Vehicular Communications最新文献

With the popularity of vehicular communication systems and mobile edge vehicle networking, intelligent transportation applications arise in Internet of Vehicles (IoVs), which are latency-sensitive, computation-intensive, and requiring sufficient computing and communication resources. To satisfy the requirements of these applications, computation offloading emerges as a new paradigm to utilize idle resources on vehicles to cooperatively complete tasks. However, there exist several obstacles for realizing successful task offloading among vehicles. For one thing, extra cost such as communication overhead and energy consumption occurs when a task is offloaded on a service vehicle, it is unlikely to expect the service vehicle will contribute its resources without any reward. For another, since there are many vehicles around, both user vehicles and service vehicles are trying to strike a balance between cost and profit, through matching the perfect service/user vehicles and settled with optimal offloading plan that is beneficial to all parties. To solve these issues, this work focuses on the design of effective incentive mechanisms to stimulate vehicles with idle resources to actively participate in the offloading process. A fuzzy logic-based dynamic pricing strategy is proposed to accurately evaluate the cost of a vehicle for processing the task, which provides insightful guidance for finding the optimal offloading decision. Meanwhile, the competitive and cooperation relations among vehicles are thoroughly investigated and modeled as a two-stage Stackelberg game. Particularly, this work emphasizes the social attributes of vehicles and their effect on the offloading decision making process, multiple key properties such as the willingness of UV to undertake the task locally, the reputation of UV and the satisfaction of SV for the allocated task proportion, are carefully integrated in the design of the optimization problem. A distributed algorithm with applicable complexity is proposed to solve the problem and to find the optimal task offloading strategy. Extensive simulations are conducted on real-world scenarios and results show that the proposed mechanism achieves significant performance advantages in terms of vehicles' utilities, cost, completion delay under varied network and channel environment, which justifies the effectiveness and efficiency of this work.

Intelligent Transportation System (ITS) is one of the newest technologies in the transportation sector that will give hope for better driving safety. Not only in terms of driving safety, but ITS will give also hope for driving comfort. Smart vehicles perchance better versatile to the street circumstances through trade data among vehicles. In case, they can maintain a strategic distance from activity blockage, perilous deterrents, or see activity mishaps prior. The innovation which is meticulously associated with the security of the driver must get extraordinary consideration. V2V-Vehicle-to-Vehicle connection can undermine impedance and indeed attack or anomaly. Many studies have been carried out to address this problem. The primary step is to reinforce the system's capacity to identify anomalies on Vehicular Network. Further, the growing development of machine learning seems to bring hope to support these steps. Within the proposed method, the original of our approach consists in utilizing 2-Step of anomaly detection. This framework is utilizing two classifiers machine learning from two altered preparing data-sets. We appear that the proposed method can make strides essentially attack detection achievement, compared to arrangements depending on a single detection step.

With the growing concern over range anxiety among electric vehicle (EV) owners due to limited battery capacity and sparse charging infrastructure, EV-to-EV (V2V) energy sharing emerges as a crucial solution to extend driving range. Leveraging the vehicular energy network (VEN) enabled by dynamic wireless power transfer (DWPT) technology, energy sharing among EVs in motion becomes feasible. However, the effective establishment of communication and identification of suitable V2V energy sharing pairs pose significant challenges, particularly for EVs with discharging demands. To address this challenge, we propose a new discharging driven energy sharing protocol based on vehicular ad-hoc networks (VANETs). Firstly, we present a routing approach for transmitting discharging information through VANETs, considering key factors such as distance, state of charge, number of neighbor vehicles, and vehicle speed. This routing scheme facilitates efficient relay node selection on road segments and intersections, ensuring optimal communication paths. Subsequently, we formulate a charging requester selection model to identify the most suitable requester for energy sharing. This model optimizes individual utility while accounting for the state of charge of the requesters, ensuring a comprehensive and inclusive approach to V2V energy sharing. Finally, we develop an acknowledgment message transmission scheme to ensure the completion of selection acknowledgment between discharging EVs and charging EVs. This scheme includes provisions for recovery in case of forwarding link failures, ensuring robust communication in dynamic vehicular environments. Extensive simulations conducted using network simulator 2 (NS-2) demonstrate the superior performance of the proposed protocol in terms of packet delivery ratio, end-to-end delay, and overall V2V energy sharing efficiency.

