Data Transmission In Vehicular Ad-hoc Network Research Articles

Dual-Slope Path Loss Model for Integrating Vehicular Sensing Applications in Urban and Suburban Environments.

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions.

A Clustering-Based Routing Protocol Using Path Pattern Discovery Method to Minimize Delay in VANET

In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communications can link vehicles to each other, and vehicle-to-infrastructure (V2I) messaging and communications can link roadside infrastructure such as routers. The vehicles in these networks act as relays that transmit critical messages in the network. Due to the high-speed movement of vehicles on the road, real-time messaging and minimizing the delay in sending messages is one of the most important objectives of VANET developers. On the other hand, the high mobility of vehicles causes communication interruptions and decreases the data delivery rate in VANET. To overcome this issue, predicting the path of vehicles can play an important role in sending data from the source to the destination. When an accident occurs on the road, the messages that are sensed by the imbedded sensors in the vehicles need to be sent, and if they are sent by the vehicles that change their route, these messages will not be sent to the destination and the performance of the network will be disturbed. Previous methods in the literature for data transmission in intervehicular networks have focused more on reliability and trust, and little attention has been paid to the prediction of vehicle movement paths in these types of networks. Therefore, for fast and reliable data transmission in VANET, accurate prediction of vehicle movement and creation of movement patterns can be effective in message transmission delay and data delivery rate. In this paper, we present an approach using a combination of cluster-based routing protocols and pattern discovery methods to minimize latency in VANETs. The outline of the proposed method has four modules: primary data collection and analysis, primary data preparation and analysis, pattern extraction and vehicle route discovery, and vehicle clustering and data/information transmission routing. The simulation results show that the proposed method with a delivery rate of 88.56% has significantly improved compared to the previous methods in terms of package delivery rate. Also, the proposed method with a total delay of 24.566 ms has a shorter delay than the previous methods in terms of message sending delay in the network.

Artificial Intelligence-Based Energy Efficient Communication System for Intelligent Reflecting Surface-Driven VANETs

The ever-increasing traffic, various delay-sensitive services, and energy consumption-constrained requirements have brought huge challenges to the current communication networks in the vehicular ad-hoc networks (VANETs). These challenges motivate academia and industry to investigate novel architectures with powerful data transmission and processing capabilities for low-latency and high energy-efficiency vehicular communication. In this paper, we propose an artificial intelligence (AI) and intelligent reflecting surface (IRS) empowered energy-efficiency communication system for VANETs. First, we design a smart and efficient hybrid vehicular communication framework, where IRS-aided dedicated short-range communication and long term evolution-based cellular communication are combined for data transmission in VANETs. Secondly, an IRS-aided data transmission is proposed to improve vehicular communication, in which the head vehicles selection method is designed. Based on the direct and IRS-reflecting signal propagation, fine-grained beamforming is achieved for directional vehicular transmission. Thirdly, a deep reinforcement learning (DRL) empowered network resource control and allocation scheme is proposed. In this scheme, we formulate an energy efficiency-maximizing model under the given transmission latency for VANETs and jointly optimize the settings of all participants to achieve efficient and low-latency communication. Finally, experimental results verify the effectiveness of our proposed communication system for VANETs.

Fuzzy-Based Secure Clustering with Routing Technique for VANETs

Due to the advanced developments in communication technologies, Internet of vehicles and vehicular adhoc networks (VANET) offers numerous opportunities for effectively managing transportation problems. On the other, the cloud environment needs to disseminate the emergency message to the vehicles which are consistently distributed on the roadway so that every vehicle gets the messages from closer vehicles in a straightforward way. To resolve this issue, clustering and routing techniques can be designed using computational intelligence approaches. With this motivation, this paper presents a new type-2 fuzzy sets based clustering with metaheuristic optimization based routing (T2FSC-MOR) technique for secure communication in VANET. The T2FSC-MOR technique aims to elect CHs and optimal routes for secure intercluster data transmission in VANET. The proposed model involves T2FSC technique for the selection of CHs and construction of clusters. The T2FSC technique uses different parameters namely traveling speed (TS), link quality (LQ), trust factor (TF), inter-vehicle distance (IVD), and neighboring node count (NCC). The inclusion of trust factor helps to select the proper cluster heads (CHs) for secure data dissemination process. Moreover, trust aware seagull optimization based routing (TASGOR) approach was derived for the optimal selection of routes in VANET. In order to validate the enhanced performance of proposed technique, the set of simulations take place and the outcomes are examined interms of different measures. The experimental outcomes highlighted the improved performance of the proposed model over the other state of art techniques with a higher throughput of 98%.

