Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network

Pan, Chengsheng; Wang, Yuyue; Shi, Huaifeng; Shi, Jianfeng; Cai, Ren

doi:10.3390/s22072674

Cited by 15 publications

(12 citation statements)

References 25 publications

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“…Understanding how to deeply analyze complex and diverse data through machine learning and make efficient use of information has become one of the main problems paid attention to by big data. Pan Chengsheng et al [ 9 ] (2022) used the neutral net to achieve traffic prediction. Meanwhile, Lin Guancen et al [ 10 ] (2022) succeeded in traffic prediction based on the traditional machine learning method.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

Shi

2022

Sensors

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As the aggravation of road congestion leads to frequent traffic crashes, it is necessary to relieve traffic pressure through traffic flow prediction. As well, the traffic flow of the target road section to be predicted is also closely related to the adjacent road sections. Therefore, in this paper, a prediction method based on the combination of multiple linear regression and Long-Short-Term Memory (MLR-LSTM) is proposed, which uses the incomplete traffic flow data in the past period of time of the target prediction section and the continuous and complete traffic flow data in the past period of time of each adjacent section to jointly predict the traffic flow changes of the target section in a short time. The accurate prediction of future traffic flow changes can be solved based on the model supposed when the traffic flow data of the target road section is partially missing in the past period of time. The accuracy of the prediction results is the same as that of the current mainstream prediction results based on continuous and non-missing target link flow data. Meanwhile, there is a small-scale improvement when the data time interval is short enough. In the case of frequent maintenance of cameras in actual traffic sections, the proposed prediction method is more feasible and can be widely used.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

Shi

2022

Sensors

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“…Proactive network management, where base station/access points take proactive decisions based on their previously encountered traffic patterns, decisions taken and their future predicted traffic patterns, can significantly enhance the QoS in the network. Besides traffic classification, real-time traffic prediction can also help in efficient mapping of network resources to user demands [131]. These user demands keep changing at different times of day and the traffic variations also occur due to user mobility which shifts traffic load from one access point to another.…”

Section: Traffic Prediction At the Edgementioning

confidence: 99%

“…Different approaches and learning algorithms have been proposed in literature. Authors in [131] included prior knowledge in information fusion to train neural networks and achieved a 10% improvement over statistical traffic predictions. Similarly, authors in [132] and [133] were able to achieve MAE of 0.3 and 0.002% using LSTMs and regression models respectively.…”

Section: Role Of Ai and ML In Distributed Network Management And Edge...mentioning

confidence: 99%

AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions

Zia

Chiumento

Havinga

2022

IEEE Open J. Commun. Soc.

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Wireless IoT networks have seen an unprecedented rise in number of devices, heterogeneity and emerging use cases which led to diverse throughput, reliability and latency (Quality of Service) requirements. Fulfilling these diverse requirements in a rapidly changing and dynamic wireless environment is a complex and challenging task. On top of including new technologies and wireless standards, one solution is to deploy cross-layer Design (CLD) and multiple Radio Access Technologies (Multi-RAT) to develop scalable QoS-aware IoT networks. However, the complexity of such solutions is high as it involves complex inter-layer interactions and dependencies and inter-application dependencies in multi-RAT networks. Moreover, the wireless environment is very dynamic, so having an optimal constellation of parameters is a challenging task. In this paper, we address the possibilities of using Artificial Intelligence (AI) and Machine Learning (ML) to address these high dimensional and dynamic problems. Based on our findings, we have proposed a distributed network management framework employing AI & ML for studying inter-layer dependencies and developing cross-layer design, traffic classification and traffic prediction at the edge devices for reliable QoS in multi-RAT IoT networks. A thorough discussion on future directions and emerging challenges related to our proposed framework has also been provided for further research in this field.INDEX TERMS QoS in IoT networks, AI & ML for cross-layer design, cross-layer optimization, reliable QoS, multi-RAT networks, edge intelligence.

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“…When the trough of network traffic is predicted, reducing the transmission power of base stations and letting some of them go into dormancy are helpful to reduce the energy consumption of base stations. Therefore, accurate network traffic prediction is important to improve the quality of service in communication networks [5], [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning Method Based on CEEMDAN and Attention Mechanism for Network Traffic Prediction

2023

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Accurate prediction of network traffic trends is important for self-management, intelligent scheduling and network resource optimization of base stations. Network traffic prediction is a prerequisite for intelligent scheduling of base stations, and accurate prediction will be beneficial for improving network utilization and energy saving in scheduling. In this paper, a hybrid deep learning method for network traffic prediction, CEEMDAN-TGA which consists of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Temporal Convolutional Network (TCN), Gated Recurrent Unit (GRU), and Attention Mechanism is proposed. Firstly, CEEMDAN is introduced to decompose the original network traffic data into different modes, then reconstruct the modes into trend sequence and noise sequence. Secondly, TCN is used to extract the short-term local features in the network traffic, GRU is used to obtain the longterm data-dependent features, and the attention mechanism is used to improve the prediction accuracy and stability. Finally, through the comparison of experiments, the prediction effect and accuracy of the proposed method are verified to have significant advantages, and the network traffic scheduling strategy is proposed on the basis of prediction.INDEX TERMS Network traffic prediction; deep learning; complete ensemble empirical mode decomposition with adaptive noise; temporal convolutional network; gated recurrent unit; attention mechanism.

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Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network

Cited by 15 publications

References 25 publications

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions

A Hybrid Deep Learning Method Based on CEEMDAN and Attention Mechanism for Network Traffic Prediction

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