Vehicles In Vehicular Ad-hoc Networks Research Articles

An optimized routing algorithm for vehicle ad-hoc networks

Efficient routing algorithms are essential to guarantee reliable communication in Vehicular Adhoc Networks (VANETs). In this paper, we present a twofold approach entailing the design of a new route metric for VANET communication, which considers important parameters such as the received signal strength; transmit power, frequency and the path loss. We further present an improved genetic algorithm-based route optimization technique (IGAROT) that guarantees better routing in VANETs. We used IGAROT to determine optimal routes required to communicate road anomalies effectively between vehicles in VANETs. The performance of our proposed algorithm was compared with the well-known conventional Genetic Algorithm (GA) route optimization technique under same simulation conditions. Based on the average route results obtained, our findings indicate that IGAROT provided 4.24%, 75.7% and 420% increment over the conventional GA in the low, medium and high car density scenarios, respectively. Our findings suggest that IGAROT improves road anomaly communication among vehicles thus enabling drivers to better navigate anomalous roads with the aim to reduce road-anomaly induced accidents. Further benefits of our system may include the prompt notification of road maintenance agencies concerning persisting road conditions via vehicle to infrastructure communication.

Effect of security and trustworthiness for a fuzzy cluster management system in VANETs

Recently, smart cities and Internet of Things (IoT) applications, such as Vehicular Ad-hoc Networks (VANETs) and Opportunistic networks have been deeply investigated. However, these kinds of wireless networks have security problems. Also, the vehicles can be not trustworthy, which brings different communication problems. In this work, we present a Fuzzy Cluster Management System (FCMS) for VANETs. We present and compare two fuzzy-based system models: FCMS1 and FCMS2 for clustering of vehicles in VANETs. For FCMS1, we use three input parameters: Vehicle Relative Speed with Vehicle Cluster (VRSVC), Vehicle Degree of Centrality (VDC) and Vehicle Security (VS). The output parameter is Vehicle Remain or Leave Cluster (VRLC). For FCMS2, we consider four input parameters by adding Vehicle Trustworthiness (VT) as a new parameter. We evaluate both systems by simulations. The simulation results show that vehicles with the same VRSVC and with high VDC, VS and VT values have higher possibility to remain in the cluster. By comparing FCMS1 and FCMS2, we found that the FCMS2 can manage better the vehicles in the cluster than FCMS1.

Secure Tilted-Rectangular-Shaped Request Zone Location-Aided Routing Protocol (STRS-RZLAR) for Vehicular Ad Hoc Networks

Securing data communication in Vehicular Ad Hoc Networks (VANETs) becomes a demand to provide safe and secure warning messages to drivers. In this paper, a secure routing protocol for VANETs was proposed. The protocol integrates a security unit that adopts a modified Diffie-Hellman key agreement protocol with an optimized location-aided routing protocol (LAR) that adopts a tilted rectangular shaped (TRS) request zone. The proposed Secure Tilted-Rectangular-Shaped Request Zone Location-Aided Routing Protocol (STRS-RZLAR) provides a secure communication between vehicles in VANETs and thus protects the overall communication scheme from man-in-the-middle attack (MITMA). By conducting extensive simulation results using two main network parameters: vehicular node density and the number of the malicious nodes, the proposed routing protocol outperforms the insecure TRS-LAR and the standard LAR in terms of secure data delivery.

Dissemination of Message Based on Calculation of Residual Energy of Vehicles in Vehicular Ad-Hoc Network

Dissemination of Message Based on Calculation of Residual Energy of Vehicles in Vehicular Ad-Hoc Network

A fuzzy-based approach for cluster management in VANETs: Performance evaluation for two fuzzy-based systems

In recent years, due to the growing interest in smart cities and Internet of Things (IoT) applications, several innovation types of networks, such as Vehicular Ad-hoc Networks (VANETs) and Opportunistic networks have been deeply investigated. Moreover, inter-vehicle communication has attracted attention because it can be applicable not only to alternative networks but also to various communication systems. In this paper, we present a Fuzzy-Based Cluster-Management System (FBCMS) for VANETs. We present and compare two fuzzy-based system models: FBCMS1 and FBCMS2 for clustering of vehicles in VANETs. We evaluate both systems by simulations. The simulation results show that by selecting vehicles with high GS, RA, SC and DC values, the vehicles have high connectivity with other vehicles and are more secure, so they will be selected as the cluster head of the cluster. By comparing FBCMS1 and FBCMS2, we found that the FBCMS2 can manage better the vehicles in the cluster than FBCMS1.

