Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as autonomous vehicles, healthcare diagnostics, and financial fraud detection. Despite its benefits, the deployment of ML models brings significant security challenges, such as adversarial attacks, which can compromise the integrity and reliability of these systems. To address these challenges, this paper builds upon the concept of Secure Machine Learning Operations (SecMLOps), providing a comprehensive framework designed to integrate robust security measures throughout the entire ML operations (MLOps) lifecycle. SecMLOps builds on the principles of MLOps by embedding security considerations from the initial design phase through to deployment and continuous monitoring. This framework is particularly focused on safeguarding against sophisticated attacks that target various stages of the MLOps lifecycle, thereby enhancing the resilience and trustworthiness of ML applications. A detailed advanced pedestrian detection system (PDS) use case demonstrates the practical application of SecMLOps in securing critical MLOps. Through extensive empirical evaluations, we highlight the trade-offs between security measures and system performance, providing critical insights into optimizing security without unduly impacting operational efficiency. Our findings underscore the importance of a balanced approach, offering valuable guidance for practitioners on how to achieve an optimal balance between security and performance in ML deployments across various domains.

Connected and Autonomous Vehicles (CAVs) equipped with diverse sensors can enhance environmental perception through cooperative sensing, overcoming individual sensor limitations such as restricted range and occlusion. However, privacy concerns regarding location exposure and data leakage significantly hinder widespread adoption. This paper presents a comprehensive privacy-preserving cooperative sensing framework that enables secure data sharing among CAVs without compromising performance. We introduce two key innovations: the Vehicular Spatial Index Tree (VSITree), which provides efficient spatial indexing while preventing location leakage through cryptographic encoding, and the Vehicular Attribute Matching Protocol (VAMP), which enables oblivious membership testing between encrypted sensing data and queries. Our framework leverages arithmetic secret sharing and predicate encryption to protect both sensing providers and requesters throughout the data lifecycle. The system is designed to operate through roadside units (RSUs) that facilitate secure matching and aggregation without learning sensitive information. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of our approach, confirming its resilience against various attack vectors while maintaining real-time performance suitable for safety-critical vehicular applications.

Vehicular crowdsensing enables Connected and Autonomous Vehicles (CAVs) to jointly contribute driving-related data to support applications such as traffic management and accident analysis. Ensuring the reliability of such data requires identifying observations that have been corroborated by multiple vehicles. However, achieving this corroboration typically necessitates comparing each vehicle’s private set of observations, which can inadvertently reveal sensitive trajectory information. To address this privacy challenge, we introduce Edge-Assisted Private Set Intersection (EA-PSI), a scheme that enables secure computation of the intersection among CAV observation sets without disclosing individual data elements. Our design begins with a protocol that leverages polynomial-based set encoding and additive secret sharing, in which each vehicle encodes its observation set as a polynomial and divides it into two shares, delegating one share to a roadside unit (RSU) while retaining the other locally. The RSU then performs intersection computation on the collected shares using randomized encoding techniques, without learning any private observations or intersection results. To support necessary polynomial operations over secret-shared data, we develop a secure multiplication mechanism based on Beaver triples, enabling the RSU and vehicles to jointly compute polynomial products without reconstructing underlying values. In addition, we design a key-distribution protocol that facilitates secure communication among vehicles through the RSU, eliminating the need for direct vehicle-to-vehicle exchange. We analyze the security of EA-PSI under the simulation-based paradigm and formally prove privacy against semi-honest adversaries. Experimental evaluation of computational and communication costs demonstrates the efficiency and practicality of the proposed EA-PSI scheme.

Industrial Internet of Things (IIoT) systems in sectors like manufacturing, energy, and healthcare are increasingly deployed in cloud-assisted operational environments, where network telemetry and security analytics are routinely processed in the cloud. However, these systems remain highly vulnerable to cyber threats from shared threat actors. Among these, low-rate Denial of Service (LDoS) attacks, marked by subtle periodic traffic patterns, are particularly challenging to detect when analyzed in isolation. Cross-organization collaborative detection across cloud platforms can improve identification accuracy, but sharing threat intelligence risks exposing sensitive operational information. To tackle this challenge, we propose Labelled-Threshold Private Set Intersection (LT-PSI), a cryptographic framework that allows two organizational clouds to securely identify common elements whose associated label vectors satisfy a similarity threshold, without revealing any additional data. Our LT-PSI protocol introduces an innovative combination of position encoding, Diffie-Hellman Oblivious Pseudorandom Functions (DH-OPRF), and Bloom filters, effectively transforming threshold-based label similarity matching into efficient and privacy-preserving set membership tests. Particularly, our protocol achieves sublinear online complexity and is well-suited for cloud execution, integrating an adaptive early termination strategy that significantly reduces the number of OPRF invocations. We provide formal security proofs under the semi-honest model and validate the protocol through extensive experiments across diverse similarity thresholds and dataset sizes. Results show that LT-PSI is significantly more efficient than brute-force threshold matching while preserving privacy. The framework naturally supports cloud-to-cloud collaborative security analytics and generalizes to broader cloud and edge threat intelligence scenarios requiring private, threshold-based feature matching.

