Age of Information and Energy-Efficiency Optimization in RIS-Assisted Vehicular Edge Computing via Hierarchical Deep Reinforcement Learning

Jun Lan; Xiangdong Jia; Zhizhuo Kou

doi:10.1109/jiot.2025.3598039

ScienceGate Book Chapters

JOURNAL ARTICLE

Age of Information and Energy-Efficiency Optimization in RIS-Assisted Vehicular Edge Computing via Hierarchical Deep Reinforcement Learning

Jun Lan Xiangdong Jia Zhizhuo Kou

Year: 2025 Journal: IEEE Internet of Things Journal Vol: 12 (20)Pages: 43681-43695 Publisher: Institute of Electrical and Electronics Engineers

DOI: 10.1109/jiot.2025.3598039

Get Full-Text PDF Get Analytical Report

Keywords:

Reinforcement learning Computer science Enhanced Data Rates for GSM Evolution Edge computing Efficient energy use Artificial intelligence Electrical engineering

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.32

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Age of Information Optimization

Physical Sciences → Computer Science → Computer Networks and Communications

IoT and Edge/Fog Computing

Physical Sciences → Computer Science → Computer Networks and Communications

IoT Networks and Protocols

Physical Sciences → Engineering → Electrical and Electronic Engineering

Age of Information and Energy-Efficiency Optimization in RIS-Assisted Vehicular Edge Computing via Hierarchical Deep Reinforcement Learning

Metrics

Topics

Related Documents

Energy-Efficiency Optimization of Active Flying-RIS-Assisted Mobile-Edge Computing Networks: A Deep-Reinforcement-Learning Approach

Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning

Energy Harvesting-Based UAV-Assisted Vehicular Edge Computing: A Deep Reinforcement Learning Approach

Efficient Task Offloading in Double Roadside RIS-Assisted Vehicular Edge Computing Networks Using Deep Reinforcement Learning

Deep Reinforcement Learning for Vehicular Edge Computing