RT info:eu-repo/semantics/article
T1 Deep Reinforcement Learning-Based Task Scheduling and Resource Allocation for Vehicular Edge Computing: A Survey
A1 Li, Peisong
A1 Wang, Xinheng
A1 Li, Changle
A1 Iqbal, Muddesar
A1 Al-Dulaimi, Anwer
A1 I, Chih-Lin
A1 Casaseca de la Higuera, Juan Pablo
K1 Computación periférica vehicular
K1 Aprendizaje profundo por refuerzo
K1 Programación de tareas
K1 Asignación de recursos
AB With the development of intelligent transportation systems, vehicular edge computing (VEC) has played a pivotal role by integrating computation, storage, and analytics closer to the vehicles. VEC represents a paradigm shift towards real-time data processing and intelligent decision-making, overcoming challenges associated with latency and resource constraints. In VEC scenarios, the efficient scheduling and allocation of computing resources are fundamental research areas, enabling real-time processing of vehicular tasks and intelligent decision-making. This paper provides a comprehensive review of the latest research in Deep Reinforcement Learning (DRL)-based task scheduling and resource allocation in VEC environments. Firstly, the paper outlines the development of VEC and introduces the core concepts of DRL, shedding light on their growing importance in the dynamic VEC landscape. Secondly, the state-of-the-art research in DRL-based task scheduling and resource allocation is categorized, reviewed, and discussed. Finally, the paper discusses current challenges in the field, offering insights into the promising future of VEC applications within the realm of intelligent transportation systems.
PB IEEE
SN 1524-9050
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/78725
UL https://uvadoc.uva.es/handle/10324/78725
LA eng
NO IEEE Transactions on Intelligent Transportation Systems, 2025, p. 1-30.
NO Producción Científica
DS UVaDOC
RD 19-oct-2025