Unique Characteristics Of Vehicular Networks Research Articles

VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks Using Deep Reinforcement Learning

Vehicular social networking is an emerging application of the Internet of Vehicles (IoV) which aims to achieve seamless integration of vehicular networks and social networks. However, the unique characteristics of vehicular networks, such as high mobility and frequent communication interruptions, make content delivery to end-users under strict delay constraints extremely challenging. In this paper, we propose a social-aware vehicular edge computing architecture that solves the content delivery problem by using some vehicles in the network as edge servers that can store and stream popular content to close-by end-users. The proposed architecture includes three main components: 1) the proposed social-aware graph pruning search algorithm computes and assigns the vehicles to the shortest path with the most relevant vehicular content providers. 2) the proposed traffic-aware content recommendation scheme recommends relevant content according to its social context. This scheme uses graph embeddings in which the vehicles are represented by a set of low-dimension vectors (vehicle2vec) to store information about previously consumed content. Finally, we propose a deep reinforcement learning (DRL) method to optimise the content provider vehicle distribution across the network. The results obtained from a real-world traffic simulation show the effectiveness and robustness of the proposed system when compared to the state-of-the-art baselines.

A Probabilistic Approach for Cooperative Computation Offloading in MEC-Assisted Vehicular Networks

Mobile edge computing (MEC) has been an effective paradigm for supporting computation-intensive applications by offloading resources at network edge. Especially in vehicular networks, the MEC server, is deployed as a small-scale computation server at the roadside and offloads computation-intensive task to its local server. However, due to the unique characteristics of vehicular networks, including high mobility of vehicles, dynamic distribution of vehicle densities and heterogeneous capacities of MEC servers, it is still challenging to implement efficient computation offloading mechanism in MEC-assisted vehicular networks. In this article, we investigate a novel scenario of computation offloading in MEC-assisted architecture, where task upload coordination between multiple vehicles, task migration between MEC/cloud servers and heterogeneous computation capabilities of MEC/cloud severs, are comprehensively investigated. On this basis, we formulate cooperative computation offloading (CCO) problem by modeling the procedure of task upload, migration and computation based on queuing theory, which aims at minimizing the delay of task completion. To tackle the CCO problem, we propose a probabilistic computation offloading (PCO) algorithm, which enables MEC server to independently make online scheduling based on the derived allocation probability. Specifically, the PCO transforms the objective function into augmented Lagrangian and achieves the optimal solution in an iterative way, based on a convex framework called Alternating Direction Method of Multipliers (ADMM). Last but not the least, we implement the simulation model. The comprehensive simulation results show the superiority of the proposed algorithm under a wide range of scenarios.

Bandwidth-Aware Traffic Sensing in Vehicular Networks with Mobile Edge Computing.

Traffic sensing is one of the promising applications to guarantee safe and efficient traffic systems in vehicular networks. However, due to the unique characteristics of vehicular networks, such as limited wireless bandwidth and dynamic mobility of vehicles, traffic sensing always faces high estimation error based on collected traffic data with missing elements and over-high communication cost between terminal users and central server. Hence, this paper investigates the traffic sensing system in vehicular networks with mobile edge computing (MEC), where each MEC server enables traffic data collection and recovery in its local server. On this basis, we formulate the bandwidth-constrained traffic sensing (BCTS) problem, aiming at minimizing the estimation error based on the collected traffic data. To tackle the BCTS problem, we first propose the bandwidth-aware data collection (BDC) algorithm to select the optimal uploaded traffic data by evaluating the priority of each road segment covered by the MEC server. Then, we propose the convex-based data recovery (CDR) algorithm to minimize estimation error by transforming the BCTS into an -norm minimization problem. Last but not the least, we implement the simulation model and conduct performance evaluation. The comprehensive simulation results verify the superiority of the proposed algorithm.

Vehicular Communications: A Network Layer Perspective

Vehicular communications, referring to information exchange among vehicles, pedestrians, and infrastructures, have become very popular and been widely studied recently due to its great potential to support intelligent transportation and various safety applications. Via vehicular communications, manually driving vehicles and autonomous vehicles can collect useful information to improve traffic safety and support infotainment services. In this paper, we provide a comprehensive overview of recent research on enabling efficient vehicular communications from the network layer perspective. First, we introduce general applications and unique characteristics of vehicular networks and the corresponding classifications. Based on different driving patterns of vehicles, we divide vehicular networks into two categories, i.e., manually driving vehicular networks and automated driving vehicular networks, and then discuss the available communication techniques, network structures, routing protocols, and handoff strategies applied in these vehicular networks. Finally, we identify the challenges confronted by the current vehicular communications and present the corresponding research opportunities.

Lightweight Cryptographic Techniques for Automotive Cybersecurity

A new integration of wireless communication technologies into the automobile industry has instigated a momentous research interest in the field of Vehicular Ad Hoc Network (VANET) security. Intelligent Transportation Systems (ITS) are set up, aiming to offer promising applications for efficient and safe communication for future automotive technology. Vehicular networks are unique in terms of characteristics, challenges, architecture, and applications. Consequently, security requirements related to vehicular networks are more complex as compared to mobile networks and conventional wireless networks. This article presents a survey about developments in vehicular networks from the perspective of lightweight cryptographic protocols and privacy preserving algorithms. Unique characteristics of vehicular networks are presented which make the embedded security applications computationally hard as well as memory constrained. The current study also deals with the fundamental security requirements, essential for vehicular communication. Furthermore, awareness of security threats and their cryptographic solutions in terms of future automotive industry are discussed. In addition, asymmetric, symmetric, and lightweight cryptographic solutions are summarized. These strategies can be enhanced or incorporated all in all to meet the security perquisites of future cars security.