Modeling of Situation Response Time in Vehicular Ad-Hoc Network

<p class="Text"><strong>—<em> </em></strong>In Vehicular Ad-Hoc Networks (VANETs), wireless-equipped vehicles form a network spontaneously while traveling along the road. The direct wireless transmission from vehicle to vehicle makes it possible for them to communicate even where there is no telecommunication infrastructure; this emerging new technology provide ubiquitous connectivity to vehicular nodes while on the move, The main idea is to provide ubiquitous connectivity to vehicular nodes while on the move, and to create efficient vehicle-to-vehicle communications that enable the Intelligent Transportation Systems (ITS). This is achieved by allowing nodes within certain ranges to connect with each other in order to exchange information. Since accident happens in split seconds, to avoid communication inefficiency, there is need for this information to get to the intended vehicle on time. To solve this problem, this work models each vehicle in a chain of others and how it responds to the traffic around it using Microscopic (also known as car-following) method for modeling traffic flow; driver- to-driver and driver-to-road interactions within a traffic stream and the interaction between a driver and another driver on road were considered. The essence of this modeling is to determine the minimum response time required for a vehicle in VANET to respond and communicate situations on the road. A simulated scenario was carried out for two vehicles, a leading vehicle and following vehicle. The result shows that with an average of 32 meters apart with average difference in velocity of 1.23m/s, a minimum of 0.9secs is required for efficient situation response communication to ensue between them.</p>

Design of the Emergency Messages Integration for Vehicle to Everything (V2X) Communications of Connected Cars

So far, international standardization of message specifications between V2X(Vehicle to Everything) safety communications are BSM(Basic Safety Message) by US-led SAE and CAM(Cooperative Awareness Message)/DENM(Decentralized Environmental Notification Message) by EU-led ETSI. However, in the emergency situation such as car accidents or road traffic jam, there may be insufficient time to process all messages. In this paper, we propose an integration of emergency messages for V2X communications of connected vehicles in VANETs(Vehicular Ad hoc Networks), To do this, we are using the Mobile Virtual Fence(MVF), which has been preliminarily researched and developed. So we extract and integrate only emergency messages as the fields of similar messages between BSM and CAM/DENM, and apply it to the MVF. If it will be the connected MVFs like connected cars in the future and any traffic accident or traffic jam occurs, the proposed messages will be communicated to each other to avoid the accident. Therefore the proposed integration of emergency messages between BSM and CAM/DENM specification will be contribute to improve the safety and reliability as the unified and integrated emergency messages.

Authenticating Communication of Autonomous Vehicles With Artificial Intelligence

Autonomous vehicles in Vehicular Ad-hoc Networks(VANET)-the spontaneous creation of a wireless network for data exchange to the domain of vehicles may pave the way for future systems where computers take over the art of driving. It aids in transferring secure messages for proper communication between the vehicles. Unauthorized access like injecting spoofed messages to VANET can arise as an extreme threat. So remedies should be taken at early degree of architectonic process. Denial-of Service(DoS) attack takes place while validating each message in VANET, so an equity between message authentication and DoS prevention should be maintained. This paper discusses on diverse security demanding situations and distinct techniques to secure message transfer between the vehicles that are connected wirelessly.

Reinforcement Learning Based Mobility Adaptive Routing for Vehicular Ad-Hoc Networks

Vehicular ad-hoc networks (VANETs) is drawing more and more attentions in intelligent transportation system to reduce road accidents and assist safe driving. However, due to the high mobility and uneven distribution of vehicles in VANETs, multi-hops communication between the vehicles is still particularly challenging. Considering the distinctive characteristics of VANETs, in this paper, an adaptive routing protocol based on reinforcement learning (ARPRL) is proposed. Through distributed Q-Learning algorithm, ARPRL constantly learns and obtains the fresh network link status proactively with the periodic HELLO packets in the form of Q table update. Therefore, ARPRL's dynamic adaptability to network changes is improved. Novel Q value update functions which take into account the vehicle mobility related information are designed to reinforce the Q values of wireless links by exchange of HELLO packets between neighbor vehicles. In order to avoid the routing loops caused in Q learning process, the HELLO packet structure is redesigned. In addition, reactive routing probe strategy is applied in the process of learning to speed up the convergence of Q learning. Finally, the feedback from the MAC layer is used to further improve the adaptation of Q learning to the VANETs environment. Through simulation experiment result, we show that ARPRL performs better than existing protocols in the form of average packet delivery ratio, end-to-end delay and number hops of route path while network overhead remains within acceptable ranges.

