Mobile Vehicular Networks Research Articles

Anti-Jamming Technique for IRS Aided JRC System in Mobile Vehicular Networks

Anti-Jamming Technique for IRS Aided JRC System in Mobile Vehicular Networks

Coordination-free Safety Messages Dissemination Protocol for Vehicular Networks

Many vehicular safety applications depend on the rapid and reliable dissemination of safety messages to vehicles at risk. In order to allow sufficient reaction time for emergency events in adverse driving and road conditions, disseminating safety messages over multi-hop vehicles is often needed. However, owing to the capacity limitation of the shared control channel, disseminating safety messages in highly mobile and dynamic vehicular network conditions is challenging. In this article, a Zero-Coordination Opportunistic Routing (ZCOR) algorithm is proposed to deliver mission-critical life safety messages over limited target geocast regions. ZCOR is scalable and robust over dynamic VANET conditions with low rebroadcast overhead. In addition, ZCOR exploits neighbor knowledge for coordinationfree opportunistic packet relay using the novel concept of Circle of Trust (CoT), which defines the range of reliable local neighbor knowledge collection. The performance of ZCOR is evaluated through extensive and realistic simulations capturing time-correlated vehicular channel characteristics.

A Review on Contemporary Handover Mechanisms for Wireless Networks

Wireless communication is undergoing a paradigm shift with emergence of high performance machine learning (ML) computing and internet of things (IoT). The demand for bandwidth has significantly risen due to multimedia applications and high speed data transfer. However, with increasing number of cellular users, the challenge is to effectively manage the limited spectrum allotment for wireless communication while maintaining satisfactory quality of service. Hence, different multiplexing techniques have been used to effectively use the available bandwidth. Recently, the concept of automatic fallback in receivers are gaining popularity due to high mobility in vehicular networks and IoT. Automatic fallback and handover mechanisms often utilize the channel state information (CSI) of the radio and can switch between technologies to provide the best available quality of service for particular spatial and temporal channel conditions. With the advent of machine learning and deep learning methods, estimating the channel state information has become computationally efficient and feasible thereby improving the performance metrics of the system. This paper presents a comprehensive review on the need for cognitive systems with CSI availability, handover mechanisms in wireless networks and different strategies involved in estimating the channel state information for wireless networks. Keywords: Wireless Networks, Handover, Channel State Information (CSI), Cognitive Networks, Machine Learning (ML).

Secure Transmission Scheme Based on Joint Radar and Communication in Mobile Vehicular Networks

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, potential eavesdropping is one of the main threats to V2V communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multiple armed bandit problem is designed to implement the secret key switching procedure when it is needed. Through constant perception-execution loops (PELs), the security and confidentiality is improved for the authorized vehicles in their behavioral interactions with the illegal eavesdropper. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

Spatial-Index Modulation Based Orthogonal Time Frequency Space System in Vehicular Networks

In this paper, a spatial-index modulation (SIM) based orthogonal time frequency space (OTFS) system, named SIM-OTFS, is proposed to enhance the effectiveness and the reliability of high mobility vehicular networks in intelligent transportation systems. The SIM-OTFS system adopts a three dimensional index modulation (IM) technique which utilizes the transmit antenna, delay, and Doppler indexes in the space and delay-Doppler domains, respectively, to achieve a higher transmission rate. Considering the characteristics of vehicular networks, we first present the SIM-OTFS system design and the corresponding signal processing. Then, we derive the average bit error rate (ABER) upper bound of the SIM-OTFS system by the union bound theory. Moreover, the diversity, the coding gain, and the complexity of the SIM-OTFS system are further investigated. Numerical results verify the theoretical analysis of the ABER and the diversity of the SIM-OTFS system, which shows the superiority of the SIM-OTFS system in the ABER performance over the multiple-input and multiple-output (MIMO) based OTFS (MIMO-OTFS) system. Meanwhile, the SIM-OTFS system realizes better performance than the spatial modulation (SM) and IM based orthogonal frequency division multiplexing (SM-OFDM-IM) system in high mobility vehicular communication with reasonable complexity sacrifice. Furthermore, the influence of the resolvable multipaths of the channel in vehicular networks on the ABER performance of the SIM-OTFS system is also illustrated.

