Real-time traffic congestion mapping and bottleneck analysis using an edge-deployable lightweight deep learning framework for UAV-assisted monitoring

Deep Learning for Optimizing Virtual Resources Allocation in MEC-based UAV Environments

A virtualization-aware MEC framework that integrates GRU-based CPU and memory demand forecasting with dynamic virtual resource allocation in UAV-assisted environments and shows the effectiveness of proactive virtualization-aware orchestration for the UAV-assisted MEC systems is proposed.

AI-Assisted Dynamic RSU Deployment Using Real-Time Traffic Density Estimation in VANETs

Simulation results demonstrate that the proposed AI-driven framework significantly improves network throughput, reduces communication latency, and ensures stable connectivity compared to traditional static RSU deployment strategies.

LiteUAV-Det: An Efficient Lightweight Deep Learning Framework for UAV-to-UAV Small Target Detection in Complex Aerial Scenes

Detecting small UAV targets in air-to-air scenarios is important for aerial security, autonomous surveillance, and multi-UAV defense systems. However, this task remains challenging due to severe scale variation, motion blur, and complex aerial backgrounds. Existing detectors suffer from performance degradation on extre...

Deep Learning and Computer Vision-Based Modeling for a Real-Time Traffic Congestion Monitoring Framework

As intelligent traffic systems evolve to manage complex urban mobility, conventional congestion estimation techniques, such as the time-windowed Volume-to-Capacity (V/C) ratio, fail to capture capture the real-time traffic situation. Because these methods rely on vehicles crossing a specific point, they often fail to r...

Edge-Assisted Vehicle Perception Using DNN-Based Elastic Models

Deep Learning-Driven Throughput Prediction in 5G for UAV-Assisted Emergency Response

Mobile and 5G networks offer high data rates, yet they remain vulnerable to congestion during emergency events, potentially degrading service for users and first responders. Accurate throughput prediction is essential for optimizing resource allocation in such scenarios. This paper proposes a deep learning-driven frame...