Effective sensing capabilities are crucial for the safe and reliable operation of Connected and autonomous vehicles (CAVs). While traditional approaches focus on enhancing onboard sensors, the integration of road sensor networks (RSNs) into the CAV ecosystem presents a promising solution to improve sensing performance, but also introduces significant challenges, including heterogeneous sensing requirements, inconsistencies in multisource sensor data, and efficient resource utilization. To address these challenges, this article proposes a novel cooperative sensing framework that leverages multisource and multilevel sensing information from RSNs to optimize CAV sensing performance in resource-constrained scenarios. We develop an improved decision transformer (DT)-based approach that dynamically adapts to diverse driving conditions and efficiently fuses sensor data at various abstraction levels. To tackle the issue of long-delayed rewards, we introduce a reshaped reward function and a bi-level optimization framework that enables effective propagation of rewards along decision sequences. An advanced gradient approximation technique is employed to efficiently solve the optimization problem. Extensive simulations demonstrate the superior performance of our improved DT approach compared to state-of-the-art reinforcement learning (RL) methods in terms of sensing accuracy, coverage, and data efficiency under various traffic conditions.

DevOps, with its increasing prevalence in both industry and academia, has evolved into various DevOps variants (namely XOps) to address emerging technological and operational challenges. However, this proliferation has created confusion and a lack of clarity about the systematic understanding of these XOps and their interrelationship in the DevOps landscape, leading to fragmented knowledge and application. This research seeks to construct a comprehensive picture of the existing DevOps landscape, clarifying the nature and nuances of various XOps, to guide effective future studies and implementations. Utilizing Multivocal Literature Review (MLR), 80 gathered documents are thoroughly examined from throughout the whole community, encompassing both white and grey literature, to map the DevOps landscape. Our review systematically discovered 38 XOps terms and 13 well-studied XOps including AIOps, BizDevOps, CloudOps, DataOps, DevSecOps, FinOps, GitOps, MLOps, ModelOps, NetDevOps, NoOps, SecDevOps and TwinOps. We provided dictionary-like resource that elucidates the core concepts and main ideas associated with each XOps. An in-depth understanding of intricate evolution from DevOps to XOps is delved into, supplemented by the research of relationships between XOps and various technological enablers as well as relationships between XOps and organizational teams, contributing to the ongoing dialogue surrounding their application and evolution. This paper provides a foundational understanding of the DevOps landscape including open issues and challenges, current and future trends, assisting both researchers and practitioners in navigating this complex field. It establishes a platform for further research and practical applications in the evolving field of DevOps and XOps.

With the rapid evolution of vehicular network technology, the integration of Machine Learning (ML) with Connected and Autonomous Vehicles (CAVs) presents both remarkable opportunities and formidable challenges. This paper addresses the crucial need for efficient ML model training in the context of Federated Learning (FL) within vehicular networks. Recognizing the limitations imposed by the tradeoff between the high energy cost at the local level with the performance problem at the global level, we propose an innovative approach that harmonizes cost optimization with strategic model selection. Our strategy primarily focuses on optimizing energy consumption during model training and updating at the vehicle end, thereby resolving the prevalent issue of limited end-user participation in FL due to high energy demands. Additionally, we introduce an advanced model selection method, prioritizing local model uploads and adaptively allocating bandwidth to clients with more extensive training data. This method enhances the efficiency and reliability of model updates, ensuring robust global model performance. We validate our approach through extensive simulations, demonstrating not only improved learning performance but also a significant reduction in energy consumption among participating clients.

