5G-enabled Vehicular Networks Research Articles

Formal validation of authentication scheme in 5G-enabled vehicular networks using AVISPA

Smart transportation may come from 5G-enabled cars. Traffic reports include congestion, roads, and driving. Urbanisation and population growth increase traffic accidents and travel time. Traffic accidents kill and injure most people worldwide. Intelligent transportation systems (ITS) improves driver and pedestrian safety. This study connects the VANET to 5G to create a 5G-enabled vehicle network because the road-side unit (RSU) is expensive and unsecure. This study connects numerous automobiles to TA for 5G-BS D2D communication. Data transmissions between autos are risky. Several scholars suggest authentication techniques for safe vehicle-to-vehicle communications. Overhead may enable side-channel attacks with these tactics. A secure and effective efficient and secure authentication-privacy-preserving (ES-APP) system connected TA, 5G-BS, and on-border unit (OBU) was presented. Initialization, vehicle registration, parameter renewal, message signing, single and batch verification are ES-APP steps. The formal evaluation automated verification of internet security protocols and applications (AVISPA) tool with on-the-fly model-checker (OFMC) and attack searcher (ATSE) back-ends secures the suggested ES-APP technique. ES-APP appears impervious to active and passive AVISPA assaults.

Chebyshev Polynomial-Based Fog Computing Scheme Supporting Pseudonym Revocation for 5G-Enabled Vehicular Networks

The privacy and security of the information exchanged between automobiles in 5G-enabled vehicular networks is at risk. Several academics have offered a solution to these problems in the form of an authentication technique that uses an elliptic curve or bilinear pair to sign messages and verify the signature. The problem is that these tasks are lengthy and difficult to execute effectively. Further, the needs for revoking a pseudonym in a vehicular network are not met by these approaches. Thus, this research offers a fog computing strategy for 5G-enabled automotive networks that is based on the Chebyshev polynomial and allows for the revocation of pseudonyms. Our solution eliminates the threat of an insider attack by making use of fog computing. In particular, the fog server does not renew the signature key when the validity period of a pseudonym-ID is about to end. In addition to meeting privacy and security requirements, our proposal is also resistant to a wide range of potential security breaches. Finally, the Chebyshev polynomial is used in our work to sign the message and verify the signature, resulting in a greater performance cost efficiency than would otherwise be possible if an elliptic curve or bilinear pair operation had been employed.

Lattice-Based Lightweight Quantum Resistant Scheme in 5G-Enabled Vehicular Networks

Both security and privacy are central issues and need to be properly handled because communications are shared among vehicles in open channel environments of 5G-enabled vehicular networks. Several researchers have proposed authentication schemes to address these issues. Nevertheless, these schemes are not only vulnerable to quantum attacks but also use heavy operations to generate and verify signatures of messages. Additionally, these schemes need an expensive component RoadSide Unit (RSU)-aided scheme during the joining phase. To address these issues, we propose a lightweight quantum-resistant scheme according to the lattice method in 5G-enabled vehicular networks. Our proposal uses matrix multiplication instead of operations-based bilinear pair cryptography or operations-based elliptic curve cryptography to generate and verify signatures of messages shared among vehicles. Our proposal satisfies a significant reduction in performance, which makes it lightweight enough to handle quantum attacks. Our proposal is based on 5G technology without using any RSU-aided scheme. Security analysis showed that our proposal satisfies privacy and security properties as well as resists quantum attacks. Finally, our proposal also shows favorable performance compared to other related work.

