Software defined vehicular networks: A comprehensive review

Bhatia, Jitendra; Modi, Yash; Tanwar, Sudeep; Bhavsar, Madhuri

doi:10.1002/dac.4005

Cited by 89 publications

(30 citation statements)

References 70 publications

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“…Jianqi et al 24 presents an architecture for better communication, reducing traffic congestion and improving the current urban traffic management with the help of rapidly emerging technologies such as fifth‐generation (5G), software‐defined networking (SDN), and mobile edge computing (MEC) 30,31 . The SDN‐based heterogeneous network with the convergence of VANETs, 5G wireless networks, and SDN technologies collectively have the potential to provide high‐frequency band, and low‐lag communication services and programmability 32 . The MEC platform architecture offers services like vehicle localization, traffic data processing, traffic prediction, and traffic light control.…”

Section: Taxonomymentioning

confidence: 99%

Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

Nama

Nath

Bechra

et al. 2021

Int J Communication

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Summary The rise in traffic congestion has become a significant concern in the urban city environment. The conventional traffic control systems with inefficient human resources management fail to control the traffic discipline, leading to increased traffic density and road offenses. However, the intelligent transportation system (ITS) can provide safety, efficiency, and sustainability for large‐scale vehicular traffic dilemmas. ITS unites machine learning with the available traffic control force and performs real‐time police scheduling to ensure the smooth flow of traffic. Many researchers have demonstrated notable work in intelligent traffic police scheduling and deployment using various optimization algorithms. However, the compilation of such praiseworthy work as a whole is still missing. Motivated by these facts, we provide a comprehensive review of the machine learning‐based state‐of‐the‐art technologies that can be used to form a three‐tier solution taxonomy. The first tier describes various tools and technologies that can be utilized to collect traffic data. The second tier highlights the machine learning algorithms and their accuracy, which forms a pattern in the collected data and then yields some crucial information about traffic flow, congestion levels, and so forth. In the third tier, the most vital taxonomy layer, various traffic police scheduling strategies are discussed. The proposed survey also presents the use of cases of traffic police scheduling that elaborates this review's applicability in various domains. Finally, some of the key challenges in the subject being reviewed are discussed, which initiates a further scope of improvement.

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Section: Taxonomymentioning

confidence: 99%

Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

Nama

Nath

Bechra

et al. 2021

Int J Communication

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“…Henceforth, solid parameters for choosing the trusted vehicles in a network are needed. Analysts have proposed a technique for troublemaking identification, yet no methodologies are available for using these data [127]. The penalties are not well characterized for malignant vehicles.…”

Section: Trustworthiness Evaluation Misbehavior Detection and Revocmentioning

confidence: 99%

SDN-based VANETs, Security Attacks, Applications, and Challenges

et al. 2020

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Vehicular ad-hoc networks (VANETs) are the specific sort of ad-hoc networks that are utilized in intelligent transportation systems (ITS). VANETs have become one of the most reassuring, promising, and quickest developing subsets of the mobile ad-hoc networks (MANETs). They include smart vehicles, roadside units (RSUs), and on-board units (OBUs) which correspond through inconsistent wireless network. The current research in the vehicles industry and media transmission innovations alongside the remarkable multimodal portability administrations expedited center-wise ITS, of which VANETs increase considerably more attention. The particular characteristics of the software defined networks (SDNs) use the vehicular systems by its condition of the centralized art having a complete understanding of the network. Security is an important issue in the SDN-based VANETs, as a result of the effect the threats and vulnerabilities can have on driver’s conduct and personal satisfaction. This paper opens a discourse on the security attacks that future SDN-based VANETs should confront and examines how SDNs could be advantageous in building new countermeasures. SDN-based VANETs encourage us to dispose of the confinement and difficulties that are available in the traditional VANETs. It helps us to diminish the general burden on the system by dealing with the general system through a single wireless controller. While SDN-based VANETs provide us some benefits in terms of applications and services, they also have some important challenges which need to be solved. In this study we discuss and elaborate the challenges, along with the applications, and the future directions of SDN-based VANETs. At the end we provide the conclusion of the whole study.

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“…Traffic monitoring, adaptive traffic signals, accident avoidance, monitoring the road condition, traffic divert alerts, etc. are the essential application of IoT revolution for the intelligent transportation system (ITS) [15]. Various IoT technologies can work jointly to provide better Fig.…”

Section: Smart Transportation and Road Safetymentioning

confidence: 99%

Emerging Communication Technologies for 5G-Enabled Internet of Things Applications

Kumhar

Bhatia²

2020

Blockchain for 5g-Enabled IoT

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The IoT has gained much interest recently due to its ability to connect many realworld objects. It can play a vital role in many domains to improve the quality of life in different areas such as smart homes, medical health, education, industry, agriculture, transports, and smart cities. Now there is a paradigm shift towards the concept of "anytime, anywhere, anyone, connected to anything." In many areas, whether it is our homes or workplace, the devices are becoming essential for our daily lifestyle. IoT has made it possible to connect everyone with everything in the environment [1]. Connecting different devices and adding sensors enhance a level of intelligence to devices, otherwise they would be dumb, making them transfer up-todate data without any human involvement. The organization of the chapter is shown in Fig. 6.1. Contribution and MotivationInternet of Things (IoT) is very important for the upcoming 5G mobile networks, which will enable several innovative IoT applications which include Self-driving cars, Healthcare, Smart cities, and other enormous IoT use cases set in 5G standards.

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Software defined vehicular networks: A comprehensive review

Cited by 89 publications

References 70 publications

Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

SDN-based VANETs, Security Attacks, Applications, and Challenges

Emerging Communication Technologies for 5G-Enabled Internet of Things Applications

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