Connected and Autonomous Vehicles (CAVs) increasingly leverage sophisticated sensor systems integrated with emerging technologies like the sixth generation (6G) for enhancing driving safety and efficiency. Despite the potential of utilizing the advanced communication technology to enhance driving reliability, conficts between high-quality sensing needs of CAVs and insufficient sensing sharing wellness present significant challenges. To bridge the gaps, this paper proposes a novel cooperative sensing sharing framework that utilizes vehicle-to-vehicle (V2V) communication to extend the effective sensory range of CAVs, thereby reducing perception gaps and enhancing driving efficiency in complex driving environments. Aiming at incentivizing cooperative sensing sharing ecosystem within this decentralized framework, we first introduce a multi-tier blockchain architecture that ensures secure and transparent data sharing among CAVs. Concurrently, a custom-designed efficient consensus algorithm is proposed to minimize the overhead while guaranteeing transaction throughput. To tackle issues related to trust and motivate long-term cooperative behavior, we introduce a supervision-oriented model that utilizes evolutionary game to formulate incentive mechanisms discouraging malicious participation and promoting honest, active engagement. Finally, theoretical analysis and extensive simulations demonstrate that our system not only ensures healthy sensing sharing performance but also maintains system security.

Currently, heterogeneous driving scenarios and complex traffic conditions challenge connected and autonomous vehicles (CAVs) to achieve accurate sensing of road conditions. Existing research on the sensing capabilities of vehicles only relys on adding more onboard sensors, which makes the driving safety unable to be guaranteed due to the installment and cost limit of various sensors. Therefore, this paper proposes an enhanced 3D sensor deployment method to break through the sensing capabilities of CAVs’ own equipment limitations. By efficiently utilizing road infrastructure, reasonable roadside sensor deployment will effectively assist CAVs to expand the sensing range and improve overall sensing accuracy. Firstly, in order to address the limitations of existing works that often rely on simplified sensor models and idealized road conditions, we propose a Bresenham-based sensor and environment model that can be used to construct realistic road environments. Secondly, a decision transformer (DT)-based method is adopted to solve the problem of optimal deployment of sensors in road environments. Our approach effectively addresses the limitations of traditional static deployment methods, which often fail to consider the complexities of real-world driving conditions and the diverse factors influencing optimal sensor deployment. Finally, in order to solve the problem of DT delayed rewards, we propose a two-layer optimization method to redistribute the reward function to solve the challenge of local optimization. A large number of simulations oriented to sensing effects not only verify the effectiveness of the sensor deployment method but also ensure the reliability of sensing assistance.

Large Language Models (LLMs), as advanced AI technologies for processing and generating natural language text, bring substantial benefits to the Industrial Internet of Things (IIoT) by enhancing efficiency, decision-making, and automation. Nevertheless, their deployment faces significant obstacles due to high computational and energy demands, which often exceed the capabilities of many industrial devices. To overcome these challenges, edge-cloud collaboration has become increasingly essential, assisting in offloading LLMs tasks to reduce the computational load. However, traditional reinforcement learning (RL)-based strategies for LLMs task offloading encounter difficulties with generalization ability and defining explicit, appropriate reward functions. Therefore, in this paper, we propose a novel framework for offloading LLMs inference tasks in IIoT, utilizing a Decentralized Identifier (DID)-based identity management system for trusted task offloading. Furthermore, we introduce an intelligence-based RL (IRL) approach, which sidesteps the need for defining specific reward functions. Instead, it uses “intelligence” as a metric to evaluate cognitive improvements and adapt to varying environmental preferences, significantly improving generalizability. In our experiments, we employ the GPT-J-6B model and utilize the Human Eval dataset to assess its ability to tackle programming challenges, demonstrating the superior performance of our proposed solution compared to existing methods.

Connected and Autonomous Vehicles (CAVs) can improve their perception by integrating data from roadside sensors. However, ensuring secure authentication between CAVs and sensors is challenging due to the limited capabilities of sensors and the growing number of vehicles. This paper introduces a secure authentication protocol that enables direct communication between CAVs and roadside sensors, addressing a critical gap in existing research focused on vehicle-to-cloud authentication. The proposed multi-factor authentication scheme combines password, biometric, and device-specific factors with Elliptic Curve Cryptography (ECC) and efficient key agreement protocols. A comprehensive adversary model tailored for vehicular networks is presented, along with an in-depth security analysis demonstrating the scheme’s resilience against various threats. The cloud-assisted authentication framework offloads computationally intensive tasks to the cloud server, reducing the burden on resource-constrained Roadside Units (RSUs) and ensuring scalability. Extensive performance evaluations showcase the scheme’s computational efficiency, low communication overhead, and storage costs compared to state-of-the-art solutions, highlighting its practical feasibility and potential for real-world deployment in intelligent transportation systems.