In the Internet of Vehicles (IoV) domain, the collection of traffic data is executed by intelligent devices and stored within a cloud-assisted IoV system. However, ensuring confidentiality and authorized access to data are the main problems of data storage. To address these problems, this paper proposes an efficient heterogeneous online/offline certificateless signcryption with a proxy re-encryption scheme (HOOCLS-PRE). This scheme enables IoV nodes in a certificateless cryptosystem (CLC) environment to store encrypted IoV-related data in the cloud. When an authorized user from an identity-based cryptosystem (IBC) wishes to access the data, the IoV node delegates the task of re-encrypting the data to the cloud, and only an authorized user can decrypt the data and verify its integrity and authenticity. The cloud cannot obtain any plaintext details about the data. In the proposed scheme, the signcryption process is split into offline and online phases. Most heavy computations are conducted without knowledge of the message during the offline phase. Only light computations are performed in the online phase when a message is available. The scheme protects the privacy and anonymity of vehicles by preventing adversaries from linking vehicle identities and locations. Moreover, a formal security proof is provided in the random oracle model. Finally, the performance analysis reveals that HOOCLS-PRE outperforms existing relevant schemes. Hence, HOOCLS-PRE is ideal for cloud-assisted IoV environments.

Time-efficient route planning is a significant research area of Internet of Vehicular Things. Optimal route selection is important to reach the destination in minimal time. Further, energy efficiency is vital for route planning in a sustainable environment. To address these issues, this paper proposes a federated learning and genetic algorithm-based green edge computing framework for optimal route planning in Internet of Vehicular Things. The vehicles are connected to the road side unit. The road side unit processes the image and video of the road, and predicts the number of vehicles on the road. For video processing Region-based Convolutional Neural Network is used. The road side units send the result and the local model parameters to the regional server. The regional server determines the optimal route using modified genetic algorithm, and sends it to the vehicles and the cloud. Also, the regional server updates its model and sends the updated model parameters to the road side units. The road side units update their local models accordingly. The regional server also sends the model parameters to the cloud, and the cloud updates the global model. The cloud sends the updated model parameters to the regional servers. The regional servers update their models accordingly. The results present that above 90% accuracy is achieved by the proposed model. The results also present that using modified GA the proposed approach reduces time and power consumption to find the optimal route by ∼62% and ∼66% than the cloud-only model.

Vehicular Ad-Hoc Networks (VANETs), a subset of Mobile Ad-Hoc Networks (MANETs), are wireless networks formed around moving vehicles, enabling communication between vehicles, roadside infrastructure, and servers. With the rise of autonomous and connected vehicles, security concerns surrounding VANETs have grown. VANETs still face challenges related to privacy with full-scale deployment due to a lack of user trust. Critical factors shaping VANETs include their dynamic topology and high mobility characteristics. Authentication protocols emerge as the cornerstone of enabling the secure transmission of entities within a VANET. Despite concerted efforts, there remains a need to incorporate verification approaches for refining authentication protocols. Formal verification constitutes a mathematical approach enabling developers to validate protocols and rectify design errors with precision. Therefore, this review focuses on authentication protocols as a pivotal element for securing entity transmission within VANETs. It presents a comparative analysis of existing protocols, identifies research gaps, and introduces a novel framework that incorporates formal verification and threat modeling. The review considers key factors influencing security, sheds light on ongoing challenges, and emphasises the significance of user trust. The proposed framework not only enhances VANET security but also contributes to the growing field of formal verification in the automotive domain. As the outcomes of this study, several research gaps, challenges, and future research directions are identified. These insights would offer valuable guidance for researchers to establish secure authentication communication within VANETs.

Quantum-proof authentication is essential for vehicular communications as the threat of quantum computing attacks on traditional encryption methods grows. Several lattice-based authentication protocols have recently been developed to help with this issue, but they come with hefty storage and communication overheads. Most of them also fail to provide strong anonymity for vehicles and edge nodes and can not support authentication for vehicles from multiple domains. This study presents a new lattice-based authentication protocol for vehicular communications that addresses limitations of previous methods and offers advanced security features such as anonymity, and unlinkability. The protocol utilizes a distributed ledger to store public keys, making the system more secure, tamper-proof, and efficient for key revocation in large networks. Additionally, it allows for a multi-domain authentication system for vehicular communication with improved security and flexibility. The proposed protocol's security is evaluated both formally and informally to demonstrate its resistance against the well-known attacks. Additionally, the performance analysis indicates that the proposed protocol surpasses current protocols and is suitable for vehicular communications.

