Proposing a Distributed and Dynamic Bio-inspired Recognition of Identity in Vehicular Networks

Vehicular networks aim to provide a communication framework for moving vehicles, road infrastructure and pedestrians. Such kind of networks is the entrance to a new era of services that will make areas such as security and safety, information, transactions, entertainment and sustainability (green transportation) more efficient than they are today, especially in the upcoming era of autonomous vehicles and self-driving cars. However, the severe nature of vehicular environments makes efficient inter-vehicular communication very difficult to achieve. Vehicular Delay-Tolerant Networks (VDTN), as these networks are called, have very sparse, intermittent connections, and the lack of a fixed topology gives rise to one of the main challenges that they face: packet routing. A range of routing algorithms has been proposed in recent years to optimize communication in vehicular networks, and significant progress has been made in the matter but the quest for the optimal performance is still ongoing. In this paper, we explore a deep learning approach to the routing problem in VDTN, proposing a routing architecture and algorithm based on Deep Neural Networks that helps routers make packet forwarding decisions based on the current conditions of its surroundings. In order to assess the performance of the proposed architecture, simulations were run showing important gains in terms of network overhead and hop count with respect to popular routers, while maintaining acceptable packet delivery rates and average delivery delays.

Certificateless public key infrastructure (PKI) avoids the key escrow problem associated with identity-based PKI and has recently been widely employed in anonymous communication schemes for vehicular adhoc networks (VANETs). In existing certificateless anonymous signcryption schemes for VANETs, vulnerabilities such as potential attacks involving the substitution of pseudonyms and the forging of pseudonymous public-private key pairs exist due to the lack of “identity-key binding” and “non-linearity processing of public-private key pairs.” To address this issue, we propose an improved certificateless anonymous signcryption scheme based on elliptic curve cryptography. The scheme incorporates bilinear pairing as one of the authentication mechanisms, designs pseudonym generation algorithms and public-private key pair structures, and introduces a pseudonym verification mechanism. The correctness of the scheme is proven under the random oracle model, and its security is extensively demonstrated through detailed discussions on its confidentiality, authentication, unforgeability, anonymity, and traceability. Furthermore, the time and space complexity of the scheme are calculated. By comparing with recently published certificateless signcryption schemes, it is shown that the proposed scheme offers higher security with smaller computational and communication overheads. This certificateless vehicular network signcryption algorithm provides an efficient encryption solution for anonymous communication in vehicular networks, thereby ensuring the rapid development of secure technology for intelligent connected vehicle super terminals.

Reliable, secure, private, and fast communication in vehicular networks is extremely challenging due to the highly mobile nature of these networks. Contact time between vehicles is very limited and topology is constantly changing. Trusted communication in vehicular networks is of crucial importance because without trust, all efforts for minimizing the delay or maximizing the reliability could be voided. In this paper, we propose a trust-based framework for a safe and reliable information dissemination in vehicular networks. The proposed framework consists of two modules such that the first one applies three security checks to make sure the message is trusted. It assigns a trust value to each road segment and one to each neighborhood, instead of each car. Thus, it scales up easily and is completely distributed. Once a message is evaluated and considered to be trustworthy, our method then in the second module looks for a safe path through which the message is forwarded. Our frameworks are application-centric; in particular, it is capable of preserving traffic requirements specified by each application. Experimental results demonstrate that this framework outperforms other well-known routing protocols since it routes the messages via trusted vehicles.