Secure Edge Computing-Assisted Video Reporting Service in 5G-Enabled Vehicular Networks

Since traffic accidents occur frequently, a real-time video traffic reporting service is necessary in vehicular networks for a prompt response to accidents. Although the fifth-generation (5G) network provides a solution for real-time services in vehicular networks, the security and privacy of the services needs to be addressed first. The existing secure video reporting service schemes in 5G-enabled vehicular networks have significant computing, communication, and storage overheads, because of public key certificates, expensive bilinear pairing operations, and repeated video reporting to the cloud. To address these issues, we propose a secure edge computing-assisted video reporting service in 5G-enabled vehicular networks. In the proposed method, edge nodes complete the message verification and classification. Moreover, these nodes send the first received report message of the same accident to the designated official vehicles to realizes a local upload and download of video reports and to minimize the storage of repeated accident reports in the cloud. Security analysis indicates the proposed scheme is secure under the random oracle model and meets a series of vehicular networks requirements. In addition, performance evaluations show that the scheme achieves lower authentication overhead than existing signature schemes, and has lower total delay than other relevant video reporting service schemes.

FC-PA: Fog Computing-Based Pseudonym Authentication Scheme in 5G-Enabled Vehicular Networks

The fifth-generation (5G) technology-enabled vehicular network has been widely used in intelligent transportation in recent years. Since messages shared among vehicles are always broadcasted by openness environment’ nature, which is vulnerable to several privacy and security problems. To cope with this issue, several researchers have proposed pseudonym authentication schemes for the 5G-enabled vehicular network. Nevertheless, these schemes applied complected and time-consumed operations. Therefore, this paper proposes a fog computing-based pseudonym authentication (FC-PA) scheme to decrease the overhead of performance in 5G-enabled vehicular networks. The FC-PA scheme applies only one scalar multiplication operation of elliptic curve cryptography to prove information. A security analysis of our work explains that our scheme satisfies privacy-preserving and pseudonym authentication, which are resilient against common security attacks. With performance efficiency, our work can obtain better trade-offs between efficiency and security than the well-known recent works.

A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

Social Internet of Vehicles (SIoV) falls under the umbrella of social Internet of Things (IoT), where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology, which brings new opportunities and challenges, e.g., collaborative power trading can address the mileage anxiety of electric vehicles. However, it relies on a trusted central party for scheduling, which introduces performance bottlenecks and cannot be set up in a distributed network, in addition, the lack of transparency in state-of-the-art Vehicle-to-Vehicle (V2V) power trading schemes can introduce further trust issues. In this paper, we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center. Based on the game theory, we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare. We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching. The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

Provably Secure with Efficient Data Sharing Scheme for Fifth-Generation (5G)-Enabled Vehicular Networks without Road-Side Unit (RSU)

The vehicles in the fifth-generation (5G)-enabled vehicular networks exchange the data about road conditions, since the message transmission rate and the downloading service rate have been considerably brighter. The data shared by vehicles are vulnerable to privacy and security issues. Notably, the existing schemes require expensive components, namely a road-side unit (RSU), to authenticate the messages for the joining process. To cope with these issues, this paper proposes a provably secure efficient data-sharing scheme without RSU for 5G-enabled vehicular networks. Our work included six phases, namely: TA initialization (TASetup) phase, pseudonym-identity generation (PIDGen) phase, key generation (KeyGen) phase, message signing (MsgSign) phase, single verification (SigVerify) phase, and batch signatures verification (BSigVerify) phase. The vehicle in our work has the ability to verify multiple signatures simultaneously. Our work not only achieves privacy and security requirements but also withstands various security attacks on the vehicular network. Ultimately, our work also evaluates favourable performance compared to other existing schemes with regards to costs of communication and computation.

CM-CPPA: Chaotic Map-Based Conditional Privacy-Preserving Authentication Scheme in 5G-Enabled Vehicular Networks.