Influence of intersections on the performance of position-based routing protocols for VANETs

PurposeThe vehicular ad hoc network (VANET) is an emerging area for smart cities as observed in last few decades. However, some hurdles for VANET exist that need to be resolved before its full implementation in smart cities. Routing is one of the main factors for having effective communication between smart vehicles that urgently needs to be addressed. One factor that affects communication between the vehicles is the intersection points that obstruct the communication. The paper aims to discuss these issues.Design/methodology/approachThe conventional routing schemes fail to address the intersection problems that occur during the two points of communication. Therefore, this paper analyses the performance of existing position-based routing protocol for inter-vehicle ad hoc networks, considering the impact of a number of intersections. This simulation evaluates different position-based routing protocols such as Intersection-based Distance and Traffic-Aware Routing (IDTAR), Greedy Traffic-Aware Routing, Anchor-based Street and Traffic-Aware Routing and Geographic Source Routing, based on road topology and the number of intersections.FindingsAs a result, the protocol IDTAR has a lower end-to-end delay and high packet delivery ratio in terms of the number of intersections as a case study of smart cities. This concludes that IDTAR can be adaptive to smart cities communication, although some questions need to be considered in terms of its security, compatibility, reliability and robustness.Practical implicationsThe role of VANET has been highlighted in smart cities due to its implications in day-to-day life. The vehicles in VANET are equipped with wireless communication nodes to provide network connectivity. Such types of network operate without the legacy infrastructure, as well as legacy client/servers.Originality/valueAdditionally, the study contributes to smart cities by measuring the performance of position-based routing protocols for VANETs.

Distributed Privacy-Preserving Collaborative Intrusion Detection Systems for VANETs

Vehicular ad hoc network (VANET) is an enabling technology in modern transportation systems for providing safety and valuable information, and yet vulnerable to a number of attacks from passive eavesdropping to active interfering. Intrusion detection systems (IDSs) are important devices that can mitigate the threats by detecting malicious behaviors. Furthermore, the collaborations among vehicles in VANETs can improve the detection accuracy by communicating their experiences between nodes. To this end, distributed machine learning is a suitable framework for the design of scalable and implementable collaborative detection algorithms over VANETs. One fundamental barrier to collaborative learning is the privacy concern as nodes exchange data among them. A malicious node can obtain sensitive information of other nodes by inferring from the observed data. In this paper, we propose a privacy-preserving machine-learning-based collaborative IDS (PML-CIDS) for VANETs. The proposed algorithm employs the alternating direction method of multipliers to a class of empirical risk minimization problems and trains a classifier to detect the intrusions in the VANETs. We use the differential privacy to capture the privacy notation of the PML-CIDS and propose a method of dual-variable perturbation to provide dynamic differential privacy. We analyze theoretical performance and characterize the fundamental tradeoff between the security and privacy of the PML-CIDS. We also conduct numerical experiments using the network security laboratory-knowledge discovery and data mining (NSL-KDD) dataset to corroborate the results on the detection accuracy, security-privacy tradeoffs, and design.

