| Title |
Multi-dimensional Task Offloading using Deep Learning for Vehicular Edge Computing Networks: A Survey |
| Authors |
(S. Syed Abuthahir) ; (J. Selvin Paul Peter) |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.1.1 |
| Keywords |
Deep learning; Machine learning; Mobile edge computing; Optimization techniques; Task offloading; Vehicular edge computing networks |
| Abstract |
Vehicular networks must support connection ubiquity and high levels of services for a large number of vehicles. In vehicular networks, mobile edge computing (MEC) is considered a viable technique, utilizing computing resources at the edge of wireless access networks. This survey examines effectual task offloading systems forvehicular edge computing (VEC) networks. Several deep learning methods have been suggested to enable task offloading schemes. Therefore, task offloading optimality is a main research topic in fog computing. The main aim of this work is to offer readers an overview of the journey from the task offloading concept to its mathematic problem formulation. This survey introduces fog computing and the process of task offloading that emulates numerous aspects of optimization. Subsequently, a number of machine learning and deep learning methodologies are employed in task offloading,and the challenge of fog computing is discussed. This paper provides a detailed statistical analysis from papers published between 2019 and 2022. |