The security and privacy concerns in vehicular communication are often faced with schemes depending on either elliptic curve (EC) or bilinear pair (BP) cryptographies. However, the operations used by BP and EC are time-consuming and more complicated. None of the previous studies fittingly tackled the efficient performance of signing messages and verifying signatures. Therefore, a chaotic map-based conditional privacy-preserving authentication (CM-CPPA) scheme is proposed to provide communication security in 5G-enabled vehicular networks in this paper. The proposed CM-CPPA scheme employs a Chebyshev polynomial mapping operation and a hash function based on a chaotic map to sign and verify messages. Furthermore, by using the AVISPA simulator for security analysis, the results of the proposed CM-CPPA scheme are good and safe against general attacks. Since EC and BP operations do not employ the proposed CM-CPPA scheme, their performance evaluation in terms of overhead such as computation and communication outperforms other most recent related schemes. Ultimately, the proposed CM-CPPA scheme decreases the overhead of computation of verifying the signatures and signing the messages by 24.2% and 62.52%, respectively. Whilst, the proposed CM-CPPA scheme decreases the overhead of communication of the format tuple by 57.69%.

Chebyshev Polynomial-Based Scheme for Resisting Side-Channel Attacks in 5G-Enabled Vehicular Networks

The privacy and security vulnerabilities in fifth-generation (5G)-enabled vehicular networks are often required to cope with schemes based on either bilinear pair cryptography (BPC) or elliptic curve cryptography (ECC). Nevertheless, these schemes suffer from massively inefficient performance related to signing and verifying messages in areas of the high-density traffic stream. Meanwhile, adversaries could launch side-channel attacks to obtain sensitive data protected in a tamper-proof device (TPD) to destroy the system. This paper proposes a Chebyshev polynomial-based scheme for resisting side-channel attacks in 5G-enabled vehicular networks. Our work could achieve both important properties of the Chebyshev polynomial in terms of chaotic and semi-group. Our work consists of five phases: system initialization, enrollment, signing, verification, and pseudonym renew. Moreover, to resist side-channel attacks, our work renews periodically and frequently the vehicle’s information in the TPD. Security analysis shows that our work archives the privacy (pseudonym identity and unlikability) and security (authentication, integrity, and traceability) in 5G-enabled vehicular networks. Finally, our work does not employ the BPC or the ECC; its efficiency performance outperforms other existing recent works, making it suitable for use in vehicular networks.

CLASRM: A Lightweight and Secure Certificateless Aggregate Signature Scheme with Revocation Mechanism for 5G-Enabled Vehicular Networks

The rapid deployment of 5G technology has further strengthened the large-scale interconnection between sensing devices and systems and promoted the rapid development of smart cities and intelligent transportation systems. 5G-enabled vehicular networks take advantage of cellular vehicle-to-everything (C-V2X) technology to achieve the connection between moving vehicles, between vehicles and infrastructure, and between vehicles and the cloud, which can reduce the possibility of traffic jams and accidents, improve transportation efficiency, and realize automatic driving. Besides, 5G-enabled vehicular networks also provide infotainment services and industry application services. High-strength data transmission, however, will bring a serious burden of resource overhead, and there are hidden dangers of security and privacy in the communication process of vehicular networks. Some current vehicular network authentication schemes adopt public key infrastructure-based (PKI-based) and identity-based authentication methods to achieve conditional privacy preservation. Still, these schemes are too expensive and cannot address the problems of costly certificate management or risky key escrow. Some schemes use computationally complex bilinear pairing operations that result in low efficiency and do not consider the revocation of malicious nodes so that they cannot effectively prevent further malicious attacks. This paper proposes a lightweight certificateless aggregate signature (CLAS) scheme with a revocation mechanism suitable for 5G-enabled vehicular networks in response to the above problems. Our proposed scheme uses aggregation signature technology to aggregate multiple signatures into a single short signature, thus reducing communication overhead and storage overhead of road side units (RSUs). Furthermore, our proposed scheme utilizes the elliptic curve cryptography (ECC) to reduce verification time and computational overhead. Moreover, in order to prevent malicious users from sending invalid signatures to attack, our proposed scheme uses binary search to identify invalid signatures and introduces a cuckoo filter to revoke malicious users to prevent reattack. Finally, formal proof and experimental analysis show that our proposed scheme has greater advantages with respect to security and efficiency compared with the previous schemes.