Edge intelligence and federated learning (FL), as key enablers of 6G, is a promising solution for networked Autonomous Driving (NAD). However, traditional federated learning is a server-client architecture, which makes the model training overly dependent on a fixed single aggregation server and makes the FL process insecure and unreliable due to the vulnerability of the aggregation server to a single point of failure. In this paper, we propose a rotating server FL scheme (RSFL) to solve the problem of single point of failure and limited resources and improve environmental adaptability. Specifically, we consider multiple factors to measure the vehicle performance and find the vehicle with the highest performance score in this round as the server for the next round while setting weights that are randomized in each round, which reduces even more the likelihood that a malicious user will recognize the regularity of the chosen server. Finally, the performance of RSFL is evaluated through a large number of experiments, and the results show that compared with baseline FL, FL with randomly selected servers, and peer-to-peer decentralized FL, RSFL can effectively reduce the cases of servers being detected and attacked by malicious adversaries, and improve the accuracy of the model.

Federated learning (FL), as a distributed technology, has great application potential in autonomous driving. However, due to the lack of a quality verification mechanism for the client, it faces the problem of malicious users attacking the global model, which reduces the accuracy of FL model. In response to this problem, this paper proposes a hierarchical blockchain-enabled FL with reputation management scheme, which improves the efficiency and accuracy of FL while ensuring privacy and security. In this paper, we first propose a hierarchical blockchain framework and design a blockchain consensus algorithm based on parameter proof of quality (PoQ), which can provide FL workers with a secure and efficient data storage environment in a decentralized manner, while making up for the poor scalability of traditional single blockchains. On this basis, we design a reputation management method based on Bayesian theory and multiple subjective logic models to select high-quality local participating users. In particular, the weights of factors such as interactive activity and interactive position are taken into account to improve the accuracy of reputation calculations. Extensive simulations validate the performance of our scheme in improving FL accuracy and efficiency.

The virtual world created by the development of the Internet, computers, artificial intelligence (AI), and hardware technologies have brought various degrees of digital transformation to people’s lives. With multiple demands for virtual reality increasing, the metaverse, a new type of social ecology that can connect the physical and virtual worlds, is booming. However, with the rapid growth of data volume and value, the continuous evolution of the metaverse faces the demands and challenges of privacy, security, high synchronization, and low latency. Fortunately, the ever-evolving blockchain and intelligent networking technologies can be used to satisfy the trusted construction, continuous data interaction, and computing demands of the metaverse. Therefore, it is necessary to conduct an in-depth review of the role and gains of blockchain, intelligent networking, and the combination of both in providing the immersive experiences of the metaverse. In this survey, we first discuss the development trend, characteristics, and architecture of the metaverse. Then, the existing work on blockchain, networking, and the combination of the two technologies are reviewed, including overviews, applications, and challenges. Next, applications of the metaverse are summarized, emphasizing the importance of the metaverse and the fields of development. Finally, we discuss some open issues, challenges, and future research directions.

Federated learning (FL), as a distributed machine learning technology, allows large-scale nodes to utilize local datasets for model training and sharing without revealing privacy, which has significant efficiency and advantages in artificial intelligence (AI)-based knowledge sharing of connected and autonomous vehicles (CAVs). However, for FL, there are challenges to ensure the security of knowledge, deal with the lazy behavior of participants, and enforce effective incentives. To bridge the gaps, in this paper, we first propose a hierarchical blockchain-supported FL architecture that utilizes the immutable and transparent properties of blockchain to enable secure storage and sharing of knowledge and transaction information with scalability. Then, considering the cost and laziness of the participants in the FL process, we propose an incentive mechanism combined with the supervision game to attract high-quality participants based on a comprehensive evaluation of model quality and participants’ reputation. Extensive simulation results validate that our proposal can improve learning accuracy and efficiency while ensuring security.

