Spatiotemporal Self-Attention-Based Network Traffic Prediction in IIoT

The sixth-generation (6G) mobile communications are considered as a future network and very closed to the Industrial Internet of Things (IIoT) due to its low latency and high throughput. Massive nodes supported by 6G make up the complexity of the network. Moreover, the heterogeneous traffic brings difficulties to the network management. Long-term network traffic matrix (TM) prediction is a crucial technology for realizing network edge intelligence and dealing with the above issues. However, predicting long-term network traffic in heterogeneous IIoT is challenging. Due to the powerful feature extraction capability over long sequences, self-attention is widely applied in language inference tasks. Motivated by these observations, we propose a self-attention traffic matrix prediction (SATMP) model for long-term network TM prediction in IIoT scenarios. SATMP consists of three components: (a) a spatial–temporal encoding for obtaining the spatial–temporal features of network TM; (b) a learnable positional encoding for providing positional correlation to the traffic sequence; and (c) a self-attention module for capturing long-term dependence. These components work together to enhance long-term prediction performance in complex networks effectively. Extensive experiments on three publicly available datasets demonstrate that SATMP is feasible and accurate in IIoT long-term network TM prediction.

• Publication year

  2023

• Venue

  Wireless Communications and Mobile Computing

• Publication date

  2023-07-14

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1155/2023/8331642
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Edge Intelligence-Driven Joint Offloading and Resource Allocation for Future 6G Industrial Internet of Things

  2024cited by this paper
• Dynamic Computation Offloading and Server Deployment for UAV-Enabled Multi-Access Edge Computing

  2023cited by this paper
• An EMD and ARMA-based network traffic prediction approach in SDN-based internet of vehicles

  2021cited by this paper
• 6G Internet of Things: A Comprehensive Survey

  2021cited by this paper
• 5G-Enabled UAV-to-Community Offloading: Joint Trajectory Design and Task Scheduling

  2021cited by this paper
• 6G Opportunities Arising from Internet of Things Use Cases: A Review Paper

  2021cited by this paper
• Intelligent resource allocation in mobile blockchain for privacy and security transactions: a deep reinforcement learning based approach

  2021cited by this paper
• Network Traffic Prediction in Industrial Internet of Things Backbone Networks: A Multitask Learning Mechanism

  2021cited by this paper
• Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework

  2021cited by this paper
• A Federated Learning-Based License Plate Recognition Scheme for 5G-Enabled Internet of Vehicles

  2021cited by this paper
• A lightweight model with spatial–temporal correlation for cellular traffic prediction in Internet of Things

  2021cited by this paper
• Research on GRU Neural Network Satellite Traffic Prediction Based on Transfer Learning

  2021cited by this paper
• LNTP: An End-to-End Online Prediction Model for Network Traffic

  2021cited by this paper
• Distributed and Dynamic Service Placement in Pervasive Edge Computing Networks

  2021cited by this paper
• Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

  2021cited by this paper
• A Reinforcement Learning-Based Network Traffic Prediction Mechanism in Intelligent Internet of Things

  2021cited by this paper
• Mobile Edge Cooperation Optimization for Wearable Internet of Things: A Network Representation-Based Framework

  2021cited by this paper
• Dynamic UAV Deployment for Differentiated Services: A Multi-Agent Imitation Learning Based Approach

  2021cited by this paper
• Machine learning, Prophet and XGBoost algorithm: Analysis of Traffic Forecasting in Telecom Networks with time series data

  2020cited by this paper
• Industrial internet of things: Recent advances, enabling technologies and open challenges

  2020cited by this paper
• Network traffic prediction method based on wavelet transform and multiple models fusion

  2020cited by this paper
• Internet Traffic Volumes are Not Gaussian—They are Log-Normal: An 18-Year Longitudinal Study With Implications for Modelling and Prediction

  2020cited by this paper
• Network Traffic Prediction Using Long Short-Term Memory

  2020cited by this paper
• A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction

  2020cited by this paper
• A deep learning method based on an attention mechanism for wireless network traffic prediction

  2020influential reference
• From Design to Practice: ETSI ENI Reference Architecture and Instantiation for Network Management and Orchestration Using Artificial Intelligence

  2020cited by this paper
• Network traffic prediction based on INGARCH model

  2020cited by this paper
• Network Traffic Prediction in Network Security Based on EMD and LSTM

  2020cited by this paper
• Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges

  2020cited by this paper
• Spatial-Temporal Attention-Convolution Network for Citywide Cellular Traffic Prediction

  2020cited by this paper
• Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

  2020cited by this paper
• A New Traffic Prediction Algorithm to Software Defined Networking

  2019cited by this paper
• Network Traffic Prediction based on Diffusion Convolutional Recurrent Neural Networks

  2019cited by this paper
• An effective spatial-temporal attention based neural network for traffic flow prediction

  2019cited by this paper
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

  2019cited by this paper
• Efficient Attention: Attention with Linear Complexities

  2018cited by this paper
• Deep Learning-Based Traffic Prediction for Network Optimization

  2018cited by this paper
• DeepTP: An End-to-End Neural Network for Mobile Cellular Traffic Prediction

  2018cited by this paper
• Attention is All you Need

  2017cited by this paper
• Network Traffic Prediction Based on Deep Belief Network in Wireless Mesh Backbone Networks

  2017cited by this paper
• Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks

  2017cited by this paper
• A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction

  2017cited by this paper
• NeuTM: A neural network-based framework for traffic matrix prediction in SDN

  2017cited by this paper
• A Structured Self-attentive Sentence Embedding

  2017cited by this paper
• Applying deep learning approaches for network traffic prediction

  2017influential reference
• Gaussian Process Regression Based Traffic Modeling and Prediction in High-Speed Networks

  2016cited by this paper
• Mobile Network Traffic Prediction Using MLP, MLPWD, and SVM

  2016cited by this paper
• Inter-data-center network traffic prediction with elephant flows

  2016cited by this paper
• A multi-source dataset of urban life in the city of Milan and the Province of Trentino

  2015cited by this paper
• CORINE Land Cover and Land Cover Change Products

  2014cited by this paper
• Arima model for network traffic prediction and anomaly detection

  2008cited by this paper
• Providing public intradomain traffic matrices to the research community

  2006cited by this paper
• Voronoi diagram and convex hull based geocasting and routing in wireless networks

  2003cited by this paper

• No citing papers are available for this paper.