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

Article type: Research Article

Authors: Chen, Sichao^a | Hu, Yuanchao^{b; *} | Huang, Liejiang^a | Shen, Dilong^a | Pan, Yuanjun^a | Pan, Ligang^a

Affiliations: [a] Hangzhou Xinmei Complete Electric Appliance Manufacturing Co., Ltd, Hangzhou, China | [b] Shandong University of Technology, Zibo, Shandong, China

Correspondence: [*] Corresponding author. E-mail: huyuanchao3211@126.com.

Abstract: 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.

Keywords: Internet of Vehicles (IoV), energy consumption, vehicular communications, resource management, machine learning, optimization algorithms

DOI: 10.3233/JHS-222004

Journal: Journal of High Speed Networks, vol. 29, no. 1, pp. 27-39, 2023