Connectivity Improvement of Hybrid Millimeter Wave and Microwave Vehicular Networks

The network connectivity while traveling in a vehicle is an important issue, which needs to be addressed by mobile vehicular networks. This paper proposes a novel scheme to improve the connectivity of mobile vehicular networks. In particular, the paper proposes a medium access control (MAC) layer hybrid mmWave and microwave scheme for vehicular networks, and leverage their capabilities to improve the vehicle’s connectivity. The novel computational model is derived to evaluate the connectivity for the proposed scheme. The model is used for performance analysis of vehicles moving on a multi-lane highway road and getting connectivity with road-side-units (RSUs) deployed along the roads. Our analysis considers that reference VN has perfect channel state information. It is assumed that the RSU radiates ubiquitously on the road surface using microwave radio access technology (RAT), while it radiates directionally towards reference VN using mmWave RAT. For mathematical analysis, the directionality in mmWave RAT is well approximated by a sectored antenna model. The analysis for the proposed scheme is compared with the existing mmWave network and packet data convergence protocol (PDCP) layer hybrid scheme. The analysis claims that the proposed hybrid scheme significantly improves the connectivity performance in mobile vehicular networks over the existing schemes. The computation results are validated with the simulation results. Also, the paper offers parametric analysis for connectivity probability with vehicle speed and slot duration to enable its practical implementation in 5G/6G technologies.

Exploiting Deep Reinforcement Learning for Coverage Maximization and Cost Minimization in Air Quality Monitoring Systems

Air-quality monitoring is highly desired in modern life, where environmental problems have become more serious. Such a task requires continuous surveillance over large urban areas, which is costly both in infrastructure and sensor resources. Hence it gives rise to Vehicular Mobile Networks (VMNs), in which mobile vehicles play the role of sensor devices and constantly monitor the area. However, with extensive constraints, the optimization of both maximizing the coverage and minimizing the sensory costs is vastly challenging. In this research, we resolve the problem in terms of a learning process. Applying deep reinforcement learning, we outperform more than 1.65% in terms of coverage, compared to common setups while remaining considerably small sensory costs in terms of sensor activation rate. We conduct extensive experiments for a better understanding of the behavior of the deep reinforcement learning model.

A Distributed Authentication Scheme Based on Smart Contract for Roaming Service in Mobile Vehicular Networks

Secure and real-time communication is an essential condition in mobile vehicular networks, and this requires secure authentication and seamless access enabled by roaming services. As a security inspector, roaming authentication ensures that legitimate users can access the network securely. However, today’s roaming authentication protocols authenticate users with the help of centralized authentication servers, leading to the risk of the single point of failure and roaming fraud. The massive device access in 5G networks further exacerbates the losses when problems occur. In light of it, we propose a decentralized fraud-proof roaming authentication framework based on blockchain. We leverage smart contracts to implement a roaming authentication protocol, including user/AP registration, authentication, and revocation. For higher efficiency, we utilize the Bloom filter for the revocation process. In addition, we design an unforgeable and undeniable billing scheme based on hash chain technology. Security and performance analysis show that the proposed roaming authentication scheme can provide the required security features while incurring an acceptable authentication delay.

Joint Communication, Computation, and Control for Computational Task Offloading in Vehicle-Assisted Multi-Access Edge Computing

Future generation of Electric Vehicles (EVs) equipped with modern technologies will impose a significant burden on computation and communication to the network due to the vast extension of onboard infotainment services. To overcome this challenge, multi-access edge computing (MEC) or Fog Computing can be employed. However, the massive adoption of novel infotainment services such as Augmented Reality, Virtual Reality-based services will make the MEC and Fog resources insufficient. To cope with this issue, we propose a system model with onboard computation offloading, where an EV can utilize its neighboring EVs resources that are not resource-constrained to enhance its computing capacity. Then, we propose to solve the problem of computational task offloading by jointly considering the communication, computation, and control in a mobile vehicular network. We formulate a mixed-integer non-linear problem (MINLP) to minimize the trade-off between latency and energy consumption subject to the network resources and the mobility of EVs. The formulated problem is solved via the block coordination descent (BCD) method. In such a way, we decompose the original MINLP problem into three subproblems which are resource block allocation (RBA), power control and interference management (PCP), and offload decision problem (ODP). We then alternatively obtain solutions of RBA and PCP via the duality theory, and the third sub-problem is solvable via the relaxation method and alternating direction Lagrangian multiplier method (ADMM). Numerical results reveal that the proposed solution BCD-based algorithm performs a fast convergence rate.