With accurate network traffic prediction, communication systems can make self-management and embrace efficient automation. However, due to the lack of consideration of dynamic spatial interactions between regions in existing methods, the performance of network traffic prediction still needs to be improved. To solve the above problem, this paper proposes a spatiotemporal dynamic relative-flow network (STDRN) to capture the dynamic spatial dependency, thereby improving the accuracy of urban network prediction. In the proposed STDRN, we take both spatial and temporal dependence of network traffic into consideration. First, we propose relative-flow gating mechanism (RGM) combined with convolutional neural network (CNN) to learn the dynamic spatial dependency. Then, we adopt the periodic shifting attention mechanism to capture the long-term periodic temporal dependency while long short-term memory (LSTM) for the short-term temporal dependency. The STDRN is tested on real-world network traffic dataset. Experimental results demonstrate that compared with other benchmark methods, STDRN can reduce RMSE by up to 48.99% and MAPE by 13.61%, which proves the effectiveness of our proposal.

The customization of edge computing services is one of the key research fields in sixth-generation (6G) heterogeneous vehicular networks (HetVNETs). With various personalized requirements of vehicles on computation-intensive applications, how to explore the heterogeneous computing resources in the 6G HetVNETs to guarantee vehicles with the customized Quality of Experience (QoE), therefore, becomes a challenge. In this article, we develop a novel secure scheme to provide personalized edge computing services for moving vehicles (MVs) in 6G HetVNETs. In the scheme, a smart-contract-based secure edge computing architecture is designed by jointly considering the attack models and the characteristics of the 6G network infrastructures (e.g., satellites, drones, base stations, and roadside units), where each network infrastructure manages a number of parking vehicles to complete computing services collaboratively. With this architecture, based on the available computing resources owned by different network infrastructures, the collaborative computing resource allocation algorithm is designed to help each network infrastructure decide a customized service strategy (CSS) to satisfy the QoE of MVs. After deciding the CSSs, a model based on the second price-sealed auction is formulated to describe the competition among the network infrastructures, where the Nash equilibrium of the game is obtained to guide their optimal bidding strategies to obtain the chance for completing the services. The security analysis and the simulation results show that the proposed scheme can defend against the attacks and lead to a lower cost for completing the services than the conventional schemes.

The rapid growth of connected and autonomous vehicles (CAVs) shows an urgent demand for driving and transportation-related data, which gives rise to vehicular crowdsensing systems (VCSs). Nevertheless, the existing centralized VCS framework mainly faces the system reliability problem while the decentralized one cannot satisfy the management flexibility. In addition, when the privacy preservation scheme that prevents information leakage encounters the user selection scheme that desires detailed information of participants, how to balance this seemingly irreconcilable contradiction is inevitable for VCS. To remedy that, we take the first research attempt and explore the balance point between the system management, privacy preservation, and quality of experience (QoE) of participants. By fully exploiting the characters of participating entities, a blockchain-enabled conditional decentralized VCS is proposed in this paper. Firstly, we propose a privacy-preserving scheme where the zk-SNARK proof combines with the mixed-task smart contract to guarantee the interaction process will not reveal any private information of participants. Secondly, we propose an efficient reputation management mechanism that renders certain the participants can get a satisfactory QoE even under the condition that the private information of users is secured. And also, the malicious operations in the system will be effectively supervised. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of privacy preservation and indicate the effectiveness of reputation management.

Realizing driving safety is the fundamental goal pursued by artificial intelligence (AI)-enabled autonomous driving. However, due to the limited capacity of vehicles and the limited scenarios involved, the reliability and environmental adaptability of the current single-vehicle intelligence still need to be improved. In addition, driving knowledge only exists locally in each connected autonomous vehicle (CAV) and cannot be effectively reused or shared with other CAVs. To solve the above problems, this paper proposes to use federated learning (FL) to realize the collaboration of CAVs without revealing local data, thereby improving the accuracy of CAVs decision-making and driving safety. First, for the typical rear-end collision scenario, we propose a local decision-making model that comprehensively considers multiple influencing factors to fit the actual traffic environment. Then, we design a federated learning process for knowledge sharing. In particular, we propose a model similarity method to select high-quality local models for upload, thereby reducing communication overhead while improving the accuracy of the global model. Extensive simulations validate the performance of our proposal in reducing communication overhead and improving decision accuracy.

