Vehicles In Vehicular Ad-hoc Networks Research Articles

Shared group session key-based conditional privacy-preserving authentication protocol for VANETs

Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Due to the open nature of VANETs, conditional privacy-preserving authentication protocol should be offered against potential attacks. Efficient and secure authentication among vehicles in VANETs are important requirements, but there are various limitations in the existing conditional privacy-preserving authentication protocols for securing VANETs. To cope with the inherent issues, we propose a conditional privacy-preserving authentication protocol based share group session key (SGSK) by integrating the self-healing key distribution technique, blockchain, and MTI/C0 protocol. In our protocol, we use SGSK instead of the time-consuming Certificate Revocation List (CRL) checking, and we revoke malicious vehicles by updating SGSK. It is shared among unrevoked vehicles within a domain and across-domain. As a result, when a malicious revoked vehicle enters a new domain, it is difficult for it to access the system and send false messages. Furthermore, our protocol can not only achieve computation efficiency by reducing the number of computing operations of bilinear pairing but also resist various attacks while keeping conditional privacy protection. We implement our protocol in the Hyperledger Fabric platform. The experimental results show that our protocol is available to revoke 180 malicious vehicles in across-domain scenarios within one second, and it can meet the requirement of verifying 600 messages per second easily. Moreover, our comprehensive performance evaluations demonstrates that our protocol outperforms other approaches in terms of vehicle revocation checking cost, computation overhead, and communication overhead. In addition, to show the feasibility and validity of our protocol, we use SUMO and NS2 to simulate the actual VANET scenario and validate the efficiency and performance of our protocol. Simulation results prove the practicability of our protocol for VANETs.

Traffic coordination by reducing jamming attackers in VANET using probabilistic Manhattan Grid Topology for automobile applications

In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and dynamic characteristics of road vehicles in VANET (Vehicular Adhoc Network). These vehicles take up a convenient side lane position on the road, disregarding lane discipline. They utilize the opposing lane to overtake slower-moving vehicles, even when there are oncoming vehicles approaching. The primary objective of this study is to minimize injuries resulting from vehicle interactions in mixed traffic conditions on undivided roads. This is achieved through the implementation of the Modified Manhattan grid topology, which primarily serves to guide drivers in the correct path when navigating undivided roads. Furthermore, the Fuzzy C-Means algorithm (FCM) is applied to detect potential jamming attackers, while the Modified Fisheye State Routing (MFSR) Algorithm is employed to minimize the amount of information exchanged among vehicles. Subsequently, the Particle Swarm Optimization (PSO) algorithm is developed to enhance the accuracy of determining the coordinates of jamming attackers within individual clusters. The effectiveness of the outcomes is affirmed through the utilization of the Fuzzy C-Means algorithm, showcasing a notable 30% reduction in the number of attackers, along with the attainment of a 70% accuracy rate in this research endeavor.

A Secured Privacy-preserving Multifactor Approach for Autonomous Vehicles Using Blockchain Technology

Background and Aims: Smart vehicles are connected to the Internet of Things (IoT), bringing with them the potential to transform human existence in so-called "smart cities." Intelligent vehicle architecture revolves around the Vehicular-AdhocNetwork(VANET). The goal of a VANET is to make driving more pleasant. VANETs' message-sharing capabilities contribute to improved traffic management, reduced congestion, and safer driving. However, VANETs' usefulness could be diminished by the spread of fraudulent or erroneous messages Objectives and Methodology: For better road safety and less congestion, it guarantees secure and accurate communication between vehicles and between vehicles and infrastructure. The security and privacy of a VANET, however, can be compromised by threats, including denial-of-service (DoS), replay, and Sybil attacks. These problems can cause a rogue node to send out faulty data throughout the system. We introduce a biometrics-blockchain(BBC) approach to ensure the safety of information exchanged between vehicles in a VANET and to preserve archival data in a triedand-true environment. To protect the anonymity of users, the suggested framework makes use of biometric data to verify the identity of the sender. Results and Conclusion: As a result, the proposed BBC scheme creates a safe and reliable environment for vehicles in VANET, with the added benefit of identity tracing capabilities. To prove the effectiveness of the proposed framework, simulations were run in the urban mobility models OMNeT++, veins, and SUMO. Packet-delivery-rate(PDR), packet-loss-rate(PLR), and computing cost (CC) were used to assess the framework's efficiency. The outcomes highlighted the superiority of our innovative model over conventional methods, such as PDR slightly increased to 8-10%, PLR decreased to 20-25%, and CC also reduced to 15-20% compared to state-of-art models.

