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Intelligent Vehicular Networks and Communication Systems
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Intelligent Vehicular Networks and Communication Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 6861

Special Issue Editors

Prof. Wessam Ajib

E-Mail Website
Guest Editor
Department of Computer Science, Université du Québec à Montréal (UQAM), Montreal, QC H2L 2C4, Canada
Interests: wireless communications systems; wireless networks; vehicular networks; multiple access design; traffic scheduling and radio resource allocation
Dr. Dariush Ebrahimi

E-Mail Website
Guest Editor
Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
Interests: Internet of Things (IoT); smart vehicular transportations and wireless communications; autonomous driving; intelligent transportation systems; UAV-assisted vehicular networks cloud and mobile edge computing; data mining and machine learning; algorithm design and optimization; IoT and cyber security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, industries, governments, and municipalities of major cities across the world are pursuing the vision of smart cities and automation. One of the important sectors toward this vision is intelligent transportation systems (ITS), which are gaining substantial attention owing to the great benefits they offer to vehicular networks and vehicle users, such as data exchange, autonomous driving, internet browsing, entertainment applications, and many more. This vision massively relies on information and communication technologies required for data processing, gathering, and transmission among Internet of Things (IoT) devices (e.g., traffic and streetlights, connected vehicles, etc.). The rush to digitization has resulted in a significant growth in the number of Internet-connected things, which has created the need for an intelligent monitoring and management of IoT systems to deal with the massive requirements that are beyond current resources’ and networks’ capabilities.

This Special Issue addresses several aspects and challenges in ITSs and proposes smart solutions and algorithms for a better performance of intelligent vehicular networks and communication systems.

Prof. Wessam Ajib
Dr. Dariush Ebrahimi
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Intelligent transportation systems (ITS)
  • Vehicular networks (VANET)
  • Internet of Things (IoT)
  • Data and wireless communications
  • Autonomous driving
  • Artificial Intelligence (AI) on smart city applications

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Published Papers (2 papers)

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Research

21 pages, 2630 KiB  
Article
A Fuzzy-Based Context-Aware Misbehavior Detecting Scheme for Detecting Rogue Nodes in Vehicular Ad Hoc Network
by Fuad A. Ghaleb, Faisal Saeed, Eman H. Alkhammash, Norah Saleh Alghamdi and Bander Ali Saleh Al-rimy
Sensors 2022, 22(7), 2810; https://doi.org/10.3390/s22072810 - 6 Apr 2022
Cited by 12 | Viewed by 2796
Abstract
A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs’ applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at a high [...] Read more.
A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs’ applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at a high rate due to high vehicles mobility. However, rogue nodes, which exploit the co-operativeness feature and share false messages, can disrupt the fundamental operations of any potential application and cause the loss of people’s lives and properties. Unfortunately, most of the current solutions cannot effectively detect rogue nodes due to the continuous context change and the inconsideration of dynamic data uncertainty during the identification. Although there are few context-aware solutions proposed for VANET, most of these solutions are data-centric. A vehicle is considered malicious if it shares false or inaccurate messages. Such a rule is fuzzy and not consistently accurate due to the dynamic uncertainty of the vehicular context, which leads to a poor detection rate. To this end, this study proposed a fuzzy-based context-aware detection model to improve the overall detection performance. A fuzzy inference system is constructed to evaluate the vehicles based on their generated information. The output of the proposed fuzzy inference system is used to build a dynamic context reference based on the proposed fuzzy inference system. Vehicles are classified into either honest or rogue nodes based on the deviation of their evaluation scores calculated using the proposed fuzzy inference system from the context reference. Extensive experiments were carried out to evaluate the proposed model. Results show that the proposed model outperforms the state-of-the-art models. It achieves a 7.88% improvement in the overall performance, while a 16.46% improvement is attained for detection rate compared to the state-of-the-art model. The proposed model can be used to evict the rogue nodes, and thus improve the safety and traffic efficiency of crewed or uncrewed vehicles designed for different environments, land, naval, or air. Full article
(This article belongs to the Special Issue Intelligent Vehicular Networks and Communication Systems)
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20 pages, 2789 KiB  
Article
Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices
by Leo Tišljarić, Filip Vrbanić, Edouard Ivanjko and Tonči Carić
Sensors 2022, 22(7), 2807; https://doi.org/10.3390/s22072807 - 6 Apr 2022
Cited by 10 | Viewed by 3124
Abstract
Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology [...] Read more.
Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that supports Connected Vehicles (CVs), which act as an active mobile sensor for collecting traffic data, provides an opportunity to harness the large datasets to develop novel methods regarding traffic bottlenecks detection. This paper presents a speed transition matrix based model for bottleneck probability estimation on motorways. The method is based on the computation of the speed at the vehicle transition point between consecutive motorway segments, which forms a traffic pattern that is represented using transition matrices. The main feature extracted from the traffic patterns was the center of mass, whose position is used as an input to the fuzzy-based system for bottleneck probability estimation. The proposed method is evaluated on four different simulated motorway traffic scenarios: (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms. Full article
(This article belongs to the Special Issue Intelligent Vehicular Networks and Communication Systems)
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