Multi-Connectivity Enhanced Communication-Incentive Distributed Computation Offloading in Vehicular Networks

Flexible resource scheduling and network forecast are crucial functions to enhance mobile vehicular network performances. However, BaseStations (BSs) and their computing unit which undertake the functions cannot meet the delay requirement because of limited computation capability. Offloading the time-sensitive functions to User Equipment (UE) is believed to be an effective method to tackle this challenge. The disadvantage of the method is offloading occupies communication resources, which deteriorate the system capability. To better coordinate offloading and communication, a multi-connectivity enhanced joint scheduling scheme for distributed computation offloading and communication resources allocation in vehicular networks is proposed in this article. Computation tasks are divided into many slices and distributed to UEs to aggregate the computation capability. A communication-incentive mechanism is provided for involving UEs to compensate the loss of UEs, while multi-connectivity is adopted to enhance the system throughput. We also defined offloading failure ratio as a conclusive condition for offloading size by analyzing the movement of UEs. By a two-step optimization, the co-scheduling of offloading size and throughput is solved. The system-level simulation results show that the offloading size and throughput of the proposed scheme are larger than comparisons when the time constraint is tight.

Blockchain Based Intelligent Transport System

Blockchain technology is widely studied in these days and has vital role in the ITS and Vehicular network. Intelligent Transport System (ITS) have resolved several issues of transportation like congestion, electronic toll collection, traffic light cameras, traffic updates, and environment forecasting. The vehicular network is the ever-increasing network it is not only facilitates us but also brings new challenges with it. The mobile nature of vehicular networks it is very important to collect and broadcast information of traffic events in real-time. A little delay to broadcast important information or deciding on this information can cause a serious situation in the mobile vehicular network. Moreover, malicious vehicles in the network broadcasting false information about these traffic events cause a disturbance in the network. In large-scale scenarios, the transmission of malicious messages offers a lot of danger to the system. They can wrongly claim the roads and provide false information about the incident. These traffic events can be life-threatening and cause unwanted situations like accidents, wastage of time and other resources. Therefore, it is very much important to provide real-time information on recent traffic events and real-time authentication of vehicles that broadcast information in the network. Traditional studies are unable to solve these security issues and contain a single point of failure issue. These studies are centralized and dependent on a single higher authority. Moreover, they have serious security concerns that are harmful for vehicular network. Moreover, any vehicles are unwilling to share their private information while broadcasting information about traffic events because they are strangers to each other. And if a vehicle does not want to share its private information like name, id, etc. It is not possible to authenticate this vehicle and manage trust in the network. It means that it is very crucial to prevent vehicles to broadcast wrong information in the network while preserving their privacy at the same time. Therefore, there is a need to authenticate vehicles and manage trust in the network while preserving their privacy simultaneously. Blockchain can offer better solution to solve these issues due to its secure distributed environment and features that ensure immutability about actions. The purpose of this report is to provide real-time security and privacy in the network. It is also ensured that vehicles get real-time authenticated information about traffic incidents from legitimate vehicles while simultaneously preserving their privacy. It means that only authenticated and legitimate entities (vehicles) can participate in vehicular network and privacy of both sender and receiver is secured in the network. Details of conducted experiments are given, and shreds of evidence are provided to evaluate the performance of architectures for authentication and trust management. The shreds of evidence show that these blockchain-based systems can solve security and trust issues more effectively.