Smart dynamic evacuation planning and online management using vehicular communication system

AbstractDuring disasters, swiftly and efficiently evacuating populations in hazardous situations is crucial to minimize losses. This study proposes a novel framework to address dynamic population evacuation (DPE) problems, which includes planning and online evacuation management phases facilitated by vehicular communication. In the planning phase, a shelter allocation problem (SAP) is solved dynamically for destination choice and a dynamic traffic assignment (DTA) for path choice toward the chosen destinations to obtain an initial evacuation plan. The initial plan is then enhanced by employing a vehicular ad hoc network (VANET) within the vehicular edge computing (VEC) architecture. This enhancement enables communication among evacuees, allowing them to revise their vehicle's route choice and planned destination. These revisions take into account the changing risk and traffic conditions. The proposed online DPE framework is applied to the real evacuation scenario of Mill Valley City, CA. The proposed model is evaluated with different VANET architectures, including vehicular cloud computing (VCC) and VEC. The results show that the VEC framework outperforms other configurations and improves the evacuation process compared to the scenario with an initial plan by more than 30% in network clearance time. Additionally, a performance analysis is carried out for evacuation scenarios with different penetration rates of connected vehicles in VANET.

Analysing and Evaluation of Routing Protocols in VANET

Among the application of Ad Hoc network we have Vehicular Ad Hoc Network (VANET), which is an important data research domain today. VANET can improve the traffic road efficiency in order to overcome many problems and offer more services to customer. For instance, VANET can lead to reduce traffic jam or improve road safety by minimizing vehicles’ accident. Like any wireless network without infrastructure, the routing task is the most difficult task especially when nodes are mobile. The mobility of nodes, which are vehicles in VANET, causes a rapid change of network topology and leads to bread connection between source and destination nodes. In this paper, we will analyze some routing protocols in order to evaluate their performances. In this study we have proposed some scenarios of VANET routing protocols. The simulation has been done on NS3 simulator and SUMO. Simulation proves that routing protocols have a huge impact on VANET network performances.

Maximizing VANET performance in cluster head selection using Intelligent Fuzzy Bald Eagle optimization

Vehicular Ad-hoc Network (VANET) is a type of wireless network that allows communication among vehicles and roadside units to develop advanced intelligent transportation systems. The success of VANETs depends on the stability of wireless communication, among vehicles, which is challenging to achieve due to high vehicle speed, rapidly changing topology, and unstable communication links. Moreover, the instability of the network caused by the mobile nature of vehicles in VANET reduces the performance of the network. Clustering in VANETs is a crucial technique that organizes the network and forms the basis of the routing protocol. Clustering algorithms are designed for VANETs to work efficiently and require several phases that must be integrated into the process before a clustering decision can be made. Therefore, this paper presents a novel Intelligent Fuzzy Bald Eagle (IFBE) optimization to enhance the performance of VANETs by optimizing the cluster head selection (CHS) process. The experimental results prove that the IFBE approach outperforms other existing mechanisms in terms of energy consumption, end-to-end delay, and packet delivery ratio. The proposed mechanism utilizes an intelligent fuzzy system to optimize the CHS process. The fuzzy system uses a set of rules and membership functions to evaluate the candidate nodes' suitability for being cluster heads. The Bald Eagle Search (BES) optimization meta-heuristic algorithm is used to find the optimal values for the fuzzy system's membership functions. The proposed mechanism was evaluated using the MATLAB simulator. Finally, the experimental result proved that the IFBE approach achieved minimum delay and energy consumption of 13.58 ms, and 15.5 J, and higher clustering efficiency and packet delivery rate of 94.15% and 97.65% respectively, which show that it performs better than other existing approaches.

BCGS: Blockchain-assisted privacy-preserving cross-domain authentication for VANETs

Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Secure authentication among vehicles in VANETs is an important requirement, but there are various limitations in many existing authentication protocols, for example, in terms of privacy protection, malicious entity tracking, and cross-domain authentication. In response, we first reconstruct a secure and effective short group signature scheme for realizing anonymous authentication and traceable identity within a domain. We then propose a secure blockchain-based privacy-preserving cross-domain authentication protocol for VANETs, namely BCGS, by integrating both blockchain and group signature. In BCGS, blockchain is leveraged to facilitate trusted information sharing and consequently supports cross-domain authentication among vehicles, and the group signature scheme is used to provide conditional privacy protection. Our comprehensive security and performance evaluations demonstrate that BCGS outperforms other similar approaches in terms of security, computation, and storage costs.

