Network Traffic Prediction Using Recurrent Neural Networks

Ramakrishnan, Nipun; Soni, Tarun Kumar

doi:10.1109/icmla.2018.00035

Cited by 130 publications

(72 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, the nonparametric model has its advantages in this respect. The representative methods such as Bayesian network model [12,13], support vector regression model (SVR) [35,36], K-Nearest Neighbor model [37], and neural network model [38] use just historical traffic information to automatically learn statistics.…”

Section: Relatedmentioning

confidence: 99%

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

Zhang

Xin

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Accurate and real-time network traffic flow forecast holds an important role for network management. Especially at present, virtual reality (VR), artificial intelligence (AI), vehicle-to-everything (V2X), and other technologies are closely combined through the mobile network, which greatly increases the human-computer interaction activities. At the same time, it requires high-throughput, low delay, and high reliable service guarantee. In order to achieve ondemand real-time high-quality network service, we must accurately grasp the dynamic changes of network traffic. However, due to the increase of client mobility and application behavior diversity, the complexity and dynamics of network traffic in the temporal domain and the spatial domain increase sharply. To accurate capture the spatiotemporal features, we propose the spatial-temporal graph convolution gated recurrent unit (GC-GRU) model, which integrates the graph convolutional network (GCN) and the gated recurrent unit (GRU) together. In this model, the GCN structure could handle the spatial features of traffic flow with network topology, and the GRU is used to further process spatiotemporal features. Experiments show that the GC-GRU model has better prediction performance than other baseline models and can obtain spatial-temporal correlation in traffic lows better.

show abstract

Section: Relatedmentioning

confidence: 99%

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

Zhang

Xin

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The traffic condition in the next time step is predicted according to the information of past traffic patterns. The hyperparameters of the RNN are similar to [23]. So far, the traffic prediction between nodes is finished, and the enhanced node will use this information to make further routing decisions.…”

Section: End For End Formentioning

confidence: 99%

Deep Reinforcement Learning-Based Multi-Hop State-Aware Routing Strategy for Wireless Sensor Networks

et al. 2021

View full text Add to dashboard Cite

With the development of wireless sensor network technology, the routing strategy has important significance in the Internet of Things. An efficient routing strategy is one of the fundamental technologies to ensure the correct and fast transmission of wireless sensor networks. In this paper, we study how to combine deep learning technology with routing technology to propose an efficient routing strategy to cope with network topology changes. First, we use the recurrent neural network combined with the deep deterministic policy gradient method to predict the network traffic distribution. Second, the multi-hop node state is considered as the input of a double deep Q network. Therefore, the nodes can make routing decisions according to the current state of the network. Multi-hop state-aware routing strategy based on traffic flow forecasting (MHSA-TFF) is proposed. Simulation results show that the MHSA-TFF can improve transmission delay, average routing length, and energy efficiency.

show abstract

“…An improved RNN named Long Short Term Memory (LSTM) [147], including complex gates and memory cells within the hidden units for "better memories", became popular in various applications such as speech recognition [126], video analysis [335], language translation [215], activity recognition [130] etc. Since data streaming is most common in the IoT environment, RNN (LSTM) is deemed as one of the most powerful modelling techniques, and there are various IoT applications such as smart assistant [109,336], smart car navigator system [161], malware threat hunting [134], network traffic forecasting [272], equipment condition forecasting [387], energy demand prediction system [243], load forecasting [181], etc.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications

Qian

Wen

et al. 2020

ACM Comput. Surv.

View full text Add to dashboard Cite

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This paper provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy, and subsequently assess and compare existing standard techniques used at individual stages.

show abstract

Network Traffic Prediction Using Recurrent Neural Networks

Cited by 130 publications

References 17 publications

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

Deep Reinforcement Learning-Based Multi-Hop State-Aware Routing Strategy for Wireless Sensor Networks

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications

Contact Info

Product

Resources

About