Resource Allocation in D2D Enabled Vehicular Communications: A Robust Stackelberg Game Approach Based on Price-Penalty Mechanism

This paper studies how to apply game theory to realize a well-function device-to-device enabled vehicular (D2D-V) communication system, where the uplink channel allocated to the cellular user (CU) is reused by multiple D2D-V users. Considering a non-cooperative setting where the CU and D2D-V users are selfish and profit-driven, a novel Stackelberg game framework is proposed to model the single-leader-multiple-follower hierarchical competition, where the CU and D2D-V users act as the leader and the followers, respectively. For the current D2D-V networks, the interference in the dense vehicle scene often leads to extremely poor communication quality. Moreover, a vehicle's mobility leads to an uncertain channel state and further affects the stability of communication. To achieve effective communication, robust Stackelberg game-based resource allocation is developed, and a price-penalty mechanism is further proposed. Unlike previous Stackelberg games, the robust game is highlighted by handling the channel uncertainty which is embedded in the interference probability constraints. Besides, a game equilibrium (GE) is considered to be the solution and its existence and uniqueness are investigated. Also, a distributed robust power control and nonuniform price bargaining algorithm is proposed to approach the GE. Numerical simulations are performed to evaluate the algorithm performances, and the results indicate that the proposed algorithm is effective in high mobility vehicular networks under uncertain channel environments.

GCH-MV: Game-Enhanced Compensation Handover Scheme for Multipath TCP in 6G Software Defined Vehicular Networks

The latest developments in terms of self-driving technologies have increased the demand for high quality data transmission in vehicular networks. However, frequent handovers among the Road Side Units (RSUs) cause degradation of data transmission performance, especially in the context of high-speed mobility. In order to address this problem, a novel game-enhanced compensation handover scheme (GCH-MV) for multipath TCP in 6G software defined vehicular networks is proposed. An innovative system architecture which integrates 6G communication solutions, SDN and multipath techniques is presented. GCH-MV redesigns the fluid model of Multipath TCP (MPTCP) to compensate for the declining throughput by using multiple paths. GCH-MV transfers traffic quickly between different paths, adapting the transmission to path quality and maintain high throughput. In addition, an innovative game-based optimal candidate RSU selection algorithm, employed by GCH-MV during the handover process, is also introduced. GCH-MV uses these solutions to achieve its goal to mitigate the negative impact of handover as much as possible and make the handover process smooth and transparent. Experimental results show how GCH-MV outperforms the existing solutions in terms of several quality of service (QoS) metrics, addressing efficiently the data transfer quality problem during handover in highly mobile vehicular networks.

Highly Anonymous Mobility-Tolerant Location-Based Onion Routing for VANETs

Vehicular ad hoc networks (VANETs) have received considerable attention in recent years. Like any other network, privacy and anonymity are a requirement in VANETs. Classic anonymous routing protocols such as onion routing algorithm are fragile in mobile networks due to frequent link breaks. In this article, we propose a novel onion-based anonymous routing protocol for highly mobile vehicular networks. It introduces the concept of location-based dynamic relay groups. In this concept, vehicles dynamically form groups around specific locations to act as cryptographic onion relays. The proposed protocol satisfies source anonymity, destination anonymity, and route anonymity features and is very scalable. Employment of pseudo-IDs further helps in keeping the real identity of vehicles anonymous. The proposed protocol can modify the chain structure on the fly and make it longer to maintain source/destination location anonymity or cut it short to maintain performance. The extensive simulations conducted based on the SUMO traffic simulator showed that the proposed protocol significantly outperforms the naive onion routing protocol in terms of delivery ratio, delay and number of retransmissions.

Position-based emergency message dissemination for Internet of vehicles

With the advancement in communication technologies, Internet of vehicles presents a new set of opportunities to efficiently manage transportation problems using vehicle-to-vehicle communication. However, high mobility in vehicular networks causes frequent changes in network topology, which leads to network instability. This frequently results in emergency messages failing to reach the target vehicles. To overcome this problem, we propose a data dissemination scheme for such messages in vehicular networks, based on clustering and position-based broadcast techniques. The vehicles are dynamically clustered to handle the broadcast storm problem, and a position-based technique is proposed to reduce communication delays, resulting in timely dissemination of emergency messages. The simulation results show that the transmission delay, information coverage, and packet delivery ratios improved up to 14%, 9.7%, and 5.5%, respectively. These results indicate that the proposed scheme is promising as it outperforms existing techniques.

An enhanced heuristic XoR network coding-based method for high quality video streaming over VANETs.