Efficient high-resolution video delivery over VANETs

The adoption of video-equipped vehicles in Vehicular ad-hoc networks (VANETs) is experiencing a rapid growth. It is also anticipated a substantial increase in the video content distribution with the arrival of self-driving cars as both passengers and vehicles will be able to produce and consume this type of media. This unveils a set of challenges, especially in VANETs where the network resources tend to be scarce and the connections suffer from time-varying error conditions. Taking everything into consideration, a Quality of Experience (QoE)-driven mechanism is desirable to enhance the video delivery over error-prone networks. To this end, the combined use of forward error correction and unequal error protection has proven its efficiency in delivering high-quality videos with low network overhead. The proposed intelligent quality-driven and network-aware mechanism (AntArmour) uses an ant colony optimization scheme to dynamically allocate a precise amount of redundancy. This allows AntArmour to safeguard, in real-time, the live transmission of high-resolution video streams. This operation is performed according to specific high efficiency video coding details and the actual network conditions such as the signal-to-noise ratio, the network density, the vehicle’s position, and the current packet loss rate (PLR) as well as the prediction of future PLR. The experiments were performed using real map’s clippings and actual video footage. The assessment was performed with the aid of two well-known objective QoE metrics, as well as the measure of the network overhead. The results showed that the proposed mechanism outperformed all its competitors in both video quality improvement and network overhead decrement.

A COMPARATIVE STUDY OF POSITION-BASED ROUTING METHODS IN VANET

Vehicular ad-hoc network (VANET) means a network of vehicles on road. In VANET vehicles transfer the security and comfort information to each other even if they are way apart and not in the direct radio range. It is an emerging and one of the promising technologies to build an intelligent transportation system (ITS). VANETs have many challenging issues such as short transmission range, recurrent link shattering and speedily change in the network topology due to massive movability. One of the major concerns is to design a stable, scalable and robust routing protocol. In this work, a brief literature review along with the analyses based on Packet Delivery Ratio (PDR) and End to End delay (E2E delay) using MATLAB is presented. The protocols are also compared with the results available in the literature.

Clustering-based reliable low-latency routing scheme using ACO method for vehicular networks

In vehicular ad hoc networks (VANETs), communication links break frequently due to the high velocity vehicles. In this paper, based on the existing ad hoc on-demand multipath distance vector (AOMDV) routing scheme, a new clustering-based reliable low-latency multipath routing (CRLLR) scheme is proposed by employing Ant Colony Optimization (ACO) technique. Herein the link reliability is used as criteria for Cluster Head (CH) selection. In a given cluster, a vehicle will be selected as CH if it has maximum link reliability. Moreover, the ACO technique is employed to efficiently compute the optimal routes among the communicating vehicles for VANETs in terms of four QoS metrics, reliability, end-to-end latency, throughput and energy consumption. Simulation results demonstrate that the proposed scheme outperforms the AQRV and T-AOMDV in term of overall latency and reliability at the expenses of slightly higher energy consumption.

Asking neighbors a favor: Cooperative video retrieval using cellular networks in VANETs

In the future networks, driven by the constant evolution of today's applications, video streaming over vehicular ad-hoc networks (VANETs) will play an increasingly significant role in our daily life. Many users already consider the video streaming service, among other services, as a necessity. However, due to the limited bandwidth of a connection through a cellular network, a vehicle may not be able to obtain a good quality of service (QoS). In order to receive a good quality video, a vehicle in VANET may ask its neighbors a favor in helping to download the video. In this paper, we therefore propose a COoperative video REtrieval scheme (CoRe) that allows the vehicles to download a good quality video from the Internet in collaboration with the neighbors. The neighbors are selected by exploiting their multiple on-road characteristics such as euclidean distance, connection time, relative velocity, and the available cellular bandwidth. These neighboring vehicles download the video data through their cellular link and forward it to the requesting vehicle through the Dedicated Short-Range Communication (DSRC) protocol. Simulation results show that our proposed CoRe scheme outperforms the existing solution such as the K-hop scheme in terms of higher video quality and better video-flow handling.

A Novel Stable Clustering Approach based on Gaussian Distribution and Relative Velocity in VANETs

Vehicles in Vehicular Ad-hoc Networks (VANETs) are characterized by their high dynamic mobility (velocity). Changing in VANET topology is happened frequently which caused continuous network communication failures. Clustering is one of the solutions applied to reduce the VANET topology changes. Stable clusters are required and Indispensable to control, improve and analyze VANET. In this paper, we introduce a new analytical VANET's clustering approach. This approach aims to enhance the network stability. The new proposed grouping process in this study depends on the vehicles velocities mean and standard deviation. The principle of the normal (Gaussian) distribution is utilized and emerged with the relative velocity to propose two clustering levels. The staying duration of vehicles in a cluster is also calculated and used as an indication. The first level represents a very high stabile cluster. To form this cluster, only the vehicles having velocities within the range of mean ± standard deviation, collected in one cluster (i.e. only 68% of the vehicles allowed to compose this cluster). The cluster head is selected from the vehicles having velocities close to the average cluster velocity. The second level is to create a stable cluster by grouping about 95% of the vehicles. Only the vehicles having velocities within the range of mean ± 2 standard deviation are collected in one cluster. This type of clustering is less stable than the first one. The analytical analysis shows that the stability and the staying duration of vehicles in the first clustering approach are better than their values in the second clustering approach.

