Internet Of Vehicles Network Research Articles

Adapting the H.264 Standard to the Internet of Vehicles

We suggest two steps of reducing the amount of data transmitted on Internet of Vehicle networks. The first step shifts the image from a full-color resolution to only an 8-color resolution. The reduction of the color numbers is noticeable; however, the 8-color images are enough for the requirements of common vehicles’ applications. The second step suggests modifying the quantization tables employed by H.264 to different tables that will be more suitable to an image with only 8 colors. The first step usually reduces the size of the image by more than 30%, and when continuing and performing the second step, the size of the image decreases by more than 40%. That is to say, the combination of the two steps can provide a significant reduction in the amount of data required to be transferred on vehicular networks.

Toward a Distributed Trust Management System for Misbehavior Detection in the Internet of Vehicles

Recent considerable state-of-the-art advancements within the automotive sector, coupled with an evolution of the promising paradigms of vehicle-to-everything communication and the Internet of Vehicles (IoV), have facilitated vehicles to generate and, accordingly, disseminate an enormous amount of safety-critical and non-safety infotainment data in a bid to guarantee a highly safe, convenient, and congestion-aware road transport. These dynamic networks require intelligent security measures to ensure that the malicious messages, along with the vehicles that disseminate them, are identified and subsequently eliminated in a timely manner so that they are not in a position to harm other vehicles. Failing to do so could jeopardize the entire network, leading to fatalities and injuries amongst road users. Several researchers, over the years, have envisaged conventional cryptographic-based solutions employing certificates and the public key infrastructure for enhancing the security of vehicular networks. Nevertheless, cryptographic-based solutions are not optimum for an IoV network primarily, since the cryptographic schemes could be susceptible to compromised trust authorities and insider attacks that are highly deceptive in nature and cannot be noticed immediately and are, therefore, capable of causing catastrophic damage. Accordingly, in this article, a distributed trust management system has been proposed that ascertains the trust of all the reputation segments within an IoV network. The envisaged system takes into consideration the salient characteristics of familiarity, i.e., assessed via a subjective logic approach, similarity, and timeliness to ascertain the weights of all the reputation segments. Furthermore, an intelligent trust threshold mechanism has been developed for the identification and eviction of the misbehaving vehicles. The experimental results suggest the advantages of our proposed IoV-based trust management system in terms of optimizing the misbehavior detection and its resilience to various sorts of attacks.

Lightweight Reputation Management for Multi-Role Internet of Vehicles

With the rapid development of the Internet of Vehicles (IoV), smart vehicles can fulfill multiple roles in either the information-centric IoV or the task-oriented IoV. However, malicious vehicles may undermine the trustiness of vehicles towards each other, and further damage these IoV networks. Given the multiple roles undertaken by vehicles in IoV networks, we aim to design a lightweight reputation-based mechanism for a hybrid IoV network in this article. This mechanism can realize real-time reputation updates and synchronization in the device-edge-cloud continuum. Simulation is conducted to validate the reputation management strategy in this article. We also discuss some opportunities and challenges to shed a light on the future research directions in this topic.

On Attack-Resilient Service Placement and Availability in Edge-Enabled IoV Networks

Achieving network resilience in terms of attack tolerance and service availability is critically important for Internet of Vehicles (IoV) networks where vehicles require assistance in sensitive and safety-critical applications like driving. It is particularly challenging in time-varying conditions of IoV traffic. In this paper, we study an attack-resilient optimal service placement problem to ensure disruption-free service availability to the users in edge-enabled IoV network. Our work aims to improve the user experience while minimizing the delay and simultaneously considering efficient utilization of limited edge resources. First, an optimal service placement is performed while considering traffic dynamicity and meeting the service requirements with the use of a deep reinforcement learning (DRL) framework. Next, an optimal secondary mapping and service recovery placements are performed to account for the attacks/failures at the edge. The use of DRL framework helps to adapt to dynamically varying IoV traffic and service demands. In this work, we develop three ILP models and use them in the DRL-based framework to provide attack-resilient service placement and ensure service availability with efficient network performance. Extensive numerical experiments are performed to demonstrate the effectiveness of the proposed approach.

