Internet Of Vehicles Applications Research Articles

SMITS: Social and Mobility aware Intelligent Task Scheduling in Vehicular Fog Computing — A Federated DRL Approach

Vehicular fog computing (VFC) has become an enticing research hot spot to provide resources for the Internet of Vehicles (IoV) application requests. Moreover, with the massive expansion of services in vehicular fog networks such as augmented reality, 3-D gaming, and autonomous driving, the allocation of fog computing resources to IoV applications constraining the deadline and energy consumption of the vehicles is becoming challenging. In this paper, we investigate a task scheduling problem in vehicular fog networks to jointly optimize the success rate of vehicular tasks and energy consumption of vehicles by considering the moving paths of the vehicles and the social relationships among the vehicles. In this problem, we model the social relationship among the vehicles based on their communication patterns and social characteristics to improve the success rate of the tasks. We also incorporate the mobility of vehicles using the Markov renewal process (MRP) technique. Initially, we formulate a mixed integer nonlinear programming (MINLP) problem for the proposed problem and further prove it to be an NP-hard problem. To solve the proposed problem, we design a federated deep reinforcement learning (FDRL) mechanism by converting the problem into a Markov decision process (MDP) problem. We compare our proposed algorithm with existing scheduling approaches, and simulation results show that the proposed algorithm performs efficiently for task scheduling problems. The proposed algorithm achieved a substantial improvement of 12% in the maximization of the success rate and 36% in the minimization of the energy consumption compared to the existing algorithms.

Big data challenges and opportunities in Internet of Vehicles: a systematic review

PurposeBig data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.Design/methodology/approachThis review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.FindingsThis paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.Research limitations/implicationsThis paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.Originality/valueThis paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

Performance assessment of full-duplex two-way Internet-of-vehicles relay networks under practical imperfect conditions

Contemporary Internet-of-Things (IoT) devices in general and Internet-of-Vehicles (IoV) networks in specific are taking the benefits from using full-duplex (FD) communication with the help of relay nodes to facilitate data transmission. However, self-interference between transmit and receive antennas at the relay node may cause big trouble if self-interference cancellation (SIC) is not applied. In this paper, a rigorous analysis on the performance of FD two-way relay networks (FDTWRNs) for IoV applications under imperfect conditions is provided. Different from earlier studies on the performance of FDTWRNs where one or two imperfect factors were considered, this work simultaneously takes into account many practical issues in the implementation of the IoV networks at the physical layer, including errors in channel state information at all channel links, imperfect SIC and transceiver hardware impairment at all nodes. For relaying protocol, we consider amplify-and-forward with both fixed-gain (FG) and variable-gain (VG) protocols. The exact closed-form expressions (in terms of the system parameters) of key performance factors such as signal-to-interference-plus-noise-distortion-and-error ratio (SINER), outage probability (OP), achievable data rate (ADR), and average symbol error probabilities (ASEP) of the considered system over Rayleigh fading channels and under the combined effect of imperfect CSI, SIC, and transceiver hardware are derived mathematically using probabilistic model analysis. The correctness of the analysis is then verified by Monte Carlo simulations. Drawing upon both analytical and numerical findings, the efficacy of the FDTWRNs under consideration is meticulously and comprehensively assessed in comparison to its performance in ideal conditions characterized by one or multiple critical factors. The disparity between perfect and imperfect scenarios serves as a valuable metric for appraising the practical viability of the considered FDTWRNs when deployed in practical IoV networks.

Reliable Computation Offloading of DAG Applications in Internet of Vehicles Based on Deep Reinforcement Learning

Reliable Computation Offloading of DAG Applications in Internet of Vehicles Based on Deep Reinforcement Learning

Simulation based QoS aware dynamic caching scheme for heterogeneous content requests in vehicular edge computing