An Enhanced Elliptic Curve Cryptography Scheme for Secure Data Transmission to Evade Entailment of Fake Vehicles in VANET

Vehicular Ad-hoc Networks (VANET) help to increase traffic safety, manageability, and efficiency. VANET facilitates effective communication in the vehicular nodes. Here, the network attack is one of the significant consequences of VANET, which may affect network security. The high mobility of the nodes may become difficult to transfer the data through the vehicle. In this article, a novel method of Elliptic Curve Cryptography (ECC) is proposed. The key is generated by Key Generation Unit (KGU), and also the key is fed into the Road Side Unit (RSU). The keys in Enhanced ECC (EECC) are optimized using the Adaptive Horse herd Optimization Algorithm (AHOA). During decryption, the key of source node K1 is to be taken from RSU, where the key is processed by the Digital Signature method to obtain EECC key K2. With the generated K2, the destination node can retrieve the data by the proposed model. Thus, the simulation analysis of the proposed AHOA-EECC method has attained less time complexity which was compared against the high value of 5%, 12.4%, 50%, and 13.7% for GWO-EECC, PSO-EECC, AOA-EECC, and HOA-EECC. Finally, the outcome of the proposed system enhances the security level and performs better data transmission.

TGRV: A trust-based geographic routing protocol for VANETs

Vehicular ad-hoc networks (VANETs) consist of highly mobile and self-organized nodes that wirelessly communicate with one another and transmit a variety of information, some of which may be critical information. Due to the rapidly changing topology of VANETs and the high-speed mobility of the participating vehicles, the routing of packets in these networks is a challenging task. Moreover, the presence of malicious vehicles in VANETs makes the correct routing of packets more challenging. In this paper, we propose a trust-based geographic routing protocol for VANETs (TGRV) that limits the participation of malicious vehicles in routing. In TGRV, to select the next-hop, a sender not only considers the distance, speed, and direction of its neighbors but also evaluates their direct trust and recommendation trust. For this purpose, TGRV uses a monitoring system that allows each vehicle to monitor the correct packet forwarding rate of its next-hop. In this way, the vehicle updates its direct trust to the next-hop and retransmits the packets if the packets are lost. The vehicle also sends its observations of the next-hop to its neighbors with a push-based notification, and based on that, the neighbors can update their recommendation trust about the next-hop. The monitoring system applies distance prediction in a modified promiscuous mode to better estimate the correct packet forwarding rate of the next-hop. To enhance the accuracy of trust management, the trust values of interactions are reduced over time by using a decay factor. TGRV uses the number and trust of two-hop neighbors, which helps in selecting the next-hop that is in a more trusted zone. Our extensive simulations on OMNeT++ show that when the percentage of malicious vehicles in an urban scenario increases from 0 to 25%, the packet delivery ratio of TGRV decreases from 95.1 to 88.7%, which performs very well compared to the GPSR and PGRP protocols. Also, the end-to-end delay of TGRV increases from 3.79 to 7.07 s and the average hop count of TGRV increases from 6.7 to 9.9, which are significant increases compared to the other two protocols. These increases are acceptable because, in the other two protocols, there is no monitoring and retransmission, and the probability of packet delivery failure on longer routes is higher. The cost and security analysis also show that the memory, communication, and computational overheads of TGRV are acceptable, and TGRV is resistant and resilient to some important trust-based attacks.

Secure authentication and privacy-preserving to improve video streaming vehicle ad-hoc network

In vehicular ad hoc networks (VANET), the privacy of vehicle data symbolizes a big challenge towards malicious attacks. On the other side, vehicles in VANET can play a staple role in monitoring the environment by sensing the surrounding environment, compute the sensing information, and transfer the results if needed to the authorized party. Most of the modern VANETs systems encrypt the information to prevent hacking it but mostly neglect the decryption that occurred when data need to re-processed. In this paper, we try to cover this weak point by using fully homomorphic encryption (FHE) because of its specifications. The proposed work focus on twofold: first, create secure authentication and permission management system. While the second is to preserve the privacy of vehicle data that transferred among VANET infrastructure. This scheme also deals with metric security features, such as data privacy, data integrity, and key management. In the experimental results, there is good advance in the fields of interest comparing with the related works.