Password-Guessing Attack-Aware Authentication Scheme Based on Chinese Remainder Theorem for 5G-Enabled Vehicular Networks

The new fifth-generation (5G) cellular networks dramatically improve the speed of message transmissions. Most existing authentication schemes that secure 5G communication rely heavily on the vehicle’s tamper-proof device (TPD) and roadside units (RSUs) to store the system’s master key. However, it only takes a single compromised TPD to render the whole system insecure. We propose a password-guessing attack-aware authentication scheme based on the Chinese Remainder Theorem (CRT) to secure inter-vehicle communication on 5G-enabled vehicular networks to address this issue. The trusted authorities (TAs) in the proposed scheme generate and broadcast new group keys to the vehicles assisted by CRT. Moreover, since the system’s master key does not need to be preloaded, the proposed scheme only requires realistic TPDs. The proposed scheme overcomes password-guessing attacks and guarantees top-level security for entire 5G-enabled vehicular networks. The security analysis indicates that the proposed scheme is secure against adaptive chosen-message attacks under the random oracle model and meets the security requirements of a 5G-enabled vehicular network. Since cryptographic operations based on elliptic curve cryptography are employed, the performance evaluation shows that the proposed scheme outperforms the eight existing schemes in terms of computation and communication costs.

MSR-DoS: Modular Square Root-Based Scheme to Resist Denial of Service (DoS) Attacks in 5G-Enabled Vehicular Networks

Traffic safety and efficiency are extremely significant in both private and public transportation. The fifth-generation (5G)-enabled vehicular networks works wirelessly to share information among vehicles for helping drivers and passengers. Since the vehicle broadcasts the traffic status messages, privacy and security are considered as a challenging issue in 5G-enabled vehicular networks. In order to satisfy these privacy and security requirements, many privacy-preserving and security attacks schemes have been proposed. Nevertheless, since these schemes use a complex elliptic curve and bilinear pair cryptography operations, the performance efficiency of in terms of communication and computational costs is not satisfactory, which denial of service (DoS) attacks occurs. To address this, this paper proposes modular square root-based to resist denial of service (DoS) attacks (MSR-DoS) scheme in 5G-enabled vehicular networks. Our MSR-DoS scheme satisfies authenticity of source, integrity of message, pseudonym privacy-preserving, unlinkable, traceable and revocable in vehicular networks. The security of our work is proved under burrows abadi needham (BAN) logic. The performance analysis and comparison shows that MSR-DoS scheme has less communication and computational costs as compared to the most recent existing works. Meanwhile, the proposed MSR-DoS scheme reduces the computation overhead of signing the message and verifying the message by 99.80% and 98.55%, respectively.

A novel proactive controller deployment protocol for 5G-enabled software-defined Vehicular Networks

With the vast expansion of data loads across numerous safety and infotainment applications, the software-defined paradigm has become a critical component for 5G-enabled and intelligent vehicular networks. By transforming the network’s rigid hardware devices into programmable units, SDVN offers a broad view of the entire network status and a standard interface between heterogeneous wireless access technologies. However, deploying a centralized control unit carries several problems, such as bottleneck issues and densification issues. A distributed control plane is a potential alternative to a centralized control plane. Still, it poses many questions about the optimal location of local control units and the number of control units required for a given network structure. In this article, we propose an efficient proactive controller deployment and assignment strategy for distributed 5G-enabled software-defined Vehicular Networks based on predictive vehicles’ traffic flow information and communication delays between switch-enabled roadside units. A thorough performance evaluation is conducted under different vehicular network’s mobility scenarios. We assess the performance of the proposed method in terms of communication latency and load on the subsequent control units and their assigned set of switches. The proposed proactive clustering method is compared to different forms of controller placement methods. The results showed that the proposed approach reduced the network’s communication delays while maintaining a balanced distributed load among controllers over the simulation time.

