Energy-aware resource management in Internet of vehicles using machine learning algorithms

Internet of Vehicles (IoV) presents a new generation of vehicular communications with limited computation offloading, energy and memory resources with 5G/6G technologies that have grown enormously and are being used in wide variety of Intelligent Transportation Systems (ITS). Due to the limited batt...

Full description

Saved in:
Bibliographic Details
Published in:Journal of high speed networks 2023-01, Vol.29 (1), p.27-39
Main Authors: Chen, Sichao, Hu, Yuanchao, Huang, Liejiang, Shen, Dilong, Pan, Yuanjun, Pan, Ligang
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Computation offloading
Energy consumption
Energy management
Intelligent transportation systems
Intelligent vehicles
Internet of Vehicles
Machine learning
Optimization
Resource management
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Internet of Vehicles (IoV) presents a new generation of vehicular communications with limited computation offloading, energy and memory resources with 5G/6G technologies that have grown enormously and are being used in wide variety of Intelligent Transportation Systems (ITS). Due to the limited battery power in smart vehicles, the concept of energy consumption is one of the main and critical challenges of the IoV environments. Optimizing resource management strategies for improving the energy consumption using AI-based methods is one of important solutions in the IoV environments. There are various machine learning algorithms for selecting optimal solutions for energy-efficient resource management strategies. This paper presents the existing energy-aware resource management strategies for the IoV case studies, and performs a comparative analysis among their applied AI-based methods and machine learning algorithms. This analysis presents a technical and deeper understanding of the technical aspects of existing machine learning and AI-based algorithms that will be helpful in design of new hybrid AI approaches for optimizing resource management strategies with reducing their energy consumption.
ISSN:0926-6801
1875-8940
DOI:10.3233/JHS-222004