Device-to-Device (D2D) communication when used in conjugation with Unmanned Aerial Vehicle (UAV) Femtocell and unlicensed spectrum can effectively tackle the ever-increasing mobile data demands. However, D2D communication raises security and privacy concerns among users due to the absence of a centralized entity such as a base station. Therefore, exploring social connections among users becomes imperative to enable secure and trustworthy D2D communication. This paper seeks to improve the performance of a social-assisted UAV cellular network augmented by D2D communication by proposing a novel subchannel assignment technique employing hypergraph coloring. Considering the real-world scenario, we incorporate the user/UAV mobility by using dynamic hypergraph coloring for subchannel assignment instead of the static one. Our proposed technique shifts a set of cellular users from the licensed to the unlicensed band based on their social connection with other co-channel D2D users. Additionally, we assign subchannels to different users to optimize the throughput while minimizing overall interference. Our proposed technique demonstrates significant improvements in system throughput, energy efficiency, and interference efficiency compared to conventional techniques. For our proposed technique, we observed improvements of 69%, 42%, and 15% in the per user system throughput when compared with the conventional techniques (graph, hypergraph, and dynamic-hypergraph, respectively). Moreover, our technique achieves higher per user energy efficiency by 120%, 70%, and 25%, and higher per user interference efficiency by 92%, 43%, and 22%, respectively, compared to graph, hypergraph, and dynamic-hypergraph techniques.

To secure Vehicle-to-everything (V2X) communications, many Conditional Privacy-Preserving Authentication schemes (CPPA) use symmetric and asymmetric encryption during the authentication process. However, several existing schemes have some security limitations regarding VANET requirements. In many symmetric cryptography-based schemes, the participants are required to share the same keys which could compromise the security of the network in case the key of one participant is compromised, while many asymmetric cryptography-based schemes take much time during the authentication process, and don't address the denial-of-service attack. In this paper, we propose a certificateless scheme that does not require a certificate and prevents the escrow problem. Plus, it uses the elliptic curve cryptography and avoids bilinear pairing and Map-to-Hash functions. We call our scheme Hybrid Cryptography-Based Scheme with a Conditional Privacy-Preserving Authentication (HCBS-CPPA), as it uses both symmetric and asymmetric cryptography during the authentication process. Our scheme combines the strength of an asymmetric encryption that satisfies non-repudiation, and the strength of a symmetric encryption that allows to perform a lightweight authentication. In addition, we show that our scheme is resilient to memory-based Denial of Service (DOS) attack which occurs when an attacker floods the memory of a receiver with invalid messages. A security proof shows that HCBS-CPPA is secure in the random oracle. Regarding the simulation of our scheme, it turns out that HCBS-CPPA has the best performance when compared with several existing certificateless schemes. Additionally, it requires less execution time during the signing and verification process, as well as less communication overhead when compared to the existing schemes.

The convergence of mobile edge computing (MEC) network with unmanned aerial vehicles (UAVs) presents an auspicious opportunity to revolutionize wireless communication and facilitate high-speed internet access in remote regions for mobile devices (MDs) as well as large scale artificial intelligence (AI) models. However, the substantial amount of data produced by the UAVs-assisted MEC network necessitates the integration of efficient distributed learning techniques in AI models. In recent times, distributed learning algorithms, including federated reinforcement learning (FRL) and split learning (SL), have been explored for the purpose of learning machine learning (ML) models that are distributed by sharing model parameters, as opposed to large raw data-sets as seen in traditional centralized learning algorithms. To implement the hybrid method, the model is first trained locally on each UAV-assisted MEC network using SL. Subsequently, the model parameters that have been encrypted are sent to a central server for federated averaging. Finally, after the model has been updated, it is distributed to each UAV-assisted MEC network for local fine-tuning. Our simulations indicate that the proposed split and federated reinforcement learning (SFRL) framework yields comparable high-test accuracy performance while consuming less energy compared to extant distributed learning algorithms. Furthermore, the SFRL algorithm efficiently realizes energy-efficient selection between the SL and FRL methods under different distributions. Numerical results shows that the proposed scheme improves the accuracy by 29.31% and reduced the energy consumption by around 67.34% and time delay by about 7.37%. as compared to the existing baseline schemes.