Leveraging Artificial Intelligence to Enhance Energy Efficiency in Internet of Things Implemented in Vehicular Ad-hoc Networks (IoT in VANETs)

• Auteur / Autrice : Zeinab Ezzeddine
• Direction : Habiba Ouslimani, Besma Zeddini, Khalil Ayman
• Type : Projet de thèse
• Discipline(s) : Génie Informatiques automatique et traitement du signal
• Date : Inscription en doctorat le 04/04/2024
• Etablissement(s) : Paris 10
• Ecole(s) doctorale(s) : École doctorale Connaissance, langage et modélisation
• Partenaire(s) de recherche : Laboratoire : Laboratoire Énergétique Mécanique Électromagnétisme

Mots clés

Mots clés libres

Artificial Intelligence

Vanet

IoT

Résumé

The Internet of Things (IoT) has emerged as a transformative technology that connects billions of devices and sensors, enabling them to collect and exchange data to make our lives more convenient and efficient [1]. Vehicular Ad-hoc Networks (VANETs) have become a critical component of modern transportation systems, with applications ranging from traffic management to vehicle-to-vehicle (V2V) communication. The integration of IoT technology into VANETs offers promising opportunities for improving transportation safety, traffic management, and vehicular communication [3]. However, the energy efficiency of IoT devices in the context of VANETs is a significant concern. One of the primary challenges facing IoT deployment is the limited energy resources of these devices, which often rely on batteries or energy-harvesting mechanisms. In addition to consuming high amount of energy is a challenge faced in implementing IoT technologies. The unique characteristics of VANETs, such as mobility and dynamic communication patterns, demand innovative solutions to optimize energy usage [2]. To address this challenge, this proposed Ph.D. research aims to investigate and develop advanced machine learning techniques to enhance energy efficiency and head for green IoT systems implemented in VANETs.