One of the most important challenges in live video streaming in mobile vehicular networks is the optimal use of broadband and point-to-point packet delay. Recent studies show that the sheer use of frames flow compression methods (such as H.264 or HEVC) and the proper communication overlay, such as Peer-to-Peer (P2P), has no absolute influence on increasing the quality of received video in VANET networks. Therefore, the use of an appropriate data exchange method, such as network coding, seems to be of great importance. Compared to Random Network Coding (RNC), XoR Network Coding (XNC) method has the least computational load for the network which is an important factor in optimal use of limited energy of nodes in a wireless network. The basic problem in XNC is that when a node is supposed to combine several frames and transmit them through an encoded frame, how this combination could be made to enable other nodes of the network to be broadcasted through receiving this packet and how can the available packets in their buffers decode as well as extract the largest number of frames in order to experience a higher video quality. To fulfil this aim, an encoding intelligent method is required which is based on the buffers’ status of neighbours. In the proposed method in this article, the best frame combination is reached through buffers status of neighbours and AHP methods or AHP-TOPSIS methods, and the encoded frames are broadcasted through XNC. Simulation results show that due to the reduction in number of transmitted packets in the network, parameters such as congestion and point-to-point delay are significantly reduced and vehicles experience a higher video quality compared with other similar methods.

Physical Layer Security Performance of Mobile Vehicular Networks

Vehicular communication is an emergent technology with promising future, which can promote the development of mobile vehicular networks. Due to the broadcast nature of wireless channels, vehicular user mobility, and the diversity of vehicular network structures, the physical layer security issue of the mobile vehicular networks is a major concern. In this paper, the physical layer security performance of the mobile vehicular networks over N-Nakagami fading channels is investigated. Exact closed-form expressions for the probability of strictly positive secrecy capacity (SPSC), secrecy outage probability (SOP), and average secrecy capacity (ASC) are derived. Monte-Carlo simulation is used to verify the secrecy performance under different conditions. We further investigate the relationship between secrecy performance and the system parameters.

Robust Resource Allocation With Imperfect Channel Estimation in NOMA-Based Heterogeneous Vehicular Networks

In heterogeneous vehicular networks, non-orthogonal multiple access (NOMA) with effective resource allocation improves spectrum efficiency by allowing multiple users to share the same channel. However, channel estimation errors caused by high mobility in vehicular networks affect system robustness and link reliability. As a result, resource allocation in high-mobility scenarios is a challenging issue. In this paper, robust resource allocation is studied to improve both the throughput performance and reliability of NOMA-based heterogeneous vehicular networks. A cascaded Hungarian channel assignment algorithm is proposed to simplify the formulated resource allocation problem with reliability requirements into a robust power allocation problem with chance constraints. With the approximation of non-central Chi-square distribution, the chance constraints are transformed into deterministic constraints. Furthermore, the optimal power allocation for the transformed problem is obtained in consideration of the requirements in NOMA. Simulation results illustrate the effectiv- eness of the proposed robust resource allocation scheme and its improvement over existing schemes.

Toward Intelligent Vehicular Networks: A Machine Learning Framework

As wireless networks evolve toward high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies. Future intelligent vehicles, which are at the heart of high mobility networks, are increasingly equipped with multiple advanced onboard sensors and keep generating large volumes of data. Machine learning, as an effective approach to artificial intelligence, can provide a rich set of tools to exploit such data for the benefit of the networks. In this paper, we first identify the distinctive characteristics of high mobility vehicular networks and motivate the use of machine learning to address the resulting challenges. After a brief introduction of the major concepts of machine learning, we discuss its applications to learn the dynamics of vehicular networks and make informed decisions to optimize network performance. In particular, we discuss in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach. Finally, some open issues worth further investigation are highlighted.

Enabling Collaborative Edge Computing for Software Defined Vehicular Networks

Edge computing has great potential to address the challenges in mobile vehicular networks by transferring partial storage and computing functions to network edges. However, it is still a challenge to efficiently utilize heterogeneous edge computing architectures and deploy large-scale IoV systems. In this article, we focus on the collaborations among different edge computing anchors and propose a novel collaborative vehicular edge computing framework, called CVEC. Specifically, CVEC can support more scalable vehicular services and applications by both horizontal and vertical collaborations. Furthermore, we discuss the architecture, principle, mechanisms, special cases, and potential technical enablers to support the CVEC. Finally, we present some research challenges as well as future research directions.