Improving Throughput and Fairness of Convergecast in Vehicular Networks

Delivering data from source vehicles to infrastructures, or convergecast , is a fundamental operation in vehicular networks. However, the network capacity of vehicular network is always limited because of scarce inter-vehicle contacts. Thus, throughput maximization of convergecast in vehicular networks is of great importance. The unique characteristics of vehicular networks, however, present great challenges including frequent connection unavailability and opportunistic contacts. We propose an approach called ConvergeCode for improving the convergecast throughput in vehicular networks, which employs random linear coding for packet delivery. A vehicle randomly combines all received coded data and forwards it to any contacted vehicles. Through extensive empirical study based on the two large datasets of real GPS traces, we make the key observation that significant throughput gain can be achieved by using network coding but a serious fairness issue arises. In this paper, we study the problem of maximizing the throughput of convergecast in vehicular networks at the same time enhancing the fairness among different source nodes. We first formulate the problem of allocating inter-vehicle contacts as a lexicographical max-min multi-source flow problem, and then develop an efficient approximation algorithm with $\epsilon$ -approximation guarantee. Simulations based on real vehicular GPS traces have been performed and results show that the throughput is improved by 74-110 percent while the lexicographical max-min fairness is achieved.

Performance evaluation of a cooperative reputation system for vehicular delay-tolerant networks

Abstract In the last decade, both scientific community and automotive industry enabled communications among vehicles in different kinds of scenarios proposing different vehicular architectures. Vehicular delay-tolerant networks (VDTNs) were proposed as a solution to overcome some of the issues found in other vehicular architectures, namely, in dispersed regions and emergency scenarios. Most of these issues arise from the unique characteristics of vehicular networks. Contrary to delay-tolerant networks (DTNs), VDTNs place the bundle layer under the network layer in order to simplify the layered architecture and enable communications in sparse regions characterized by long propagation delays, high error rates, and short contact durations. However, such characteristics turn contacts very important in order to exchange as much information as possible between nodes at every contact opportunity. One way to accomplish this goal is to enforce cooperation between network nodes. To promote cooperation among nodes, it is important that nodes share their own resources to deliver messages from others. This can be a very difficult task, if selfish nodes affect the performance of cooperative nodes. This paper studies the performance of a cooperative reputation system that detects, identify, and avoid communications with selfish nodes. Two scenarios were considered across all the experiments enforcing three different routing protocols (First Contact, Spray and Wait, and GeoSpray). For both scenarios, it was shown that reputation mechanisms that punish aggressively selfish nodes contribute to increase the overall network performance.

Closed User Group Automotive Communication Network Based on Addressing at Physical Layer

Intelligent Vehicle communication is the keyword for the emerging vehicular technologies such as group cooperative driving, real time Engine Operating parameters (EOP) monitoring, collision warning, geo location based mobility applications, and classical voice and data conveyance. The technologies require extensive interaction between the peers which mostly use the framework of the state of the art cellular or radio trunking networks. This may vitiate the network performance due to the surge in mobility management messages originated by the devices plugged in the vehicles. The performance may be severely impacted due to the unique characteristics of vehicular networks e.g., high mobility. Due to the high proliferation of these Machine to Machine (M2M) and Machine to Application (M2A) devices in near future, the cell sizes will shrink, resulting in more signalling messages in the network. Considering classical voice communication services for typical car fleet implementations, the radio trunking networks have capacity constrains due to inability of frequency reuse and absence of mobility management techniques. The alternative is to seek out an access technology considering the fact that a more intelligent physical layer can be employed directly for addressing and mobility management. In this paper the authors address a Closed User Group network implementation for Vehicle to Vehicle/central office communication which can actuate voice and data communication without incorporating any application layer.

Mobility and handoff management in vehicular networks: a survey

AbstractMobility management is one of the most challenging research issues for vehicular networks to support a variety of intelligent transportation system (ITS) applications. The traditional mobility management schemes for Internet and mobile ad hoc network (MANET) cannot meet the requirements of vehicular networks, and the performance degrades severely due to the unique characteristics of vehicular networks (e.g., high mobility). Therefore, mobility management solutions developed specifically for vehicular networks would be required. This paper presents a comprehensive survey on mobility management for vehicular networks. First, the requirements of mobility management for vehicular networks are identified. Then, classified based on two communication scenarios in vehicular networks, namely, vehicle‐to‐vehicle (V2V) and vehicle‐to‐infrastructure (V2I) communications, the existing mobility management schemes are reviewed. The differences between host‐based and network‐based mobility management are discussed. To this end, several open research issues in mobility management for vehicular networks are outlined. Copyright © 2009 John Wiley & Sons, Ltd.

A Novel Mobility Management Scheme for Integration of Vehicular Ad Hoc Networks and Fixed IP Networks

Integration of vehicular ad hoc network and fixed IP network is important to provide Internet connection and mobile data service for vehicles. However, the unique characteristics of vehicular networks, such as linear topology and constrained movements of vehicles, are not considered in the conventional mobility management schemes. Using conventional schemes, unnecessary management messages are generated and the connections to roadside-installed base stations are not fully utilized. As the results, bandwidth is wasted and data delivery ratio is not maximized. In this paper, we propose a novel mobility management scheme to integrate vehicular ad hoc network and fixed IP networks more efficiently. The proposed scheme manages mobility of vehicles based on street layout as well as the distance between vehicles and base stations. Utilizing the unique characteristics of vehicular networks, the proposed scheme has substantially less mobility management overhead and higher data delivery ratio. The proposed scheme is simulated by SUMO (a vehicular traffic simulator) and QualNet (a data network simulator). The simulation results show that the proposed scheme reduced the mobility management overhead up to 63% and improved the data delivery ratio up to 90%.