The travel delay caused by incidents severely reduces the efficiency of traffic. This symptom has been relieved with the development of advanced communication technologies. However, the emergency traffic information (ETI) is meaningless for vehicles which do not traverse the incident segment. Unlike most existing studies concentrating on the network performance during the dissemination of ETI to all vehicles, this paper proposes a joint traffic-communication optimization strategy (JTCS) to reduce the extra cost caused by unnecessary communication, which minimizes the total communication and traffic cost by transmitting the ETI to the worthy vehicles who need the ETI. Specifically, we capture the optimal targeted ETI transmission strategy with combination of radio resource allocation strategy (RRAS) and traffic-influencing transmission strategy (TTS), which can be converted into a bi-level optimization problem. The lower-level problem minimizes the total cost by Lagrangian method and obtains the optimal RRAS when the TTS is given. The upper-level problem develops the optimal TTS based on the optimal RRAS obtained in the lower-level problem. Simulation results using SUMO and MATLAB indicate that JTCS can achieve minimal total cost of ETI transmission and vehicle rerouting by comparing with existing approaches.

Recently, with a large number of on-board sensors, vehicles have been widely used for Vehicular Crowdsensing (VCS). Appropriate multi-task assignment strategy is crucial for V CS. However, sensing efficiency and benefit of the system of the existing works need to be improved due to the following challenges. On one hand, many sensing tasks have clustering characteristics in terms of their geographic distribution and sensing requirements, but current works are often ignored. On the other hand, existing multi-task assignment strategies often only design optimization problem for the benefit of one of the platform or participants, and fail to maximize the overall benefit of the system. To remedy that, this paper proposes a multitask assignment for tasks with clustering characteristics. First, we propose a task combination algorithm, which can greatly improve the task assignment efficiency and reduce the sensing cost. Next, we design a two-stage task assignment scheme, in which the benefits of the platform and participants are optimized respectively in two stages to maximize the benefit of the system. Finally, we carry out extensive simulations, and the simulation results verify the effectiveness of our proposal from clustering validity and sensing cost.

With the development of communication and Internet of Vehicles (IoV) technology, a large number of high precision sensors and computing units are widely used and deployed on vehicles. With the vehicles work as users, Mobile Crowdsensing (MCS) system has a broad application prospect in traffic planning, environmental monitoring and so on. The realization of these applications needs a large amount of data. However, in the process of crowdsensing, users are often faced with the consumption of computing, communication and energy and the risk of privacy leakage, so they are reluctant to actively participate. Therefore, we need to design a safe and reasonable incentive mechanism. In this paper, we focus on privacy protection and user incentive, and propose a framework of the vehicular crowdsensing with blockchain, as well as the smart contacts deployed on the blockchain. The characteristics of the blockchain are used to solve the security problems in the crowdsensing process. In addition, we propose a reverse auction-based incentive mechanism. A group of users with the highest reputation value are selected to complete the sensing tasks, and the payoffs are assigned according to the quality of the sensing data uploaded by the selected users. Finally, Matlab-based simulation verifies the effectiveness of the incentive mechanism proposed in this paper.

Three-dimensional (3D) highly dynamic unmanned aerial vehicle networks (UAVNets) could serve as a pivotal intermediate architecture in air and space integration networks. For UAVNets, efficient routing protocols for data packet delivery are crucial to its wide use. However, communication in UAVs has been a challenging project because of frequent failures at forwarding nodes. In this paper, we propose a localized heuristic solution, called the reliability-aware adaptive greedy-multicast routing (RAGM-3D), to reduce the negative impact of failure on route forwarding. RAGM-3D dynamically adjusts the next-hop forwarding node set which, initially selected by a new multi-factor hybrid greedy strategy, are based on the reliability evaluation to route packets to the destination efficiently. Simulation results show that comprehensive performance of RAGM-3D in terms of packet delivery ratio, delay, and routing overhead outperforms other compared protocols.

Leveraging the Internet of Vehicles (IoV) to accurately and timely disseminate traffic information, especially for emergency information, is an important way to improve traffic safety and efficiency. However, privacy and security issues in the information transmission process will seriously affect the reliability of the information dissemination system. In this paper, we propose a decentralized targeted information dissemination framework in vehicular networks based on blockchain techniques. In this framework, the information collected by users is uniformly judged and identified by Roadside Units (RSUs), and finally packaged and uploaded to the blockchain that makes the system run more stable and reliable. Further more, we utilize reputation evaluation algorithm to comprehensively process the collected information, and we design the corresponding reputation management system to stimulate more users to participate in the system and provide as accurate information as possible. Extensive simulation results reveal that the proposed framework is effective and feasible in collecting, filtering, conducting, and disseminating in vehicular networks.