Open Access

ARTICLE

Design and Develop Function for Research Based Application of Intelligent Internet-of-Vehicles Model Based on Fog Computing

by Abduladheem Fadhil Khudhur^*, Ayça Kurnaz Türkben, Sefer Kurnaz

Department of Electrical and Computer Engineering, Altinbaş University, Istanbul, 34000, Turkey

* Corresponding Author: Abduladheem Fadhil Khudhur. Email:

(This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)

Computers, Materials & Continua 2024, 81(3), 3805-3824. https://doi.org/10.32604/cmc.2024.056941

Received 03 August 2024; Accepted 08 October 2024; Issue published 19 December 2024

Abstract

The fast growth in Internet-of-Vehicles (IoV) applications is rendering energy efficiency management of vehicular networks a highly important challenge. Most of the existing models are failing to handle the demand for energy conservation in large-scale heterogeneous environments. Based on Large Energy-Aware Fog (LEAF) computing, this paper proposes a new model to overcome energy-inefficient vehicular networks by simulating large-scale network scenarios. The main inspiration for this work is the ever-growing demand for energy efficiency in IoV-most particularly with the volume of generated data and connected devices. The proposed LEAF model enables researchers to perform simulations of thousands of streaming applications over distributed and heterogeneous infrastructures. Among the possible reasons is that it provides a realistic simulation environment in which compute nodes can dynamically join and leave, while different kinds of networking protocols-wired and wireless-can also be employed. The novelty of this work is threefold: for the first time, the LEAF model integrates online decision-making algorithms for energy-aware task placement and routing strategies that leverage power usage traces with efficiency optimization in mind. Unlike existing fog computing simulators, data flows and power consumption are modeled as parameterizable mathematical equations in LEAF to ensure scalability and ease of analysis across a wide range of devices and applications. The results of evaluation show that LEAF can cover up to 98.75% of the distance, with devices ranging between 1 and 1000, showing significant energy-saving potential through A wide-area network (WAN) usage reduction. These findings indicate great promise for fog computing in the future-in particular, models like LEAF for planning energy-efficient IoV infrastructures.

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