Authenticating Drone-Assisted Internet of Vehicles Using Elliptic Curve Cryptography and Blockchain

The inclusion of drones in Internet of Vehicles (IoV) is a current trend that presents significant trade-offs. On the one hand, Unmanned Aerial Vehicles (UAVs) provide advantages such as enabling ground communications also when physical obstacles limit the connectivity. On the other hand, they increase the attack surface. For instance, physical attacks on drones provide the attacker with credentials that can be used to inject bogus information into the IoV network, thus jeopardizing not only security but also users’ safety. In this scenario, authentication plays a fundamental role to guarantee security. It is however fundamental to develop authentication protocols that can, at the same time, protect ground users’ data and prevent attacks to drones. However, currently available authentication schemes cannot guarantee security in case of attacks to drones. In this paper, we propose a Blockchain-supported authentication protocol for Drone-assisted IoV using Elliptic curve cryptography (BDIVE). Compared to existing authentication protocols, we extend the threat model from an honest-butcurious drone to active attacks against drones. BDIVE provides both energy-efficiency, traceability, and accountability thanks to the use of blockchain at the Trusted Authority (TA). Using Burrow-Abadi-Needham (BAN) logic, we analyze and prove the security of mutual authentication in BDIVE. We also prove the security of BDIVE against several attacks by implementing it in AVISPA. To assess its scalability and energy efficiency, we implement BDIVE using Omnetpp with its Castalia simulator. The comparison of BDIVE with currently existing authentication protocols, shows that it reduces the energy consumption up to 70% and the computational cost up to 68%, while providing resistance to previously unconsidered attack vectors.

Delay-Aware Edge-Terminal Collaboration in Green Internet of Vehicles: A Multiagent Soft Actor-Critic Approach

The high performance requirement of task processing and low energy consumption requirement of network operation result in a contradiction for the Internet of Vehicles (IoV). This paper addresses the contradiction and proposes an efficient edge-terminal collaboration scheme to sustain the diverse task requirements for a green IoV network. Specifically, two types of tasks are first modeled in terms of data size and delay requirements and then an energy minimization problem was formulated under the diverse task requirements. Thereafter, a multi-agent reinforcement learning framework is constructed, based on which the time-varying environment of the IoV can be modeled as a partially observable Markov decision process. Furthermore, a multi-agent soft actor-critic (MASAC) scheme is proposed to train the edge-terminal collaboration policy in terms of task offloading, spectrum sharing as well as vehicle power control. Finally, the effectiveness of the proposed scheme is validated by comparing with other baseline schemes through extensive simulation experiments.

A lane-based advanced forwarding protocol for internet of vehicles

PurposeThe internet of vehicles (IoV) communication has recently become a popular research topic in the automotive industry. The growth in the automotive sector has resulted in significant standards and guidelines that have engaged various researchers and companies. In IoV, routing protocols play a significant role in enhancing communication safety for the transportation system. The high mobility of nodes in IoV and inconsistent network coverage in different areas make routing challenging. This paper aims to provide a lane-based advanced forwarding protocol for internet of vehicles (LAFP-IoV) for efficient data distribution in IoV. The proposed protocol’s main feature is that it can identify the destination zone by using position coordinates and broadcasting the packets toward the direction of destination. The novel suppression technique is used in the broadcast method to reduce the network routing overhead.Design/methodology/approachThe proposed protocol considers the interferences between different road segments, and a novel lane-based forwarding model is presented. The greedy forwarding notion, the broadcasting mechanism, and the suppression approach are used in this protocol to reduce the overhead generated by standard beacon forwarding procedures. The SUMO tool and NS-2 simulator are used for the vehicle's movement pattern and to simulate LAFP-IoV.FindingsThe simulation results show that the proposed LAFP-IoV protocol performs better than its peer protocols. It uses a greedy method for forwarding data packets and a carry-and-forward strategy to recover from the local maximum stage. This protocol's low latency and good PDR make it ideal for congested networks.Originality/valueThe proposed paper provides a unique lane-based forwarding for IoV. The proposed work achieves a higher delivery ratio than its peer protocols. The proposed protocol considers the lanes while forwarding the data packets applicable to the highly dense scenarios.