The Internet of Vehicles (IoV) has witnessed a substantial surge in its expansion during recent years, with the integration of edge servers emerging as an increasingly prevalent practice to cater to content requests that demand a superior quality of service. Nonetheless, conventional cache policies often prove inadequate for IoV applications, primarily due to their incapacity to effectively handle a diverse array of content requests, the high-speed mobility of vehicles, and the inherent instability of network connections. Within the scope of this study, we commence by bifurcating various content requests into two distinct categories: latency-sensitive content request and bandwidth-sensitive content request. Subsequently, we establish a model to evaluate the service quality under the joint consideration of different types of content requests, the mobility of vehicles and storage capacity of edge nodes. Furthermore, we transform the quality-of-service (QoS) penalty function into a system reward function. This adaption enables us to propose an innovative edge cache scheme founded upon the Deep Deterministic Policy Gradient (DDPG) algorithm of Reinforcement Learning (RL) method, which empowers dynamic adjustments in response to the evolving IoV environment. To validate the effectiveness of our proposed approach, we harness the Simulation of Urban Mobility (SUMO) traffic simulation software and construct a traffic road scenario based on a specific part segment of Nanjing Beltway. A wide-ranging set of contrast experiments was performed to ensure the improved performance of the DDPG based deep reinforcement learning method. Simulation experimental results show that the proposed algorithm converges quickly and outperforms existing algorithms in terms of service quality-hit ratio for latency-sensitive content and transmission speed for bandwidth-sensitive content.

Dynamic Edge Computation Offloading for Internet of Vehicles With Deep Reinforcement Learning

Recent developments in the Internet of Vehicles (IoV) enabled the myriad emergence of a plethora of data-intensive and latency-sensitive vehicular applications, posing significant difficulties to traditional cloud computing. Vehicular edge computing (VEC), as an emerging paradigm, enables the vehicles to utilize the resources of the edge servers to reduce the data transfer burden and computing stress. Although the utilization of VEC is a favourable support for IoV applications, vehicle mobility and other factors further complicate the challenge of designing and implementing such systems, leading to incremental delay and energy consumption. In recent times, there have been attempts to integrate deep reinforcement learning (DRL) approaches with IoV-based systems, to facilitate real-time decision-making and prediction. We demonstrate the potential of such an approach in this paper. Specifically, the dynamic computation offloading problem is constructed as a Markov decision process (MDP). Then, the twin delayed deep deterministic policy gradient (TD3) algorithm is utilized to achieve the optimal offloading strategy. Finally, findings from the simulation demonstrate the potential of our proposed approach.

Multi-Agent Caching Strategy for Spatial-Temporal Popularity in IoV

Motivated by connected vehicles, the internet of vehicles (IoV) has a prosperous development, thus a variety of IoV applications have emerged, which causes the dramatic growth of data traffic and content request, thus affecting the quality of service (QoS) of IoV. Edge caching can effectively improve the QoS by caching the contents in the road side unit (RSU) near the requester. Moreover, most existing IoV caching strategies only consider that the content popularity has temporal correlation or assume that the content popularity is given, which will impact the accuracy of content popularity. In this paper, we propose a spatial-temporal correlation approach to predict the content popularity, which can improve the accuracy of the content popularity by the spatial-temporal correlation analyzing of the RSU historical content requests. Then, we introduce a multi-agent reinforcement learning (MARL) caching strategy based on the result of prediction, where each RSU agent individually chooses the action with the constraint of caching resources and updates the action to maximize the cumulative reward. Simulation results demonstrate that the proposed caching strategy has better performance. Take the Zipf parameter is 0.8 as an example, compared with the probability caching scheme (PCS), the proposed caching strategy improves the caching hit ratio by 15% and reduces the delay by 34%.

Dynamic robust analysis of IoV link delay in cellular Telematics and smart edge networking base on deep reinforcement learning

Applications for the Internet of Vehicles (IoV) are progressing from more fundamental ones like those for traffic efficiency, road safety, and information services to more sophisticated ones like autonomous driving and intelligent mobility that have a variety of connectivity requirements. Therefore, IoV communication systems must overcome various obstacles in order to enable a variety of IoV applications. Attacks on TSP servers are the major target of telematics attack threat events. This article analyses data security monitoring from the vehicle end to the TSP platform, as well as gathering and analysing the status and intelligence of linked vehicles, in order to develop a Telematics TSP server attack monitoring system. Experiments show that the error detection rate of the benchmark algorithms decreases significantly as the error rate of vehicle speed data increases, with the exponential smoothing algorithm showing the largest decrease in the error detection rate. However, the proposed combined model is very close to the error detection rate of the benchmark algorithm at the later stages when the error rate is high, with an error detection rate of more than 80%.It provides great assistance for anomaly detection and data recovery, especially in researching key technologies for remote information processing data security and reliability, greatly improving their performance.

