vulnerability detection using graph neural networks and expert knowledge was explored by Liu et al [36]. The proposed solution by Zhang et al [37] is aimed at achieving rapid video prefetching and traffic reduction. With their new detection method, Zong et al [38]applied a multiscale grouping (MSG) structure to a 3D point cloud tunnel dataset and applied a 3D-BoNet instance segmentation model.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili

Goki

Masjidi

et al. 2022

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.

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“…e sense of presence brought by the corresponding situation mainly drives the viewer's emotions [39][40][41][42][43][44][45]. e shaping and construction of the situation take the viewer's emotions and perspective as the main reference, mobilizing the viewer to generate many associations in the constructed virtual scene.…”

Section: Impact Analysis Of Ai and Mr Technologymentioning

confidence: 99%

Interactive Display of Images in Digital Exhibition Halls under Artificial Intelligence and Mixed Reality Technology

Liu¹,

Zhang

2022

Computational Intelligence and Neuroscience

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The attractiveness of traditional exhibition halls to young people is gradually decreasing. Combining modern digital technology to improve the display effect of the exhibition hall can effectively enhance the effect of cultural publicity. This article introduces the technology of image interaction and mixed reality (MR) to improve the historical and cultural propaganda level of the Shaanxi exhibition hall. The advantages of MR technology in applying digital exhibition halls are theoretically expounded. A theoretical plan for Shaanxi history and culture-related display areas is designed using artificial intelligence combined with MR technology. In addition, the survey respondent’s evaluation of the effect of the new exhibition hall is obtained using a questionnaire survey. The survey results show that 97% of people like the history and culture of Shaanxi but only 13% of the people say they know or know very well about the history and culture of Shaanxi. In addition, 60% of the tourists say they are satisfied with the cultural experience of Shaanxi, and only 27% of the tourists are very satisfied. Also, 96% of tourists are willing to experience Shaanxi’s history and culture through digital exhibition halls, and 93% are willing to participate in cultural experience activities based on MR technology. The survey results prove that tourists are satisfied with the effect of the new exhibition hall. Tourists want to add a distinctive form of cultural experience to the exhibition hall. They are willing to accept digital exhibition halls incorporating MR technology and are very happy to participate in the exhibition method of image interaction. This shows that the use of image interactive display based on MR technology in the layout of the exhibition hall is recognized by people and has strong feasibility. This article has reference significance for the digital upgrade of the exhibition hall and the development of the cultural tourism industry.

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Toward Concurrent Video Multicast Orchestration for Caching-Assisted Mobile Networks

Cited by 44 publications

References 32 publications

Network Traffic Prediction Based on LSTM and Transfer Learning

Network Traffic Prediction Based on LSTM and Transfer Learning

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Interactive Display of Images in Digital Exhibition Halls under Artificial Intelligence and Mixed Reality Technology

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