Stacked denoising autoencoder for missing traffic data reconstruction via mobile edge computing
Traffic sensing system requires to periodically collect spatial–temporal traffic data distributed
among road networks, which results in overhigh bandwidth consumption and storage cost in …
among road networks, which results in overhigh bandwidth consumption and storage cost in …
An approximation algorithm for joint data uploading and task offloading in IoV
This paper investigates cooperative data uploading and task offloading in heterogeneous
Internet of Vehicles (IoV). Specifically, considering the characteristics that different tasks may …
Internet of Vehicles (IoV). Specifically, considering the characteristics that different tasks may …
Unveiling the impact of heterogeneous driving behaviors on traffic flow: A mesoscale multi-agent modeling approach
L Wu, Z Sun, J Liu, D Shan, X Ma, T Zhu - Computers and Electrical …, 2024 - Elsevier
There are fewer simulation studies that comprehensively consider the impact of collision
events due to heterogeneous driving behaviour on multi-lane traffic flow. This …
events due to heterogeneous driving behaviour on multi-lane traffic flow. This …
Cooperative multi-agent reinforcement learning framework for edge intelligence empowered traffic light control
Edge Intelligence (EI) technologies obtain an advance with promotion by Consumer
Electronics (CE) and spread to the Intelligent Transportation System (ITS). As part of the …
Electronics (CE) and spread to the Intelligent Transportation System (ITS). As part of the …
Coverage Maximization of Heterogeneous UAV Networks
In this paper we study the deployment of a UAV (unmanned aerial vehicle) network that
consists of multiple UAVs to provide emergent communication services to people trapped in …
consists of multiple UAVs to provide emergent communication services to people trapped in …
Unification of probabilistic graph model and deep reinforcement learning (UPGMDRL) for multi-intersection traffic signal control
AR Sattarzadeh, PN Pathirana - Knowledge-Based Systems, 2024 - Elsevier
Traffic signals play a pivotal role in modern life by preventing collisions, regulating traffic
flow, and ensuring a predictable and efficient transportation system. Adaptive traffic light …
flow, and ensuring a predictable and efficient transportation system. Adaptive traffic light …
Adaptive Broad-Deep Reinforcement Learning for Intelligent Traffic Light Control
R Zhu, S Wu, L Li, W Ding, P Lv… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) has superior autonomous decision-making capabilities,
combining deep learning and reinforcement learning. Unlike DRL employs deep neural …
combining deep learning and reinforcement learning. Unlike DRL employs deep neural …
ParallEdge: Exploiting computing-mobility parallelism for efficient 5G/6G edge computing
With the emergence of the 5G/6G communications, edge computing has attracted increasing
research interests in recent years. To provide pervasive 5G/6G edge computing services …
research interests in recent years. To provide pervasive 5G/6G edge computing services …
Multi-agent dual actor-critic framework for reinforcement learning navigation
F Xiong, Y Zhang, X Kuang, L He, X Han - Applied Intelligence, 2025 - Springer
Multi-Agent navigation task remains a fundamental challenge in robotics and autopilots.
Reinforcement learning approaches to navigation often struggle to address the value …
Reinforcement learning approaches to navigation often struggle to address the value …
An Effecient Joint Source-Channel Coding Scheme for Wireless Hierarchical Federated Learning and Its Information-Theoretic Analysis
H Zhang, K Li, K Zhao, P Xu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Wireless hierarchical federated learning (WHFL) is an implementation of wireless federated
learning (WFL) on a cloud-edge-client hierarchical architecture that accelerates model …
learning (WFL) on a cloud-edge-client hierarchical architecture that accelerates model …