World State Attack to Blockchain Based IoV and Efficient Protection With Hybrid RSUs Architecture

Blockchain technology is developing rapidly and has been widely applied in the field of Internet of Vehicles (IoV) to solve trust and security problems. However, due to the high security requirements in IoV scenarios, the security threats of blockchain itself become a big challenge for its applications in IoV. As the largest distributed platform supporting smart contract, Ethereum becomes one of the popular blockchain platforms that has been applied in IoV applications. In Ethereum, the local world state (stored on Road Side Units (RSUs) in IoV) is applied to facilitate account query and transaction verification. However, previous works showed that the local database can be easily tampered, so attackers may issue invalid transactions based on the modified world state, which is not acceptable for IoV applications. In this paper, the success probability and expected time for such an attack are first analyzed theoretically, including the effect of portion of tampered RSUs and the number of required confirmation blocks. Then experiment evaluation verifies the correctness of the theoretical analysis and shows that the attack would succeed with a higher probability within a shorter time when the local database on more RSUs are attacked. On the contrary, increasing of confirmation blocks can effectively reduce the success probability of a single attack and extend the confirmation time of the invalid transaction. Finally, efficient attack detection and recovery methods are proposed based on a novel hierarchical architecture with hybrid RSUs, and the effectiveness and complexity are verified by theoretical analysis and experiments.

Secure Internet of Vehicles (IoV) With Decentralized Consensus Blockchain Mechanism

The Internet of vehicles (IoV) is increasingly being used to realize the vision of intelligent transportation systems with the rapid development of computation and communication technologies. However, numerous IoV applications rely on a central unit for storing and processing information and mediators for wireless transmission. This can lead to the leakage of sensitive data and high costs and delays. To address these issues and improve the efficiency of data storage, processing, and sharing in the IoV, we propose a vehicle-based secure blockchain consensus (VBSBC) algorithm. Our VBSBC algorithm overcomes the limitations and drawbacks of state-of-the-art approaches by leveraging blockchain technology and a consensus algorithm to ensure secure communication between vehicles. In addition, the algorithm includes an authentication process and a key distributing and request process, illustrated during vehicles' movement between different zones. In simulation results, our proposed VBSBC algorithm demonstrated high performance compared to existing approaches in terms of authentication delay, key processing time, attack detection rate, throughput, and packet loss rate.

A Blockchain-Based Data Authentication Algorithm for Secure Information Sharing in Internet of Vehicles

Secure communication between connected electric vehicles is critical for realizing the full potential of the Internet of Vehicles. However, the authentication and security of the information shared between vehicles remains a major challenge. In this work, we propose a blockchain-based data authentication algorithm to enable secure information sharing between electric vehicles. Our algorithm leverages the distributed ledger and consensus mechanism of blockchain technology to overcome limitations of traditional public key infrastructure schemes for large-scale vehicle networks. Each electric vehicle has a unique key pair and address on the blockchain network. Vehicles generate digital signatures using their private keys to share data, while recipients verify the signatures using corresponding public keys for authentication. Experimental results demonstrate that the proposed algorithm achieves high authentication success rates with acceptable latency and computation overhead. The algorithm provides benefits like decentralization, transparency and non-repudiation compared to existing approaches. Our work indicates the potential of blockchain to enhance security, trust and cooperation in Internet of Vehicles applications.

Simulation-based joint user assignment and edge resource allocation optimization for hybrid tasks in vehicular edge computing

With the swift advancement of 5G communication technology and the widespread prevalence of intelligent connected vehicles, a multitude of applications for the Internet of Vehicles (IoV) have been steadily burgeoning. Nevertheless, owing to vehicular mobility and the variety of tasks generated by IoV applications competing for restricted edge resources, the task of assigning users to edge nodes, in addition to appropriating edge node resources to hybrid tasks within edge environments, persists as a formidable challenge. In addressing this predicament, we establish a model delineating the execution duration of hybrid tasks engendered by IoV applications, and introduce a joint mechanism for user assignment and edge resource allocation. Within this mechanism, we architect an optimal strategy for edge resource allocation, predicated upon given user assignment outcomes. In order to ascertain the user assignment strategy, we employ an enhanced genetic algorithm in scenarios of a large user base, and utilize the branch-and-bound algorithm when dealing with a small user base. Finally, we design and implement an experimental setup for joint user assignment and edge resource allocation. The comprehensive simulation results provide robust verification of the effectiveness of our proposed mechanism.