A Novel MAC-Based Authentication Scheme (NoMAS) for Internet of Vehicles (IoV)

The fully dynamic dense environment of the Internet of Vehicles (IoV) and conditions like high traffic jams, collisions, and uneven road conditions increase the communication messages. Consequently, the computation and communication costs of the IoV network are increased. Therefore, IoV needs a lightweight and efficient security solution along with effective communications. Existing state-of-the-art schemes in IoV are generally based on cryptographic methods that use session keys, public keys, elliptic curves, and Message Authentication Codes (MAC). In contrast to the other schemes, MAC-based schemes provide feasible security solutions having high performance with less overhead. The existing MAC-based authentication schemes are vulnerable to the key disclosure issue along with some security attacks such as side-channel attacks, Distributed Denial of Services (DDoS), etc. Due to the side channel attack, a data leakage problem occurs, which is a serious concern in security and privacy facets. We propose a novel MAC-based authentication scheme (NoMAS) that mitigates the above-discussed challenges and provides all the benefits of MAC-based schemes. The NoMAS scheme offers a solution for key disclosure issues by providing Hard Key and Soft Key Updates (HKU and SKU) and data leakage problems using encryption along with high performance. It reduces the computational overhead maximum to 99.60% and the communication overhead maximum to approximately 81% compared to the existing schemes. In the security analysis, We have provided the formal security proof using BAN logic and the ProVerif tool and verified the correctness of the scheme.

Blockchain‐based internet of vehicles (BIoV): A systematic review of surveys and reviews

AbstractThe Internet of vehicles (IoV) has appeared as an effective method of obtaining an intelligent transportation system able to deliver various innovative solutions and enable several applications as a replacement for vehicular ad‐hoc networks (VANETs). To help IoV contexts, enormous quantities of information are created and transmitted between various communication components wirelessly across multiple channels, which may entice attackers and put the network at risk. Security is one of the vital concerns and critical issues in VANETs and IoV networks. Blockchain is employed to create a distributed and secure IoV to overcome some centralized concerns and enhance the network architecture. This article presents a systematic and detailed review by selecting 28 review articles from 2018 to 2022 on blockchain‐based IoV (BIoV). We investigate the latest review and survey articles regarding their aspects, contributions, findings, limitations, and strong points to address unsolved problems of BIoV. In this article, the review/survey articles are systematically reviewed to resolve taxonomy aspects intended for BIoV. We provide a new taxonomy and discuss security concerns, open issues, and future directions so that researchers in this field can quickly and easily access recent content and do not need to read numerous review articles.

Fractional mayfly optimization algorithm‐based Infrastructure‐to‐Vehicle and Vehicle‐to‐Vehicle scheduling for service message transmission in IoV‐fog