Vehicular communications networks (VCNs) are created by vehicles equipped with short and medium range wireless communication technology. They include vehicular ad-hoc networks (VANETs), vehicle-to-vehicle and vehicle-to-infrastructure communications. VCNs enable a plethora of important applications and services, ranging from active safety or safety of life applications to traffic information, music/maps download and multi-hop internet connection. Recently, the promises of wireless communications to support vehicular safety applications have led to several national/international projects around the world. These include the consortia like Vehicle Safety Consortium (US), Car-2-Car Communication Consortium (Europe) and Advanced Safety Vehicle Program (Japan), standardization efforts like IEEE 802.11p (WAVE), and field trials like the large-scale Vehicle Infrastructure Integration Program (VII) in the US. All these efforts have as a main goal to improve safety in vehicular environments by the use of wireless communications, but also consider transport efficiency, comfort and environment. In comparison to other communication networks, VCNs come with unique attractive features: unlimited transmission power, predictable mobility and plethora of potential applications. However, to bring its potency to fruition, VCNs have to cope with formidable challenges that include: rapidly changing topology subject to frequent fragmentations and congestions, lack of connectivity redundancy, and the stringent application requirement on real-time and robust message delivery. In this chapter, we present a detailed description of the state of the art of this fast-moving research area pointing to research, projects and standardization efforts that have been done. We explore the unique features and challenges that characterise these highly dynamic networks as well as their requirements with respect to applications, types of communication, self-organization and security. We discuss various forwarding and routing strategies focussing on position-based techniques including ‘anchor-based routing‘. We survey various ‘intelligent flooding’ and information dissemination approaches. Scenarios for highways and cities are taken as example. We conclude by exploring future research directions in this field.

Vehicular networks are the key cornerstone of Intelligent Transportation Systems (ITS). With the growth of the technical revolution in 5G the next-generation networks, it is expected to meet various future communication requirements of Intelligent ITS. However, no single technology of 5G can efficiently accommodate the broad range of requirements of vehicular networks. However, the aggregation of various communication technologies with the 5G vehicular network could help to achieve ambitious goals like efficient network management, centralized view, and computation. The need for provisioning efficient network control, management, and high resource utilization in vehicular networks motivates the hierarchical 5G Next-generation vehicular networks. Blockchain has emerged as a viable solution to offer decentralization, transparency, and immutability among the stakeholders and manage trust in networking platforms. Motivated by the preceding discussion, in this chapter, we discuss the integration of software-defined networking (SDN), cloud computing over 5G mobile communication in vehicular networks, which is the key requirement of new hierarchical 5G-SDVNs. Then, programmable, efficient, and controllable network architecture is introduced for 5G-SDVN to achieve sustainable network development. Moreover, the centralization and flexibility provided by SDN and 5G communication technologies help to meet customer demand for various ITSs and vehicular network applications like bandwidth, high speed, and ubiquity, that can be employed in next-generation ITS. Finally, we present the SDN and cloud-enabled hierarchical future 5G-vehicular network architecture, its applications, issues, and challenges based on recent advances in technology and research.KeywordsBlockchainSoftware defined network5GIntelligent transport system

Low density of vehicles in rural areas and during the periods when the vehicle traffic is low in cities (such as at night) makes the classical ad-hoc network algorithms impractical for Vehicular Ad Hoc Networks. End-to-end connectivity requirement of these algorithms causes this deflection. Connections get broken when a suitable network element isn't available for data transfer. This problem which occurs in the low dense Vehicular Networks can be solved by using delay tolerant approaches in routing algorithms. By using the store-carry-forward method, data is carried by network elements until a suitable network element has been found. By this way, network connectivity is provided in a delayed manner between the vehicles which are momentarily not connected. In this paper, the impact of delayed transfer of data to an end-to-end transfer and the impact of data lifetime to the network performance are examined. Considering the speed of the vehicles and the desired throughput, lifetime of data is determined according to the network connectivity. Results contributes to the fulfilment of the Quality of Service (QoS) requirements in Vehicular Network applications.

