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- FlexMon: A flexible and fine-grained traffic monitor for programmable . . .
Emerging programmable networking techniques provide programmable, flexible, and fine-grained traffic control capabilities, paving the way for realizing fine-grained and accurate traffic measurements In this paper, we aim to design efficient traffic measurement schemes for programmable networks
- Road Network Guided Fine-Grained Urban Traffic Flow Inference
Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem, which can help greatly reduce the number of the required traffic monitoring sensors for cost savings In this work, we notice that traffic flow has a high correlation with road network, which was either completely ignored or simply treated as an external factor in previous works To
- Federated Deep Reinforcement Learning for Traffic Monitoring in SDN . . .
This paper proposes a novel traffic monitoring framework, namely, DeepMonitor, for SDN-based IoT networks to provide fine-grained traffic analysis capability for different IoT traffic types at the network edges Specifically, we first develop an intelligent flow rule match-field control system, called DeepMonitor agent, for SDN-based IoT edge nodes, taking different granularity-level
- Towards integrated and fine-grained traffic forecasting: A Spatio . . .
This paper develops a novel approach called Spatio-Temporal Heterogeneous Graph Transformer (STHGFormer) for integrated and fine-grained traffic forecasting The proposed method addresses the challenges posed by the complex relationships between roads and turns and their heterogeneous spatio-temporal attributes
- Deep Learning Enabled Fine-Grained Path Planning for Connected . . .
In this paper, to alleviate the ever-increasing traffic congestion in urban areas by accommodating higher road traffic, we develop traffic prediction framework together with path planning method for connected vehicular networks First, through the employment of convolutional neural network (CNN) and residual unit (RN), deep learning (DL) based fine-grained traffic prediction algorithm is
- [2306. 10945] FDTI: Fine-grained Deep Traffic Inference with Roadnet . . .
This paper proposes the fine-grained traffic prediction task (e g interval between data points is 1 minute), which is essential to traffic-related downstream applications Under this setting, traffic flow is highly influenced by traffic signals and the correlation between traffic nodes is dynamic As a result, the traffic data is non-smooth between nodes, and hard to utilize previous methods
- Study on Fine-Grained Traffic Pollution Monitoring in Urban Area
Based on the field data, the effective mobile monitoring sampling frequency and monitoring network placement strategy to support the fine-grained traffic pollution distribution inference in a smart city are discussed
- Traffic flow monitoring in software-defined network using modified . . .
The following will explain this architecture’s objectives and operational workflow By changing among several of the traffic flow monitoring systems in the data plane dynamically, the variation in traffic flow granularity may be regulated It is crucial to carry out fine-grained traffic flow monitoring in advance for anomaly detection
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