Traffic Management Based on Cloud and MEC  Architecture with Evolutionary Approaches towards AI: A Review

Zainab Saadoon Naser; Hend Marouane Belguith; Ahmed Fakhfakh

doi:10.3991/ijoe.v20i12.49787

Authors

Zainab Saadoon Naser Sfax University
Hend Marouane National School of Electronics and Telecommunications (ENET‟COM), NTS‟COM, Sfax University, Tunisia
Ahmed Fakhfakh https://orcid.org/0009-0005-3219-2371

DOI:

https://doi.org/10.3991/ijoe.v20i12.49787

Keywords:

, machine learning, , Deep Learning, Reinforcement Learning, smart traffic management system, cloud computing, mobile computing, Edge Computing, scalability, and Energy, Efficiency, Security and privacy

Abstract

This review paper explores the significance of machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) techniques in improving traffic management based on cloud and mobile edge computing (MEC) architectures. The key findings and contributions of this review highlight the potential of these techniques for transforming traffic management systems through data-driven decision-making, adaptive control, and optimization. The challenges identified in this field include data availability and quality, scalability and computational requirements, privacy and security concerns, and ethical considerations. In conclusion, ML, DL, RL, and DRL techniques, in conjunction with cloud and MEC architectures, have significant implications for improving traffic management. Their ability to process and analyse large-scale and real-time traffic data enables improved traffic flow, reduced congestion, enhanced energy efficiency, and enhanced overall transportation system performance.

Author Biographies

Hend Marouane, National School of Electronics and Telecommunications (ENET‟COM), NTS‟COM, Sfax University, Tunisia

Hend Marouane Belguith received the engineering degree and the Master
degree from the National School of Engineering of Sfax (ENIS), Tunisia. She
has a diploma in wireless communication from Engineering School of
Communications (SUP‟COM, TUNISIA) in 2002. She received the PhD degree
engineering from ENIS in 2010. She is now working as an Assistant Professor
in the National School of Electronics and Telecommunication (ENET‟COM) of
Sfax, Tunisia. She is a member of NTS‟COM laboratory in ENET‟COM. Her
research interests include wireless and mobile network, advanced protocols for
mobile communication and signal processing.

Ahmed Fakhfakh

Ahmed Fakhfakh is a full professor in the National School of Electronics and Telecommunications of Sfax (ENET’Com) at the university of Sfax in Tunisia, since 2015. He is the General Director of the Digital Research Center of Sfax (CRNS) since 2023. He has obtained his HDR diploma from Sfax university in 2009, his PhD diploma from Bordeaux university, France, in 2002 and the electrical engineering diploma in 1997 from Sfax National School of engineering (ENIS), Tunisia.

He is the head of the research group ‘Intelligent Systems : design and implementation ‘ at the Laboratory of Signals, systeMs, aRtificial Intelligence and neTworkS (SM@RTS) in the digital research centre of Sfax in Tunisia.

His research interest deals with the development of smart solutions for the energy management in a smart grid, the design and implementation of IoT solutions, the design of wake-up solutions for Wireless sensor network application and the design of energy harvesting solutions.