Hybrid Beamforming in mmWave Massive MIMO for IoV With Dual-Functional Radar Communication

The dual-functional radar communication (DFRC) has been regarded as one of the most attractive solutions for facilitating vehicle applications in internet of vehicles (IoV) by utilizing millimeter Wave (mmWave) and massive multiple-in-multiple-out (MIMO) technologies. In this paper, we propose a hybrid beamforming (HBF) scheme with low power consumption and fully directional beamforming gains. The proposed HBF scheme integrates digital beamforming (DBF) with analog beamforming (ABF) to adopt a fully connected (F-C) structure to realize high spectral efficiency (SE) and accurate target sensing. Specifically, for HBF in a single-user MIMO (SU-MIMO) system, we design a fast Riemannian manifold optimization (FRMO) method by minimizing the weighted summation of the communication beamforming error and radar beamforming error. The proposed method can achieve optimal DBF for downlink communication and guarantee the desired beampattern for radar sensing. For HBF in a multiuser MIMO (MU-MIMO) system, we formulate the weighted optimization problem to mitigate multiuser interference (MUI). We design a low-complexity method, namely, the adaptive particle swarm optimization (APSO) algorithm, to obtain a near-optimal solution for HBF based on the criteria of minimizing MUI and the radar beamforming error. We conduct excessive simulations to evaluate the proposed algorithms for the HBF design in both the SU-MIMO system and MU-MIMO system. The numerical results demonstrate that the proposed algorithms achieve near-optimal performance compared to existing HFB methods with high computational efficiency in terms of beamforming gains and SE.

A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning

In the non-intrusive automated testing system for Internet of Vehicles (IoV) applications, automatic recognition of text and icons on vehicle central control screens is of paramount importance. However, the detection and recognition of content on vehicle central control screens are inherently complex. Additionally, during non-intrusive vehicle central control screen image testing, there is a deficiency of suitable datasets and detection methods. This deficiency renders information within vehicle application images difficult to be accurately extracted by the detection network. To address this problem, this study first constructs a dataset tailored for text detection and recognition on vehicle screens. This dataset encompasses a variety of vehicle central control images, enabling the generic text detection and recognition network to more effectively identify and interpret text within vehicle screens. Subsequently, this research proposes an enhanced Fully Convolutional Networks for Text Detection (FOTS) method for vehicle central control screen text detection and recognition. This method elevates the semantic expression capabilities of features by sharing vehicle central control screen text detection and recognition features. Furthermore, it improves multi-scale feature processing capabilities through the utilization of a feature transformation module. Validation through visual and quantitative experiments demonstrates that the proposed method can effectively accomplish text detection and recognition tasks on vehicle screens. This achievement bears significant implications for the field of automated testing in IoV applications.

Blockchain-Enabled Communication Framework for Secure and Trustworthy Internet of Vehicles

The emerging field of the Internet of Vehicles (IoV) has garnered significant attention due to its potential to revolutionize transportation and mobility. IoV enables the development of innovative services and applications that can enhance the efficiency, safety, and sustainability of transportation systems. However, ensuring secure and reliable communication among different components of an IoV system poses a critical challenge. This study proposes a blockchain-based communication framework for secure and trustworthy IoV applications. The framework utilizes blockchain technology’s decentralization and security features to create secure communication channels between IoV system components, including vehicles, infrastructure, and service providers. An identity management system is also integrated into the framework to authenticate and authorize users and devices, thereby preventing unauthorized access and data breaches. To assess the proposed framework’s effectiveness, real-world IoV scenarios were used to conduct experiments, and the results demonstrate that the framework can provide secure and trustworthy communication for IoV applications. The proposed blockchain-enabled communication framework provides a promising solution for addressing security and trust challenges in IoV communication systems.

SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

The content-centric networking (CCN) architecture allows access to the content through name, instead of the physical location where the content is stored, which makes it a more robust and flexible content-based architecture. Nevertheless, in CCN, the broadcast nature of vehicles on the Internet of Vehicles (IoV) results in latency and network congestion. The IoV-based content distribution is an emerging concept in which all the vehicles are connected via the internet. Due to the high mobility of vehicles, however, IoV applications have different network requirements that differ from those of many other networks, posing new challenges. Considering this, a novel strategy mediator framework is presented in this paper for managing the network resources efficiently. Software-defined network (SDN) controller is deployed for improving the routing flexibility and facilitating in the inter-interoperability of heterogeneous devices within the network. Due to the limited memory of edge devices, the delectable bloom filters are used for caching and storage. Finally, the proposed scheme is compared with the existing variants for validating its effectiveness.

Security Challenges Facing Blockchain Based-IoV Network: A Systematic Review

The Internet of Vehicles (IoV) is an innovative concept aimed at addressing the critical problem of traffic congestion. IoV applications are part of a connected network that collects relevant data from various smart sensors installed in connected vehicles. This information is freely and easily exchanged between vehicles, which leads to improved traffic management and a reduction in traffic accidents. As the IoV technology continues to grow, the amount of data collected will increase, presenting new challenges for data privacy and security. The use of blockchain technology has been proposed as a solution, as its decentralized and distributed architecture has been proven reliable with cryptocurrencies such as Bitcoin. However, studies have shown that blockchain alone may not be sufficient to address privacy and security concerns, and there are currently no tools available to evaluate its performance in an IoV simulation environment. This research aims to provide a comprehensive review of the challenges associated with the implementation of blockchain technology in the IoV context.

PPO-Based PDACB Traffic Control Scheme for Massive IoV Communications

Traffic control is regarded as a key issue to alleviate congestion in internet of vehicles (IoV) machine-type communications (MTC). Recently, many traffic control schemes have been studied, such as access class barring (ACB) scheme and back-off (BO) scheme. However, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid scheme, combining the priority dynamic ACB (PDACB) scheme and BO scheme. The IoV devices are classified depending on different delay characteristics, where the delay-sensitive devices are classified as high priority. The target is to maximum the successful transmission of packets with the success rate constraint by adjusting the various ACB factors. Proximal policy optimization (PPO) algorithm as a unique deep reinforcement learning (DRL) method is utilized in this paper, which can obtain continuous action space and solve for the optimal ACB factors without estimating backlog of nodes. A quick convergence is achieved by designing sensible state space, action space and reward. The access capability of the PDACB traffic control scheme is verified by simulations.

A novel vehicular task deployment method in hybrid MEC

AbstractWith the skyrocketing need for low-latency services on the Internet of Vehicles (IoV) and elastic cross-layer resource provisioning, multi-access edge computing (MEC) is considered a high-potent solution, which evolves from cloud and grid computing to meet the above needs in IoV scenarios. Instead of considering single-point and monolithic IoV tasks, in this paper, we consider the IoV applications to be with structural properties and the supporting environment to be with a hybrid cloud-edge architecture. We develop a scheduling method that offloads tasks to the eNode or cloud according to their estimations of latest starting time. Simulative results clearly demonstrate that our method beat existing solutions in terms of average completion time, average waiting time, and in-time completion rate.

Secure Authentication and Key Management Protocol for Deployment of Internet of Vehicles (IoV) Concerning Intelligent Transport Systems

Intelligent transport systems amalgamated with advanced technologies are an important element of the automotive industry, including critical infrastructure and transportation. Internet of Vehicles (IoV) is the modern technological framework designed for intelligent transportation. IoV creates a network of information relations among vehicles, thus contributing to reduced congestion, roadside infrastructure, driver/traveller safety, and traffic efficiency through wireless communication and sensing technology. However, a significant challenge in IoV applications is security, as criminals could potentially exploit these applications. It is clear that despite increasing industry awareness, the potential danger posed by security vulnerabilities and cyber threats is high. In this study, we have designed a new system called AKAP-IoV, which supports secure communication, mutual authentication, and key management among vehicles, roadside units, and fog and cloud servers. AKAP-IoV was tested and verified using Scyther and Tamarin to ensure its resistance to cyber threats. Furthermore, we conducted a formal security analysis using the Real-or-Random (RoR) oracle model to assess security properties logically. In addition, a detailed, comprehensive comparative study was considered to evaluate the performance, functionality, efficiency and security features supported by AKAP-IoV compared to those of recently developed schemes.