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world. VANET is susceptible to security issues, particularly DoS attacks, owing to maximum unpredictability in location. So, effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET. At the same time, congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections, for a traveler. With this motivation, the current research paper presents an intelligent DoS attack detection with Congestion Control (IDoS-CC) technique for VANET. The presented IDoS-CC technique involves two-stage processes namely, Teaching and Learning Based Optimization (TLBO)-based Congestion Control (TLBO-CC) and Gated Recurrent Unit (GRU)-based DoS detection (GRU-DoSD). The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network. TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization. TLBO is applied to avoid congestion on roadways. Besides, GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network. The outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

A novel approach to select cluster head and optimising the data transmission in VANET

Communication between the vehicles has a major role in intelligent transportation system (ITS). Vehicular ad hoc network (VANET) is developed to improve the road safety. Data transmission between the vehicles has the major challenge in VANET, because VANET has high mobility and dynamic topology. In VANET, the cluster based topology used to send the data from one vehicle to many vehicles without any data loss. Cluster head (CH) plays a vital role during the data transmission in cluster based routing. CH manages the cluster formation and data transmission in each cluster. In this paper, we proposed a cluster based approach to improve the data transmission. In this system a novel approach is proposed for choosing the CH. The CH selection algorithm is done in three levels, first level normal CH selection, second level is cluster leaving and finally cluster merging. After choosing the CH, the routing path to be established based on the link quality which is a measure using the link life time. The proposed system is implemented using the SUMO and NS3 tools.

Provable Secure Identity-Based Anonymous and Privacy-Preserving Inter-Vehicular Authentication Protocol for VANETS Using PUF

The concept of the smart city and the extensive use of vehicles are evolving progressively. This evolution makes vehicular ad-hoc network (VANET) a widely used network for inter-vehicular communication to get information about speed, traffic congestion, road condition, and vehicle location. This information is obtained from a public network, which is prone to various attacks. Generally speaking, data security in VANET is one of the most crucial tasks. We found many authentication protocols for VANETs; however, most of these protocols are insecure due to impersonation, replay, denial of service attacks, etc. Moreover, the existing protocols utilized heavy computation and communication costs, making them incompatible for resource-constrained VANET applications. This article presents a lightweight privacy-preserving authentication protocol for VANETs to offer a secure and efficient data transmission mechanism over a public channel. We analyze that our protocol is lightweight, safe, and scalable compared to related protocols. The communication and computation cost of our protocol is less concerning the state-of-the-art protocols. Hence, it is overt from the observed facts that our protocol is more reliable, convenient, and practical for secure data transmission in VANETs.

WITHDRAWN: Efficient multipath driven lossless video transmission for VANETS

Abstract In smart transport system, Vehicular Ad Hoc Networks (VANET) is considered as significant node. VANET is also depicted as one of the challenging topic to perform some transactions among the constituted network. Video transmission among the formed network is quite a difficult phase to handle. Different aspects are involved in the formation of networks and connect all of them to transmit some multimedia data. Various multipath routing protocols play a vital role in data transmission in VANETs. Routing protocols like position-based routing and distance-based routing are heavily used. Among the multipath environment, to identify the optimized path is the most tedious task to complete. Optimization of multipath routing can be possible by using some meta-heuristic algorithms. This paper covers the technique proposed for a multi hop environment and to select one path among many paths by using the Genetic Algorithm (GA).

A Lightweight Privacy-Preserving Authentication Protocol for VANETs

With the emergence of the concept of smart city and the increase of vehicles, the vehicular ad-hoc network (VANET) is widely accepted for the communication of vehicles to get information including road condition, traffic congestion description, speed, and location of vehicles. Naturally, the security of the data transmission in VANET has become one of the urgent tasks of researchers. Many privacy-preserving authentication protocols for VANETs have been presented. However, heavy computations and security flaws exist in them. Thus, we put forward a lightweight authentication protocol in a suitable communication model for VANET that meets the privacy protection needs, with only hash functions and exclusive-OR operations. Formal security analysis using BAN logic shows that our protocol achieves the security goals. Proverif is used to verify the security of the protocol and the results show that the privacy can be guaranteed under the simulated attacker. Moreover, through security properties analysis, our protocol is robust enough to get rid of common attacks and keep the communication data secretly. The performance comparison results show that our protocol is lightweight and efficient. Furthermore, we simulate the network with SUMO and NS-3, and all show that our protocol is efficient and practical for VANETs.