Resource Allocation for 5G-Enabled Vehicular Networks in Unlicensed Frequency Bands

In this paper, a resource allocation problem is formulated to maximize the throughput of vehicular user equipments (VUEs) in both licensed, and unlicensed frequency bands under constraints of reliability, and latency for vehicular communications as well as the Quality of Service (QoS) for WiFi network based on a network system with coexisting VUEs, and WiFi user equipments (WUEs). In the system, the VUEs are able to access to the unlicensed frequency bands, and the interference among the VUEs, and WUEs are mitigated by the listen-before-talk (LBT) scheme or the duty cycle scheme. Due to the mixed integer nonlinear programming (MINLP) objective in the optimization problem, the problem is hard to be solved directly. Instead, we solve the problem by two steps. The number of VUEs offloaded to unlicensed frequency bands as well as the time factor for duty cycle scheme is firstly determined, and then, the optimization problem is converted into a convex optimization problem, which is solved by the proposed Lagrange Duality Method (LDM). Numerical results show the efficiency of our proposed application scenario compared to case with only LTE system or only WiFi system. Moreover, compared to traditional Greedy algorithm, the proposed algorithm performs better in terms of throughput with a guaranteed QoS of WUEs.

Mobility Management in 5G-enabled Vehicular Networks

Over the past few years, the next generation of vehicular networks is envisioned to play an essential part in autonomous driving, traffic management, and infotainment applications. The next generation of intelligent vehicular networks enabled by 5G systems will integrate various heterogeneous wireless techniques to enable time-sensitive services with guaranteed quality of service and ultimate bandwidth usage. However, to allow the dense diversity of wireless technologies, seamless and reliable wireless communication protocols need to be thoroughly investigated in vehicular networks environment. Henceforth, efficient mobility management protocols that mitigate the challenges of vehicles’ mobility is essential to support massive data loads throughout various applications. In this article, we review different mobility management protocols and their ability to address issues related to 5G-enabled vehicular networks within the related works. First, we provide a broad view of existing models of vehicular networks and their applicability to the next generation of wireless networks. Next, we propose a classification of several vehicular network models that suit the 5G wireless network, followed by a thorough discussion of the mobility management challenges in each of these network models that need to be addressed and then discuss each of their benefits and drawbacks accordingly.

Reliable and Efficient Content Sharing for 5G-Enabled Vehicular Networks

Conditional privacy preservation and message authentication serve as the primary research issues in terms of security in vehicular networks. With the arrival of 5G era, the downloading speed of network services and the message transmission speed have significantly improved. Consequently, the content exchanged by users in vehicular networks is not limited to traffic information, and vehicles moving at high speeds can share a wide variety of contents. However, sharing content reliably and efficiently remains challenging owing to the fast-moving character of vehicles. To solve this problem, we propose a reliable and efficient content sharing scheme in 5G-enabled vehicular networks. The vehicles with content downloading requests quickly filter the adjacent vehicles to choose capable and suitable proxy vehicles and request them for content services. Thus, the purpose of obtaining a good hit ratio, saving network traffic, reducing time delay, and easing congestion during peak hours can be achieved. The security analysis indicates that the proposed scheme meets the security requirements of vehicular networks. Our cryptographic operations are based on the elliptic curve, and finally, the proposed scheme also displays favorable performance compared to other related schemes.