For vehicular networking purposes the main communication standard approach on global scale is the IEEE 802.11p. This protocol-based system, together with the 3G cellular network, was the main elements of our car communications test-bed network. The developed test-bed implements an intelligent heterogeneous traffic safety network between cars and infrastructure. It also offers the possibility to exploit vehicle based sensor and observation data in order to generate intelligent real-time services and a service platform for vehicles. The main goal was to improve traffic safety with accident and weather condition related accurate services, but also to offer a platform for true bi-directional Internet-like networking experience tailored cost-effectively to vehicular environments. This paper presents the field test results of a new standard system for car communication, IEEE 802.11p vehicular networking measurements in Northern Finland (with supporting simulations). The results achieved in developed heterogeneous networking system (with special services) pilot testing are presented also. Based on the experience gained from both field measurements and pilot deployment, we propose a system deployment strategy for simple scenarios with general system efficiency estimation. It is shown that the solution has clear potential for a comprehensive heterogeneous vehicular communication and safety network.

The Ad-hoc Vehicular networks (VANET) was developed through the implementation of the concepts of ad-hoc mobile networks(MANET), which is swiftly maturing, promising, emerging wireless communication technology nowadays. Vehicular communication enables us to communicate with other vehicles and Roadside Infrastructure Units (RSU) to share information pertaining to the safety system, traffic analysis, Authentication, privacy, etc. As VANETs operate in an open wireless connectivity system, it increases permeable of variant type's security issues. Security concerns, however, which are either generally seen in ad-hoc networks or utterly unique to VANET, present significant challenges. Access Control List (ACL) can be an efficient feature to solve such security issues by permitting statements to access registered specific IP addresses in the network and deny statement unregistered IP addresses in the system. To establish such secured VANETs, the License number of the vehicle will be the Identity Number, which will be assigned via a DNS server by the Traffic Certification Authority (TCA). TCA allows registered vehicles to access the nearest two or more regions. For special vehicles, public access should be restricted by configuring ACL on a specific IP. Smart-card given by TCA can be used to authenticate a subscriber by checking previous records during entry to a new network area. After in-depth analysis of Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Average Delay, and Handover Delay, this research offers more secure and reliable communication in VANETs.

This paper investigates the usage of unmanned aerial vehicles (UAV s) as relays for data transmission in vehicular networks. We are motivated to address the challenges induced by the lack of direct communication between the vehicles and the infrastructures, such as signal coverage limitations and the existence of obstacles. We consider a scenario in which UAV relays perform cooperative communication in vehicular networks to offer extended coverage to the vehicles, which results in an improvement in the system capacity and reliability. Identifying an efficient UAV-assisted collaboration strategy for vehicular networks is challenging due to the vehicle mobility and the limited power consumption of UAVs. To tackle this problem, we propose a UAV-assisted cooperative relaying scheme based on deep reinforcement learning. To this end, we first determine the optimal transmit powers of a given set of UAV relays to maximize the total throughput of the system. Then, we formulate the UAV-assisted cooperative relaying process as a Markov process and apply a deep Q-network to obtain an effective UAV relay selection strategy. One of the advantages of our solution is that it does not require the knowledge of the vehicle moving trajectories. Through simulations, we demonstrate the effectiveness of our proposed method.

Vehicular communication networking is one of the most important building blocks of Intelligent Transportation System (ITS). The vehicular communication network is a wireless communication system enabling vehicles to communicate with each other as well as with roadside base stations. Mobility management of vehicles which move at high speeds and occasionally make a long journey is an interesting research area of vehicular communication networks. Recently, The Proxy Mobile IPv6 (PMIPv6) protocol is proposed for network-based mobility management to reduce the overhead of mobile nodes. PMIPv6 shifts the burden of the mobility management from mobile nodes to network agents to decrease the overhead and latency for the mobility management. In this paper, we derive the scenario of deploying PMIPv6 in vehicular communication networks and propose a new LMA handover mechanism for realizing the scenario. By carrying out the ns-2 based simulations, we verify the operability of the proposed mechanism.

