Citation: Khalfaoui, H.; Azmani, A.;
Farchane, A.; Safi, S. Symbiotic
Combination of a Bayesian Network
and Fuzzy Logic to Quantify the QoS
in a VANET: Application in Logistic
4.0. Computers 2023, 12, 40. https://
doi.org/10.3390/computers12020040
Academic Editors: Phivos Mylonas,
Katia Lida Kermanidis, Manolis
Maragoudakis and Paolo Bellavista
Received: 19 December 2022
Revised: 11 January 2023
Accepted: 17 January 2023
Published: 14 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Symbiotic Combination of a Bayesian Network and Fuzzy Logic
to Quantify the QoS in a VANET: Application in Logistic 4.0
Hafida Khalfaoui
1,
* , Abdellah Azmani
2
, Abderrazak Farchane
1
and Said Safi
1
1
Department of Mathematics and Informatics, Sultan Moulay Slimane University, P.O. Box 592,
Beni Mellal 23000, Morocco
2
Intelligent Automation Laboratory, Abdelmalek Essaadi University, Tetouan 93000, Morocco
* Correspondence: hafida.khalfaoui@usms.ac.ma
Abstract:
Intelligent transportation systems use new technologies to improve road safety. In them,
vehicles have been equipped with wireless communication systems called on-board units (OBUs)
to be able to communicate with each other. This type of wireless network refers to vehicular ad
hoc networks (VANET). The primary problem in a VANET is the quality of service (QoS) because a
small problem in the services can extremely damage both human lives and the economy. From this
perspective, this article makes a contribution within the framework of a new conceptual project called
the Smart Digital Logistic Services Provider (Smart DLSP). This is intended to give freight vehicles
more intelligence in the service of logistics on a global scale. This article proposes a model that
combines two approaches—a Bayesian network and fuzzy logic for calculating the QoS in a VANET
as a function of multiple criteria—and provides a database that helps determine the originality of the
risk of degrading the QoS in the network. The outcome of this approach was employed in an event
tree analysis to assess the impact of the system’s security mechanisms.
Keywords:
quality of service; VANET; prediction; risk analysis; bayesian network; fuzzy logic;
fuzzy-bayesian; analysis tree event; smart DLSP
1. Introduction
The growth in freight transport due to the expansion of e-commerce and the global-
ization of industries and commercial exchanges has made logistics and transportation a
vital segment of most companies in the competitive market [
1
]. However, transportation
systems cause various safety and environmental issues. These issues include traffic-related
noise, air pollution, and increased vehicle accidents. Most countries face the challenge
of developing socioeconomic reforms that balance economic growth and environmental
sustainability [
2
]. To achieve this balance, new concepts and technologies, such as radio
frequency identification (RFID), robotics, and communication technologies, are used to
maximize productivity and minimize costs and errors.
This work is part of the Smart DLSP research project (Smart DLSP is a big project
registered in the Al Khawarizmi program launched in Morocco in May 2019). Its objective
is to develop a collaborative Logistics 4.0 solution that improves process efficiency while
reducing cost, effort, and errors. To achieve this goal, a carrier must be able to receive
and process customer requests dynamically, regardless of logistical constraints. For that
reason, a digital intelligent and decisional ecosystem (Ecodid) was created for the Smart
DLSP. It aims to give each actor and each object (vehicles, road environments, goods, and
warehouses) in the distribution chain an intelligent, collaborative, ubiquitous, and evolving
character within a consolidation framework.
Adopting a vehicular network in Ecodid provides a high level of visibility, automation,
control activities, and connectivity across the logistical environment and the entire supply
chain. It can potentially improve delivery efficiency and economic factors for industries, as
Computers 2023, 12, 40. https://doi.org/10.3390/computers12020040 https://www.mdpi.com/journal/computers