SummaryFor the rising number of vehicles, and the advancement of communication and computation technologies, the perception of Internet of Vehicles (IoV) is introduced. It is utilized to capture vehicle‐based information that includes road description, road congestion, vehicle speed, and location. However, this information is important, and it showed more benefits in different ways, like route selection and message dissemination. IoV is the self‐structured network composed with vehicles lies in the road and the road side units (RSUs). It offers Infrastructure‐to‐Vehicle (I2V), as well as Vehicle‐to‐Vehicle (V2V) data transmission mechanism for transmitting service messages. To reliably broadcast the service information to the intended recipient in the IoV network still faced issues. Hence, an efficient service message transmission protocol is developed using the proposed fractional mayfly optimization algorithm (FMA) for selecting the relay vehicle and cooperative vehicle for transmitting service messages to the destination vehicle from RSU through the process of I2V and V2V scheduling. The RSU selects the relay vehicle for every service message using the proposed algorithm and allocates the cooperative vehicle by RSU for scheduling V2V transmission. The simulation results showed that the proposed scheduling method obtains the best channel quality indicator (CQI), delay, distance, packet delivery ratio (PDR), and throughput value of 0.92 for 150 vehicles and 0.005891, 6.060731, 83.45%, and 171.50 Mbps for 100 vehicles.

Feasibility Analysis of Data Transmission in Partially Damaged IoT Networks of Vehicles

Nowadays, vehicle-oriented Internet of Things (IoT) is a new generation of IoT networks in which sensors are deployed on electronic hardware modules of vehicles. A secure and feasible IoT-assisted vehicle environment should include a robust data transmission mechanism for transferring and collecting data packets from both onboard and roadside sensors, resulting in the accurate delivery of packages without delay. When designing such Internet of Vehicles (IoV) networks, the vulnerability of the network should be considered to facilitate data transmission in the remaining network under the condition that some nodes (e.g., vehicles) and channels are damaged due to the dynamic environmental factors and unpredicted failures at various nodes. Fractional Critical Deleted Graph (FCDG), which is used in graph theory, can act as Fractional Factor (FF) in the IoV networks to maintain the IoT network stable and provide reliable network connectivity when a part of data transmission network is damaged. Toughness is an important condition to measure the sturdiness of such FF-encoded network. In this work, we study the relationship between toughness and FCDG in IoV networks. Moreover, the graph conditions are considered together with the tight lower bound of the toughness for the existence of path factor. Such feasibility analysis of IoV networks help to find the bound in the effort to recover or realign lost links in networks, which is critical for the next generation of intelligent transportation systems where all vehicles are connected seamlessly.

Task offloading based on edge collaboration in MEC-enabled IoV networks

Benefiting from its abundant computing resources and low computing latency, mobile edge computing (MEC) is a promising approach for enhancing the computing capacity of the 5G Internet of vehicles (IoV). Because of the high mobility, handover is frequent and inevitable in IoV networks. In this paper, we investigate an edge collaborative task offloading and splitting strategy in MEC-enabled IoV networks, in which the task is splitted on the edge and paralleling executed by each part of the task on several MEC servers when handover is occured. Applications in IoV networks have flexible requirements on latency and energy consumption. To realize the tradeoff between latency and energy consumption, we formulate the task offloading and splitting as an optimization problem with the aim of minimizing the total cost of latency and energy consumption by jointly optimizing the task splitting ratio and uplink transmit power of vehicle terminal (VT). Because the proposed problem is non-smooth and non-convex, we divide the original problem into two convex subproblems, and apply an alternate convex search (ACS) algorithm to obtain the optimized solution with low computational complexity. Numerical simulation results show that the proposed method can adjust the offloading strategy properly according to task preference, and obtain a lower total cost compared with the baseline algorithms.

Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles

Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by leveraging smart vehicles (SVs) to participate in the learning process with minimum data exchanges and privacy disclosure. The collected data and learned knowledge can help the vehicular service provider (VSP) improve the global model accuracy, e.g., for road safety as well as better profits for both VSP and participating SVs. Nonetheless, there exist major challenges when implementing the FL in IoV networks, such as dynamic activities and diverse quality-of-information (QoI) from a large number of SVs, VSP's limited payment budget, and profit competition among SVs. In this paper, we propose a novel dynamic FL-based economic framework for an IoV network to address these challenges. Specifically, the VSP first implements an SV selection method to determine a set of the best SVs for the FL process according to the significance of their current locations and information history at each learning round. Then, each selected SV can collect on-road information and propose a payment contract to the VSP based on its collected QoI. For that, we develop a multi-principal one-agent contract-based policy to maximize the profits of the VSP and learning SVs under the VSP's limited payment budget and asymmetric information between the VSP and SVs. Through experimental results using real-world on-road datasets, we show that our framework can converge 57% faster (even with only 10% of active SVs in the network) and obtain much higher social welfare of the network (up to 27.2 times) compared with those of other baseline FL methods.