Vehicular networks provide various applications for vehicles to improve road safety and traffic efficiency as well as infotainment. Security and privacy are of great importance for the deployment of vehicular networks. However, how to efficiently secure vehicular networks with privacy-preserving remains a big challenge. Many solutions have been proposed in the past decade, but most of them either rely too much on the ideal tamper-proofed devices or not being efficient enough for scenarios with high vehicle density. Meanwhile, in recent years, great progresses have been made in both the Long-Term Evolution (LTE) and the fifth-generation (5G) wireless network based vehicular networks. The 5G enabled vehicular networks are envisioned to support higher data transmission rate and a larger number of connected devices than the 802.11p and LTE based networks. Noting the great potential of 5G technology, in this paper we explore a new architecture of 5G software defined vehicular network and propose a secure and efficient privacy preserving authentication scheme for vehicular networks. The proposed scheme uses elliptic-curve public-key cryptography and a registration list to achieve efficient message authentication and to avoid the usage of ever-growing certificate revocation list. The security analysis shows that the proposed scheme has strong security guarantees without using the ideal tamper-proofed devices. Simulation results exhibit that the proposed scheme has ultra low computational overhead and packet loss ratio.

As one technique for autonomous driving, vehicular networks can achieve high efficiency with vehicle-and-infrastructure cooperation, bringing high safety and many value-added services. To achieve higher communication efficiency, much effort has been done to cope with the resource allocation issues for vehicular networks. Nevertheless, due to the strong nonconvexity and nonlinearity, the classical joint resource allocation problem in vehicular networks is typically NP-hard. The multiagent reinforcement learning (MARL) has emerged as a promising solution to tackle this challenge but its stability and scalability are not satisfactory when the amount of vehicles gets increased. In this article, we mainly investigate the issue of joint spectrum and power allocation in vehicular communication networks, and carefully consider the interactions between the vehicles and environment by incorporating the cooperative stochastic game theory with MARL, named complete-game MARL (CG-MARL), to achieve a better convergence and stability with the theoretical computational complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(n^{N})$ </tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> denoting the dimension of action space and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> denoting the number of V2X Vehicular. Furthermore, the mean-field game (MFG) theory is employed to further enhance the MARL for decreasing the horrible computing resource consumption caused by the CG-MARL to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(n^{2})$ </tex-math></inline-formula> while maintaining an approximate performance. The simulation results demonstrate that the proposed mean-field-aided MARL (MF-MARL) for vehicular network resource allocation can achieve 95% near-optimal performance with much lower complexity, which indicates its significant potentials in the scenarios with massive and dense vehicles.

Towards future intelligent vehicular network, the machine learning as the promising artificial intelligence tool is widely researched to intelligentize communication and networking functions. In this paper, we provide a comprehensive survey on various machine learning techniques applied to both communication and network parts in vehicular network. To benefit reading, we first give a preliminary on communication technologies and machine learning technologies in vehicular network. Then, we detailedly describe the challenges of conventional techniques in vehicular network and corresponding machine learning based solutions. Finally, we present several open issues and emphasize potential directions that are worthy of research for the future intelligent vehicular network.

The reliability of communication in vehicular networks depends mostly on the density of DSRC-enabled vehicles that form the network. In highway scenarios, and depending on the time of day, the probability of having a disconnected vehicular network can be very high, which hinders communication reliability. To improve communication in these scenarios, infrastructure points known as Road Side Units (RSU) may be used. RSUs, however, have an associated cost, and therefore the number of RSUs needs to be minimized while still providing a significant improvement on communications. In this paper we study the effect of including RSUs as relay nodes to improve communication in highway scenarios. We model the average time taken to propagate a packet to disconnected nodes (denoted as re-healing time) when considering both scenarios of connected and disconnected RSUs. We then compare the results of both these models and of a model with no RSUs. Results show significant improvements with RSU deployments, both connected and disconnected, particularly in multi-cluster communication scenarios.

In this paper, we propose to extend the WAVE Architecture with satellite, WiFi and cellular technologies. Many applications can be considered in vehicular networks which can be provided through one or several communication systems. In this paper, the map update application has been considered over dierent technologies. Simulations were run using NS3 simulator to compare the behavior of les downloads with 802.11p and with other technologies. The aim is to optimize the necessary amount of resource, and to determine the best combination of technologies to download maps.