Open Access

ARTICLE

Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles

Manar Ahmed Hamza^1,*, Haya Mesfer Alshahrani², Fahd N. Al-Wesabi^3,4, Mesfer Al Duhayyim⁵, Anwer Mustafa Hilal¹, Hany Mahgoub³

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia
3 Department of Computer Science, King Khalid University, Muhayel Aseer, Saudi Arabia
4 Faculty of Computer and IT, Sana'a University, Sana'a, Yemen
5 Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2022, 70(3), 5835-5853. https://doi.org/10.32604/cmc.2022.021059

Received 21 June 2021; Accepted 28 July 2021; Issue published 11 October 2021

Abstract

With recent advances made in Internet of Vehicles (IoV) and Cloud Computing (CC), the Intelligent Transportation Systems (ITS) find it advantageous in terms of improvement in quality and interactivity of urban transportation service, mitigation of costs incurred, reduction in resource utilization, and improvement in traffic management capabilities. Many traffic-related problems in future smart cities can be sorted out with the incorporation of IoV in transportation. IoV communication enables the collection and distribution of real-time essential data regarding road network condition. In this scenario, energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban computing. With this motivation, the current research article presents a new Artificial Intelligence-based Energy Efficient Clustering with Routing (AI-EECR) Protocol for IoV in urban computing. The proposed AI-EECR protocol operates under three stages namely, network initialization, Cluster Head (CH) selection, and routing protocol. The presented AI-EECR protocol determines the CHs from vehicles with the help of Quantum Chemical Reaction Optimization (QCRO) algorithm. QCRO algorithm derives a fitness function with the help of vehicle speed, trust level, and energy level of the vehicle. In order to make appropriate routing decisions, a set of relay nodes was selected using Group Teaching Optimization Algorithm (GTOA). The performance of the presented AI-EECR model, in terms of energy efficiency, was validated against different aspects and a brief comparative analysis was conducted. The experimental outcomes established that AI-EECR model outperformed the existing methods under different measures.

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Keywords

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