VANET routing protocol for V2V implementation: A suitable solution for developing countries

Yasser, Ahmed; Zorkany, M.; Kader, Neamat Abdel

doi:10.1080/23311916.2017.1362802

Cited by 43 publications

(21 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, VANET networks most often use the ad hoc on-demand distance vector (AODV) [27]. Like all distance vector routing protocols, the AODV protocol is characterized by a relatively long rerouting time.…”

Section: An Overview Of Routing Methods In Transport Networkmentioning

confidence: 99%

Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing

et al. 2020

View full text Add to dashboard Cite

This paper addresses traffic engineering (TE) issues in software-defined vehicular networking (SDVN). A brief analysis of the features of SDVN, which improves the efficiency of TE in SDVN, is presented. The feasibility of using multi-path routing with TE is substantiated. A procedure and an example of the formation of multiple routes based on a modified wave routing algorithm are given. Considering the features of the SDVN technology, a modified TE method is proposed, which reduces both the time complexity of forming multiple paths and the path reconfiguration time. The dynamic path reconfiguration algorithm is presented. INDEX TERMS Intelligent transportation system (ITS), multi-path routing, software-defined network, traffic engineering (TE). I. INTRODUCTION The ever-growing number of vehicles on the roads makes the vehicular ad-hoc network (VANET) an important research topic in the field of automotive and wireless technologies. VANETs are considered as a subclass of classic mobile ad hoc networks (MANETs) [1]-[3]. Where vehicles play the role of mobile nodes of the VANETs. That have embedded advanced equipment on-board, traveling on restricted routes (i.e., roads, streets and lanes), and communicating with each other for information exchange using vehicle-to-vehicle (V2V) communication protocols, and also between vehicles and roadside units (RSU) installed on the side of the roads (i.e., wireless and cellular network infrastructure), to form a link between a Vehicle-to-Infrastructure (V2I). Compared to static or low-speed moving nodes in a traditional wireless network, vehicles move faster and more unpredictably, resulting in frequent changes to VANET network settings [4]-[7]. This places higher demands on routing protocols in VANET networks. Most well-known routing methods are not effective for VANET networks [8]-[11]. In this regard, the urgent The associate editor coordinating the review of this manuscript and approving it for publication was Chao Chen.

show abstract

Section: An Overview Of Routing Methods In Transport Networkmentioning

confidence: 99%

Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The analysis showed that AODV performed better when it comes to the lower density of nodes. In order to show the importance of efficient and fast traffic flow, [28] carried out experiments using CBR. The results obtained showed that the CBR outperforms the GPSR, AODV, and the DSR protocols in terms of PDR and the message overhead.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Data Dissemination Protocols for Connected Autonomous Vehicles

et al. 2020

View full text Add to dashboard Cite

An overwhelmed number of vehicles have wrecked the current system of transportation due to rapid growth in population. Smart cities are the novel innovation that is inevitable to curb the problems of traffic jams, unorganized traffic, environmental pollution, and slow response rate to emergency situations. The intelligent transportation system (ITS) is an integral part of smart cities allowing communications and interaction among vehicles. An autonomous vehicle is the key element of ITS and the mass implementation of this emerging technology is the solution to traffic problems linked to the current transportation system. Autonomous vehicles lead to the need for efficient and reliable external vehicular communications particularly through vehicular ad hoc networks (VANET). However, utilizing a suitable routing protocol to provide stable routing and efficient performance for vehicular communications in autonomous vehicles is a key factor. Routing protocols are particularly important for establishing vehicular to vehicular and vehicular to infrastructure (V2X) communication, which is incredibly challenging due to the movement of nodes. The quality of inter-vehicular communications is widely affected by numerous factors such as routing protocols, traffic environment, and traffic density. This article presents a detailed evaluation of three commonly used protocols, i.e., Ad-hoc On-demand Distance Vector Routing (AODV), Dynamic Source Routing (DSR), and Destination-Sequenced Distance-Vector Routing (DSDV) under three different traffic environments. To investigate the performance of these routing protocols under diverse environments, simulations are extended further by using the varying density of vehicles. This study aims at finding the best routing protocol for efficient and reliable packet dissemination among vehicles under different scenarios.

show abstract

“…The proposed method uses a V2V routing protocol. Yasser et al uses a routing protocol and the results of simulation are performed between the proposed and existing methods over various several performance metrics.…”

Section: Introductionmentioning

confidence: 99%

Routing using reinforcement learning in vehicular ad hoc networks

Saravanan

Ganeshkumar

2020

Computational Intelligence

View full text Add to dashboard Cite

In vehicular ad hoc networks (VANETs), the frequent change in vehicle mobility creates dynamic changes in communication link and topology of the network. Hence, the key challenge is to address and resolve longer transmission delays and reduced transmission stability. During the establishment of routing path, the focus of entire research is on traffic detection and road selection with high traffic density for increased packet transmission. This reduces the transmission delays and avoids carry‐and‐forward scenarios; however, these techniques fail in obtaining accurate traffic density in real‐time scenario due to rapid change in traffic density. Thus, it is necessary to create a model that efficiently monitors the traffic density and assist VANETs in route selection in an automated way with increased accuracy. In this article, a novel machine learning architecture using deep reinforcement learning (DRL) model is proposed to monitor and estimate the data essential for the routing protocol. In this model, the roadside unit maintains the traffic information on roads using DRL. The DRL predicts the movement of the vehicle and makes a suitable routing path for transmitting the packets with improved transmission capacity. It further uses predicted transmission delays and the destination location to choose the forwarding directions between two road safety units (RSUs). The application of DRL over VANETs yields increased network performance, which provides on‐demand routing information. The simulation results show that the DRL‐based routing is effective in routing the data packets between the source and destination vehicles than other existing method.

show abstract

VANET routing protocol for V2V implementation: A suitable solution for developing countries

Cited by 43 publications

References 8 publications

Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing

Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing

Performance Evaluation of Data Dissemination Protocols for Connected Autonomous Vehicles

Routing using reinforcement learning in vehicular ad hoc networks

Contact Info

Product

Resources

About