Adaptive Multimedia Data Forwarding for Privacy Preservation in Vehicular Ad-Hoc Networks

Vehicular ad-hoc networks (VANETs) have drawn much attention of researchers. The vehicles in VANETs frequently join and leave the networks, and therefore restructure the network dynamically and automatically. Forwarded messages in vehicular ad-hoc networks are primarily multimedia data, including structured data, plain text, sound, and video, which require access control with efficient privacy preservation. Ciphertext-policy attribute-based encryption (CP-ABE) is adopted to meet the requirements. However, solutions based on traditional CP-ABE suffer from challenges of the limited computational resources on-board units equipped in the vehicles, especially for the complex policies of encryption and decryption. In this paper, we propose a CP-ABE delegation scheme, which allows road side units (RSUs) to perform most of the computation, for the purpose of improving the decryption efficiency of the vehicles. By using decision tree to jointly optimize multiple factors, such as the distance from RSU, the communication and computational cost, the CP-ABE delegation scheme is adaptively activated based on the estimation of various vehicles decryption overhead. Experimental results thoroughly demonstrate that our scheme is effective and efficient for multimedia data forwarding in vehicular ad-hoc networks with privacy preservation.

SEIR epidemic spreading model to suppress broadcast storm in vehicular ad hoc networks

Vehicular Ad Hoc Networks (VANETs) is a form of Intelligent Transportation System (ITS) to improve road safety and transport efficiency. During an emergency like accidents, traffic jams, etc., the vehicles in VANET are alerted with an Emergency Safety Message (ESM) through broadcasting. However, blind broadcasting of ESMs across VANETs leads to Broadcast Storm Problem (BSP) which would affect the QoS requirements of VANET. Hence, it is vital to suppress BSP to broadcast ESMs effectively. This paper proposes a Broadcast Storm Suppression Algorithm (BSSA) based on the concept of epidemic spreading, i.e. SEIR (Susceptible, Exposed, Infected and Removed) model. The broadcast storm is suppressed by finding the 'Farthest Infected Vehicle' (FIV) travelling in the direction of the ESM and recovering the other infected vehicles within the region to prevent from rebroadcasting the ESM. The SEIR model is simulated in NS-2.34 and found to outperform other popular broadcasting techniques.

VDNet: an infrastructure-less UAV-assisted sparse VANET system with vehicle location prediction

Vehicular Ad Hoc Network (VANET) has been a hot topic in the past few years. Compared with vehicular networks where vehicles are densely distributed, sparse VANET have more realistic significance. The first challenge of a sparse VANET system is that the network suffers from frequent disconnections. The second challenge is to adapt the transmission route to the dynamic mobility pattern of the vehicles. Also, some infrastructural requirements are hard to meet when deploying a VANET widely. Facing these challenges, we devise an infrastructure-less unmanned aerial vehicle (UAV) assisted VANET system called Vehicle-Drone hybrid vehicular ad hoc Network (VDNet), which utilizes UAVs, particularly quadrotor drones, to boost vehicle-to-vehicle data message transmission under instructions conducted by our distributed vehicle location prediction algorithm. VDNet takes the geographic information into consideration. Vehicles in VDNet observe the location information of other vehicles to construct a transmission route and predict the location of a destination vehicle. Some vehicles in VDNet equips an on-board UAV, which can deliver data message directly to destination, relay messages in a multi-hop route, and collect location information while flying above the traffic. The performance evaluation shows that VDNet achieves high efficiency and low end-to-end delay with controlled communication overhead. Copyright © 2016 John Wiley & Sons, Ltd.

A Honeypot Scheme to Detect Selfish Vehicles in Vehicular Ad-hoc Network

A Honeypot Scheme to Detect Selfish Vehicles in Vehicular Ad-hoc Network