Partial Computation Offloading and Adaptive Task Scheduling for 5G-Enabled Vehicular Networks

A variety of novel mobile applications are developed to attract the interests of potential users in the emerging 5G-enabled vehicular networks. Although computation offloading and task scheduling have been widely investigated, it is rather challenging to decide the optimal offloading ratio and perform adaptive task scheduling in high-dynamic networks. Furthermore, the scheduling policy made by the network operator may be violated, since vehicular users are rational and selfish to maximize their own profits. By considering the incentive compatibility and individual rationality of vehicular users, we present POETS, an efficient partial computation offloading and adaptive task scheduling algorithm to maximize the overall system-wide profit. Specially, a two-sided matching algorithm is first proposed to derive the optimal transmission scheduling discipline. After that, the offloading ratio of vehicular users can be obtained through convex optimization, without any information of other users. Furthermore, a non-cooperative game is constructed to derive the payoff of vehicular users that can reach the equilibrium between users and the network operator. Theoretical analyses and performance evaluations based on real-world traces of taxies demonstrate the effectiveness of our proposed solution.

Non-Orthogonal Multiple Access with Wireless Caching for 5G-Enabled Vehicular Networks

The proliferation of connected vehicles along with the high demand for rich multimedia services constitute key challenges for the emerging 5G-enabled vehicular networks. These challenges include, but are not limited to, high spectral efficiency and low latency requirements. Recently, the integration of cache-enabled networks with NOMA has been shown to reduce the content delivery time and traffic congestion in wireless networks. Accordingly, in this article, we envisage cache-aided NOMA as a technology facilitator for 5G-enabled vehicular networks. In particular, we present a cache-aided NOMA architecture, which can address some of the aforementioned challenges in these networks. We demonstrate that the spectral efficiency gain of the proposed architecture, which depends largely on the cached contents, significantly outperforms that of conventional vehicular networks. Finally, we provide deep insights into the challenges, opportunities, and future research trends that will enable the practical realization of cache-aided NOMA in 5G-enabled vehicular networks.

Security and Privacy Challenges in 5G-Enabled Vehicular Networks

Recently, many academic institutions and standardization organizations have conducted research on vehicular communications based on LTE or 5G. As the most important standardization organization of cellular systems, the 3rd Generation Partnership Project (3GPP) has been developing the standard supporting vehicle-to-everything (V2X) services based on LTE, and has already prepared the roadmap toward 5G-based V2X services. With the emergence of new technologies and applications, such as connected autonomous vehicles, 5G-enabled vehicular networks face a variety of security and privacy challenges, which have not been fully investigated. In this article, we first present the infrastructure of 5G-enabled vehicular networks. Then the essential security and privacy aspects of V2X in LTE specified by 3GPP are introduced. After that, as a case study, we investigate the security and privacy issues of a 5G-enabled autonomous platoon, and propose several candidate solutions, including secure group setup with privacy preservation, distributed group key management, and cooperative message authentication. Finally, we discuss the security and privacy challenges in 5G-enabled vehicular networks.

Computation Migration and Resource Allocation in Heterogeneous Vehicular Networks: A Deep Reinforcement Learning Approach

With the development of 5G technology, the requirements for data communication and computation in emerging 5G-enabled vehicular networks are becoming increasingly stringent. Computation-intensive or delay-sensitive tasks generated by vehicles need to be processed in real time. Mobile edge computing (MEC) is an appropriate solution. Wireless users or vehicles can offload computation tasks to the MEC server due to it has strong computation ability and is closer to the wireless users or vehicles. However, the communication and computation resources of the single MEC are not sufficient for executing the continuously generated computation-intensive or delay-sensitive tasks. We consider migrating computation tasks to other MEC servers to reduce the computation and communication pressure on current MEC server. In this article, we construct an MEC-based computation offloading framework for vehicular networks, which considers time-varying channel states and stochastically arriving computation tasks. To minimize the total cost of the proposed MEC framework, which consists of the delay cost, energy computation cost, and bandwidth cost, we propose a deep reinforcement learning-based computation migration and resource allocation (RLCMRA) scheme that requires no prior knowledge. The RLCMRA algorithm can obtain the optimal offloading and migration policy by adaptive learning to maximize the average cumulative reward (minimize the total cost). Extensive numerical results show that the proposed RLCMRA algorithm can adaptively learn the optimal policy and outperform four other baseline algorithms.