Artificial intelligence support for 5G/6G-enabled Internet of Vehicles networks: An overview

Improving transportation efficiency and on-road safety using Intelligent Transportation Systems (ITSs) has become crucial as road congestion and vehicle complexity increase coupled with ongoing rapid development and deployment of electric vehicles across the globe. Recent advances in computer systems and wireless communications have ushered in more possibilities for smart solutions to road traffic safety, congestion reduction, convenience, and overall efficiency. The evolution and deployment of 5G have opened up new technologies and features that can provide the much needed high-mobility wireless networks for the emerging Internet of Vehicles (IoV). The application of AI consisting of Deep Learning (DL), Machine Learning (ML) and Swarm Intelligence (SI) techniques have emerged in both conventional and vehicular wireless networks with strong promises towards enhancing traditional data-centric methods. Particularly, in the application domains of IoV, big data is frequently generated from various sources within the vehicular communication environment. The collected big data is usually processed and used for both safety and infotainment services including routing, broadening drivers' awareness, traffic mobility prediction for hazardous situation avoidance to improve overall safety and passenger comfort, and general quality of road experience. Applying data-driven methods enables AI to address high mobility and dynamic vehicular communications and network issues facing traditional solutions and approaches like network optimization techniques and conventional control loop design. This study provides a concise review of DL, ML and SI techniques and applications that are currently being explored by different research efforts within the application area of vehicular networks. The paper further discusses the strengths and weaknesses of the proposed AI-based solutions for the IoV networks.

Power and Element Allocation Design for RIS–NOMA IoV Networks

In this paper, a cooperative, non-orthogonal multiple access (NOMA)-based internet of vehicles (IoV) network is proposed, which is assisted by a reconfigurable intelligent surface (RIS) to enhance signal transmission. Due to the intelligent control of RISs, the channel condition of our proposed IoV system is more diversified compared to conventional NOMA–RIS schemes, and thus two power allocation schemes are considered to tackle this issue, providing better channel gain to transmit signals. In this proposed scheme, power allocation schemes are discussed for a diverse number of reflecting elements, and a number of elements are obtained serving users near or far. To further improve system capacity and guarantee quality of service (QoS), we derive the number of elements to guarantee the same transmission data sum-rate under different channel qualities. Numerical and simulation results verify the correctness as well as superiority of our proposed scheme.

Blockchain and trust-based clustering scheme for the IoV

The advent of the Internet of Vehicles (IoV) leads to the proliferation of services proposed for the users. Its adoption aims mainly to make driving more secure and enhance the users’ quality of experience. However, the deployment of multiple technologies that interact with each other brings out big security challenges. Indeed, it enlarges the attack vectors and surface. In the IoV ecosystem, cybersecurity issues can be driven from the security of communication links, security of devices, identity and liability, access control as well as privacy of drivers and vehicles. Trust management is one of the key mechanisms that can enforce the security of an IoV network. In a previous work (Hbaieb et al., 2021), we proposed a blockchain based trust management framework to enhance IoV security. Our framework showed good performance results. However, it was based on a centralized trust based approach. Thus, its performance is decreasing when dealing with effective scalability. Moreover, we did not focus on the energy consumption of the proposed model. In this paper, we propose an extension to enhance the highlighted limitations. For that, we are based on a decentralized trust process. We, also, introduce a clustering mechanism to construct our framework. Moreover, to take into account the quality of service (QoS) of our framework while enforcing security, we define our clustering process based on different parameters such as the trust value, the safety distance, and the energy factor. The evaluation of our proposal shows relevant results in terms of detection performance, communication overhead and runtime properties. The simulation results indicate that the formation of trustworthy clusters using blockchain reinforces the IoV reliability.