COMPASS: Directing Named Data Transmission in VANETs by Dynamic Directional Interfaces

Inefficient data transmission has been a development bottleneck of Vehicular Ad-hoc NETworks (VANETs), especially in urban areas. It has been proved that many complex IP-based solutions are difficult to be applied in the highly dynamic and link-interrupted vehicular environment. In recent years, Named Data Networking (NDN) has become the most popular realization of Information-Centric Networking (ICN) for future networks. Its characteristics of multi-source, multi-path and in-network caching are helpful for improving the data transmission in VANETs. However, the bottom layer of vehicles cannot provide multiple interfaces to different domains like the routers in wired networks. Thus interface-based forwarding degenerates into directionless broadcasting with low performance and high overhead. Against this problem, we propose COMPASS, a novel named data transmission protocol for VANETs. Firstly, a dynamic directional interface model is built as the cornerstone of our COMPASS. Secondly, the forwarding strategies are improved for rapid interest dissemination and named data retrieving. Besides, an interface remapping method and the update strategies of Forwarding Information Base (FIB) and Pending Interest Table (PIT) are designed to enhance the robustness in high-mobility environment. Finally, the performance of COMPASS is verified on ndnSIM. Compared with the other three state-of-the-art protocols, COMPASS obtains the highest interest satisfaction ratio and the shortest transmission delay in urban traffic scenarios with restricted communication and storage overhead.

Intelligent firefly-based algorithm with Levy distribution (FF-L) for multicast routing in vehicular communications

Vehicular ad hoc network (VANET) is an interesting technology used to attain inter-vehicle communication in intelligent transportation system (ITS). In VANET, the timely and unfailing vehicular communication is an important challenge to achieved good service. So, efficient routing techniques are highly needed for proper data transmission in VANET. In this paper, we concentrate on the quality of service (QoS) constrained multicast routing problem by considering it as an NP-complete problem. Several studies reported that bio-inspired algorithms work well than the traditional algorithms to resolve the NP-complete problem. So, a firefly with Levy distribution (FF-L) algorithm is presented to manage the multicast routing problem. To prevent the FF algorithm stuck into local optima, Levy distribution is incorporated to the FF algorithm. The FF-L algorithm for multicasting routing in VANET is validated using a set of experiments on three different scenarios. The experimental values verified that the FF-L algorithm identifies the optimal routes and it can be implemented in practical scenarios where the network structure remains quite stable.

Efficient and Reliable Cluster-Based Data Transmission for Vehicular Ad Hoc Networks

Vehicular ad hoc network (VANET) is an emerging technology for the future intelligent transportation systems (ITSs). The current researches are intensely focusing on the problems of routing protocol reliability and scalability across the urban VANETs. Vehicle clustering is testified to be a promising approach to improve routing reliability and scalability by grouping vehicles together to serve as the foundation for ITS applications. However, some prominent characteristics, like high mobility and uneven spatial distribution of vehicles, may affect the clustering performance. Therefore, how to establish and maintain stable clusters has become a challenging problem in VANETs. This paper proposes a link reliability-based clustering algorithm (LRCA) to provide efficient and reliable data transmission in VANETs. Before clustering, a novel link lifetime-based (LLT-based) neighbor sampling strategy is put forward to filter out the redundant unstable neighbors. The proposed clustering scheme mainly composes of three parts: cluster head selection, cluster formation, and cluster maintenance. Furthermore, we propose a routing protocol of LRCA to serve the infotainment applications in VANET. To make routing decisions appropriate, we nominate special nodes at intersections to evaluate the network condition by assigning weights to the road segments. Routes with the lowest weights are then selected as the optimal data forwarding paths. We evaluate clustering stability and routing performance of the proposed approach by comparing with some existing schemes. The extensive simulation results show that our approach outperforms in both cluster stability and data transmission.

An Efficient Prediction-Based Data Forwarding Strategy in Vehicular Ad Hoc Network

As an important infrastructure of intelligent transportation system (ITS), vehicular ad hoc networks (VANETs) provide navigation and positioning, information services, driver assistance, in-vehicle entertainment, and other functions for the owners improving the development of the future smart city. Due to the high dynamic nature of VANETs, choosing a relatively stable and reliable transmission path to reduce the link breakage is obviously very important. In this paper, we propose an efficient prediction-based data forwarding strategy to ensure the reliability and efficiency of data transmission in VANETs. Besides the link lifetime prediction, we introduce a new metric named link utility used in the process of forwarder selection, in order to improve data forwarding efficiency. Link utility reflects the impact of relative velocity and distance between the vehicles on the efficiency of intervehicle data transmissions. It can be used to optimize packet routing and minimize number of hops especially in the high-density VANETs. Finally, we evaluate the performance of the proposed forwarding strategy through simulation confirming its feasibility and effectiveness.