HDRS: A Hybrid Reputation System With Dynamic Update Interval for Detecting Malicious Vehicles in VANETs

The reputation-based scheme is a promising solution to prevent malicious behaviors in Vehicular Ad-hoc Networks (VANETs). However, traditional centralized reputation schemes are not suited for distributed networks, while decentralized reputation schemes are vulnerable to malicious vehicles spreading false messages. Most of these schemes assume that the behavior of vehicles can be accurately measured as reputation from the communication, ignoring that malicious vehicles may behave intelligently to avoid being detected. In this paper, we propose a hybrid reputation system (HDRS) which allows vehicles and roadside units (RSU) to complete reputation evaluations separately and provide references to each other. HDRS utilizes a reliability evaluation module to filter out unreliable calculation results and reference records. Furthermore, HDRS includes a dynamic adjustment mechanism for the reputation update interval, employing Analytic Hierarchy Process (AHP) and reliability evaluation results to resist intelligent attacks. Simulation results illustrate that HDRS can maintain a high detection rate and low false-positive rate for detecting malicious vehicles in different environments. Compared with existing schemes, HDRS increases the detection rates of collusion and intelligent attacks by 30% and 16%, respectively.

A Fine-Grained Access Control and Security Approach for Intelligent Vehicular Transport in 6G Communication System

The area of intelligent transport systems (ITS) is attracting growing attention because of the integration of the smart IoT with vehicles that improve user safety and overall travel experience. Vehicular ad hoc network (VANET) is the part of ITS; that deals with the routing protocols and security of smart vehicles. However, due to the rapid increase in the number of smart vehicles, the existing network technology’s resources unable to handle the traffic load. It expects that the 6G communication system has the ability to fulfill the requirements of VANETs. Only a few studies explore this area, but they also overlooked the security aspect of VANETs in 6G communications networks. In this paper, we present an approach to address authentication and security issues for vehicles in VANET. By authenticating cars in the VANET and identifying various cyber assaults such as DDoS, our method significantly contributes to the intelligent transport communication network. Our approach uses the concepts of identity-based encryption to provide access control to the vehicles and deep learning-based techniques for filtering malicious packets. Our identity-based encryption technique is IND-sID-CCA secure, and a state-of-the-art deep learning algorithm detects malicious packets with an accuracy of 99.72%. These results emphasize the validity of our proposed approach for VANETs in 6G communication systems.

Energy-SLA-aware genetic algorithm for edge–cloud integrated computation offloading in vehicular networks

Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are characterized by low computational and storage capabilities. In such scenarios, to meet applications’ performance requirements, requests from vehicles are offloaded to edge and cloud servers. The high energy consumption of these servers increases operating costs and threatens the environment. Energy-aware offloading strategies have been introduced to tackle this problem. Existing works on computation offloading focus on optimizing the energy consumption of either the IoT devices/mobile/vehicles and/or the edge servers. This paper proposes a novel offloading algorithm that optimizes the energy of edge–cloud integrated computing platforms based on Evolutionary Genetic Algorithm (EGA) while maintaining applications’ Service Level Agreement (SLA). The proposed algorithm employs an adaptive penalty function to incorporate the optimization constraints within EGA. Comparative analysis and numerical experiments are carried out between the proposed algorithm, random and genetic algorithm-based offloading, and no offloading baseline approaches. On average, the results show that the proposed algorithm saves 2.97 times and 1.37 times more energy than the random and no offloading algorithms respectively. Our algorithm has 0.3% of violations versus 52.8% and 62.8% by the random and no offloading approaches respectively. While the energy-non-SLA-aware genetic algorithm saves, on average, 1.22 times more energy than our approach, however, it violates SLAs by 159 times more than our proposed approach.

