MobiWM is a multimodal world model for mobile networks that learns state-action dynamics to enable unlimited-horizon counterfactual traffic simulations and optimization.
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting , url=
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IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
GeoGNN is a two-tower GNN that learns geographic cell embeddings from adjacency graphs and matches them to temporal representations via dot-product similarity plus classification, improving geolocalization accuracy by ~27% on electricity datasets.
An algorithm selects traffic counter locations to increase observed traffic-pattern diversity; real-world installation of the chosen counters improved volume estimation accuracy.
The Time-Geometric model combines GNNs for geometric patterns with temporal models and reports statistically significant accuracy gains in financial time series forecasting.
The paper provides a structured overview of IoE concepts, components, architectures, enabling technologies, challenges, and open research directions for 6G-enabled IoE systems.
citing papers explorer
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Beyond Static Forecasting: Unleashing the Power of World Models for Mobile Traffic Extrapolation
MobiWM is a multimodal world model for mobile networks that learns state-action dynamics to enable unlimited-horizon counterfactual traffic simulations and optimization.
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Incident-Guided Spatiotemporal Traffic Forecasting
IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
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GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
GeoGNN is a two-tower GNN that learns geographic cell embeddings from adjacency graphs and matches them to temporal representations via dot-product similarity plus classification, improving geolocalization accuracy by ~27% on electricity datasets.
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Selecting New Measurement Locations to Diversify Traffic-Pattern Coverage: A Real-World Evaluation for Total Traffic Volume Estimation
An algorithm selects traffic counter locations to increase observed traffic-pattern diversity; real-world installation of the chosen counters improved volume estimation accuracy.
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The Statistical Significance of the Inclusion of Graph Neural Networks in the Financial Time Series Forecasting Problem
The Time-Geometric model combines GNNs for geometric patterns with temporal models and reports statistically significant accuracy gains in financial time series forecasting.
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Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions
The paper provides a structured overview of IoE concepts, components, architectures, enabling technologies, challenges, and open research directions for 6G-enabled IoE systems.