A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles

The Internet of Things (IoT) has risen from ubiquitous computing to the Internet itself. Internet of vehicles (IoV) is the next emerging trend in IoT. We can build intelligent transportation systems (ITS) using IoV. However, overheads are imposed on IoV network due to a massive quantity of information being transferred from the devices connected in IoV. One such overhead is the network connection between the units of an IoV. To make an efficient ITS using IoV, optimization of network connectivity is required. A survey on network optimization in IoT and IoV is presented in this study. It also highlights the backdrop of IoT and IoV. This includes the applications, such as ITS with comparison to different advancements, optimization of the network, IoT discussions, along with categorization of algorithms. Some of the simulation tools are also explained which will help the research community to use those tools for pursuing research in IoV.

Ensemble Learning-Based Edge Caching Strategies for Internet of Vehicles: Outage and Finite SNR Analysis

In this paper, an ensemble learning-driven edge caching (ELDEC) strategy and a meta-based ensemble learning-driven edge caching (MELDEC) strategy are proposed for content popularity prediction and cache content placement in Internet-of-Vehicles (IoV) networks. Specifically, the proposed MELDEC and ELDEC strategies incorporate meta learning and ensemble learning for enhanced content popularity prediction in IoV networks. Closed-form outage probability and finite signal-to-noise ratio (SNR) diversity gain expressions are also derived to establish the relationship between the proposed edge caching strategies and the wireless performance of IoV networks. When compared against benchmark schemes, the proposed MELDEC and ELDEC strategies achieve near-optimal cache hit rates, outage probability, and finite SNR diversity gain under imperfect channel state information (CSI) estimation. We also show that the outage probability decay rate in the IoV network depends on the number of base stations and roadside units, and it is independent of the content popularity prediction of the MELDEC strategy, ELDEC strategy, and benchmark schemes. The performance analysis demonstrates that the proposed MELDEC and ELDEC strategies are promising solutions towards achieving reliable content access in IoV networks.

AI-based Intrusion Detection for Intelligence Internet of Vehicles

With the development of intelligent technologies, Internet of Things (IoT) opens up a new era in the field of automotive networks, namely, Internet of Vehicles (IoV). The main goal of IoV is to provide a secure and reliable network to vehicles so that users can enjoy various services. However, vulnerabilities and incomplete protection mechanisms have led to a proliferation of security threats against IoV networks. Intrusion detection technology is an effective protection solution for IoV security, especially when artificial intelligence (AI) technology has been introduced into intrusion detection study. This article first briefly introduces the concept and features of IoV, and then reviews the related research on AI-based IoV intrusion detection systems. Finally, we discuss the open challenges and future research directions.

Adaptive Congestion Control Mechanism to Enhance TCP Performance in Cooperative IoV

One of the main causes of energy consumption in Internet of Vehicles (IoV) networks is an ill-designed network congestion control protocol, which results in numerous packet drops, lower throughput, and increased packet retransmissions. In IoV network, the objective to increase network throughput can be achieved by minimizing packets re- transmission and optimizing bandwidth utilization. It has been observed that the congestion control mechanism (i.e., the congestion window) can plays a vital role in mitigating the aforementioned challenges. Thus, this paper present a cross-layer technique to controlling congestion in an IoV network based on throughput and buffer use. In the proposed approach, the receiver appends two bits in the acknowledgment (ACK) packet that describes the status of the buffer space and link utilization. The sender then uses this information to monitor congestion and limit the transmission of packets from the sender. The proposed model has been experimented extensively and the results demonstrate a significantly higher network performance percentage in terms of buffer utilization, link utilization, throughput, and packet loss.