Adaptive Computation Offloading with Task Scheduling Minimizing Reallocation in VANETs

Computation Offloading (CO) can accelerate application running through parallel processing. This paper proposes a method called vehicular adaptive offloading (VAO) in which vehicles in vehicular ad-hoc networks (VANETs) offload computationally intensive tasks to nearby vehicles by taking into account the dynamic topology change of VANETs. In CO, task scheduling has a huge impact on overall performance. After representing the precedence relationship between tasks with a directed acyclic graph (DAG), VAO in the CO requesting vehicle assigns tasks to neighbors so that it can minimize the probability of task reallocations caused by connection disruption between vehicles in VANETs. The simulation results showed that the proposed method decreases the number of reallocations by 45.4%, as compared with the well-known task scheduling algorithms HEFT and Max-Min. Accordingly, the schedule length of entire tasks belonging to one application is shortened by 14.4% on average.

Broadcast Encryption Scheme for V2I Communication in VANETs

Information dissemination in vehicular ad hoc networks (VANETs) is inseparable from the interaction between vehicles and infrastructure. The trust authority (TA) often plays a pivotal role in VANETs and requires interaction with multiple vehicles. However, when the TA sends the same message to multiple vehicles, there are many redundancies, as it needs to negotiate with each vehicle and send them different ciphertexts. This greatly reduces the work efficiency of the TA. To the best of our knowledge, there is no research on the problem of redundancy that occurs when the same message is sent to multiple vehicles in VANETs. The proposed scheme adopts identity-based broadcast encryption (IBBE) technology, which is a secure data-sharing scheme suitable for the vehicle-to-infrastructure communication mode, in VANETs for the first time. Thus, with only one encryption, the TA can generate a fixed-length ciphertext for a group of vehicles. When there are new vehicles that subsequently request a service, the TA can assign encryption tasks to the proxy server. In terms of security, our scheme meets the particular requirements of VANETs. The encryption overhead of the sender and the length of the ciphertext were comparatively analyzed. The results demonstrated that the performance of the scheme improved significantly. Thus, our scheme can prevent redundancies and effectively improve the work efficiency of TA.

An Improved Robust Misbehavior Detection Scheme for Vehicular Ad Hoc Network

Vehicular Ad Hoc Networks (VANETs) have emerged to improve road safety and traffic efficiency and provide passengers comfort. Most VANET applications rely on the cooperation among vehicles sharing their sensed information. However, misbehaving vehicles which send false information can disrupt the VANETs potential. Although many solutions have been proposed to defend against misbehaving vehicles in VANET, most of these solutions relays on honest majority assumptions and are thus vulnerable to collusion attacks. Colluding vehicles send fake information, and because detection depends on cooperation, such information misleads benign vehicles to make an accurate decision. This study proposes an improved Robust Misbehavior Detection Scheme (iRMDS) by replacing the statistics-based detection threshold, which assumes an honest majority, with a machine learning-based classifier. A Neuro-Kalman-Based Robust Misbehavior detection scheme is proposed in three phases. In the first phase, attackers-Independent features are extracted from signal properties such as the receive signal strength and signal direction and have been integrated with context information features. In the second phase, the Kalman filter algorithm has been designed to extract consistent patterns of context information for each vehicle. That is, the innovation errors of the Kalman filter have been utilized as the input features to train the misbehavior detection model. In the third phase, the artificial neural network algorithm is integrated with the outputs of the Kalman Filter algorithm to recognize the malicious pattern. Results show that the overall performance of the proposed iRMDS solution achieves a 3.44% improvement compared to the related work. Such enhancement is promising in realizing reliable VANET applications to improve road safety, traffic efficiency, and passenger comfort.

A Cross-layer Method for Identifying and Isolating the Blackhole Nodes in Vehicular Ad-hoc Networks

ABSTRACT Vehicular Ad-hoc Network (VANET) is a set of intelligent vehicles that communicate without a fixed infrastructure. The communication between each source/destination pair is done by using routing protocols. On-demand multipath distance vector (AOMDV) is one of the most known ad-hoc multipath routing protocols used in VANETs. The decentralized nature of VANET makes this type of network vulnerable to various attacks, such as blackhole attack. In such attack, the malicious vehicle aims to make the communication unavailable. To achieve this goal, the malicious vehicle persuades the source to send its data packets through it because it has the fresher route toward the destination. This is done by forging routing information. After receiving the data packets, the malicious vehicle deletes them instead of forwarding them to their intended destinations. This paper introduces a new Cross-Layer method (CRAOMDV) where information is shared between MAC and network layers to detect and ignore the malicious vehicles in VANETs. Our experiments used the simulator NS2 and SUMO for the generation and simulation of real mobility scenarios. The evaluation results demonstrate the efficiency of CRAOMDV compared to AOMDV under blackhole attack in terms of improving the packet delivery and reducing the average end-to-delay and the routing overhead.

DeepADV: A Deep Neural Network Framework for Anomaly Detection in VANETs

We are seeing a growth in the number of connected vehicles in Vehicular Ad-hoc Networks (VANETs) to achieve the goal of Intelligent Transportation System (ITS). This is leading to a connected vehicular network scenario with vehicles continuously broadcasting data to other vehicles on the road and the roadside network infrastructure. The presence of a large number of communicating vehicles greatly increases the number and types of possible anomalies in the network. Existing works provide solutions addressing specific anomalies in the network only. However, since there can be a multitude of anomalies possible in the network, there is a need for better anomaly detection frameworks that can address this unprecedented scenario. In this paper, we propose an anomaly detection framework for VANETs based on deep neural networks (DNNs) using a sequence reconstruction and thresholding algorithm. In this framework, the DNN architectures are deployed on the roadside units (RSUs) which receive the broadcast vehicular data and run anomaly detection tasks to classify a particular message sequence as anomalous or genuine. Multiple DNN architectures are implemented in this experiment and their performance is compared using key evaluation metrics. Performance comparison of the proposed framework is also drawn against the prior work in this area. Our best performing deep learning-based scheme detects anomalous sequences with an accuracy of 98%, a great improvement over the set benchmark.

A V2V charging allocation protocol for electric vehicles in VANET

This paper tackles the problem of the propagation of charging information in Vehicular Ad-hoc Networks (VANETs) and the decentralized allocation of Vehicle-to-Vehicle (V2V) charging pairs. Electric Vehicles (EVs) have gained the attention of various sectors as a green alternative to the air-polluting fuel-based vehicles. However, due to the limitation of the EVs' battery capacity and the shortage in the number of charging stations causing significant waiting time to charge, V2V charging has emerged as a promising solution to help address these issues. Mainly, V2V solutions need to define two mechanisms: an information dissemination mechanism and a charging pairs allocation mechanism. In the literature, two architectures are proposed: centralized and semi-distributed using VANET. However, in addition to suffering from the single point of failure problem, centralized solutions consider only connected vehicles during the charging pair allocation. On the other hand, existing VANET based solutions address the problem of charging information dissemination while still performing the allocation of V2V charging pairs in a centralized way. In this paper, we propose an ad-hoc-based solution for V2V charging where both information dissemination and charging pairs allocation are performed over VANET. To do so, at the application layer of VANET, a decentralized protocol for V2V is proposed. This protocol is divided into two phases: providers announcement where consumers in the network can discover the nearby providers, and provider-consumer allocation where providers select the appropriate consumer based on the received OFFERs. At the network layer, Quality of Service-Optimized Link State Routing (QoS-OLSR) is used to find routing paths using the Multi-Point Relays (MPRs). These MPRs are in fact utilized by the protocol to propagate the announcement of the providers to the network. The simulation results show high performance of the proposed protocol in terms of packet delivery ratio and end-to-end delay. In addition, in terms of payoff and allocation rate, the proposed protocol performs better than centralized allocation in the presence of disconnected EVs.

Dynamic Periodic Location Encounter Network Analysis for Vehicular Social Networks

Vehicular social networks (VSNs) are considered as an emerging class of vehicular ad hoc networks (VANETs) with social attributes. In contrast to the traditional randomly distributed human social networks, vehicles normally only move along fıxed paths. As such, information sharing and forwarding become challenging because of the high speed of vehicles, complicated propagation environment, and quickly changing network topologies. The social characteristics and human behavior features are used to improve the effıciency of information delivery among vehicles in VANETs. In this paper, we propose a dynamic periodic location encounter network (DPLEN) model that considering both time-varying and location-varying characteristics of vehicles. We calculate the line similarity for each encountered pair of vehicles based on the periodic time and location information of the encounter event, and analyze social attributes based on the network model, such as the Community structure, Betweenness Centrality and K-shell. We also use the similarity to design two routing protocols for unicast and multicast patterns. After testing the proposed model on empirically collected datasets of mobility traces, the results show that these two routing protocols are able to greatly improve the efficiency of information forwarding in the VSNs.