Traditional federated learning (FL) approaches face significant challenges when applied to dynamic and heterogeneous Internet of Vehicles (IoV) environments, which are characterized by frequent node mobility, unstable communication links, and highly non-independent and identically distributed (Non-IID) data. In particular, decentralized network topologies exacerbate the difficulty of maintaining model consistency, thereby impairing overall learning performance. To address these challenges, we propose a new hierarchical decentralized federated learning (HDFL) framework. This framework combines the advantages of centralization and decentralization, builds a three-layer collaborative structure, and improves communication flexibility through an asynchronous model exchange mechanism between the edge and the client. Simultaneously, HDFL introduces a local fine-tuning strategy based on knowledge distillation to enhance the generalization ability and stability of the model. Experimental results using an urban traffic simulation platform show that HDFL consistently outperforms representative decentralized FL methods in terms of the achieved accuracy and convergence speed under heterogeneous IoV environments.

This paper introduces a real-time semantic communication system for remote driving, addressing the challenges of video transmission over constrained and fluctuating wireless communication links. Instead of transmitting raw video streams, the proposed system extracts and transmits compact semantic representations, significantly reducing bandwidth requirements while preserving task-relevant visual information. An end-to end semantic encoding-decoding pipeline enables low latency operator-view reconstruction with low latency, improving robustness without relying on high-throughput links. Implemented and evaluated on the real-time prototype, the proposed system demonstrates the practicality of semantic communication for enhancing responsiveness and reliability in remote driving scenarios.

Accurate traffic congestion prediction is essential for optimizing urban traffic management and mitigating congestion and its consequences. However, conventional prediction models often struggle to simultaneously capture long-term periodic patterns and short-term fluctuations, leading to low prediction accuracy and computational inefficiencies. To overcome this limitation, we propose a knowledge distillation-based framework for enhanced long and short term road traffic prediction. The framework employs a teacher-student architecture, where the teacher model utilizes long-term historical data and a dynamic adjacency matrix to extract periodic traffic patterns, while the student model captures short-term variations and integrates distilled long-term knowledge to enhance responsiveness to sudden congestion changes. To resolve the dimensional mismatch between long-term and short-term feature representations, we introduce a feature alignment mechanism that reduces the dimensionality of high-dimensional intermediate outputs from the teacher model. Experimental evaluations demonstrate that our approach significantly outperforms baseline models, such as Graph Convolutional Gated Recurrent Units, Spatio- Temporal Graph Convolutional Networks and Long Short-Term Memory, in terms of Mean Squared Error, Mean Absolute Error, and Root Mean Squared Error. Moreover, the proposed framework maintains high prediction accuracy even in scenarios with severe traffic fluctuations, offering an efficient and robust solution for traffic congestion forecasting.

Autonomous driving technology has achieved remarkable advancements, offering substantial potential to revolutionize traffic safety and smart mobility. However, when faced with rare scenarios (weather, accident scenes, and lighting), autonomous driving systems can still only play a limited role due to insufficient learning in these rare situations. To address this challenge, we propose a novel approach that leverages low-rank adaptation (LoRA) and Mixture of Experts (MoEs) technologies to enhance the performance of pretrained autonomous driving models in handling rare situations. Specifically, we first use LoRA to fine tune the pretrained model of autonomous driving to focus on capturing knowledge related to rare scenarios and enhance the model’s ability to handle rare situations. Furthermore, we introduce MoEs and propose local, global, and hybrid adaptive solutions to overcome LoRA’s fixed intrinsic rank limitation. These approaches enable adaptive adjustment of LoRA’s rank, and improve the model’s performance from both local and global perspectives. Finally, we design detailed algorithms for different adaptation schemes. Extensive experiments demonstrate that our proposed solutions not only effectively improve the performance of the autonomous driving perception model in rare scenarios but also maintain lower inference latency compared to baseline methods.

In the rapidly evolving landscape of federated learning (FL), hierarchical architectures are pivotal for improving computational efficiency and safeguarding data privacy. A key challenge in this research area is the optimal selection of edge servers, crucial for executing distributed learning tasks across multiple clients and servers efficiently. Traditional selection methods falter due to their inability to dynamically handle the uncertainties in network conditions and server capabilities. To addressing this weakness, we propose a fuzzy logic-based approach that optimizes edge server selection in a novel smart way, thus enhancing resource allocation by efficiently handling the unpredictable nature of network environments and servers performance. This method is integrated with a previously developed scheme for selecting an optimal subset of clients, thereby establishing a comprehensive framework that significantly boosts the performance and reliability of FL networks. The performance of our approach is validated through real-world experiments and the results demonstrate its superiority over existing methods in terms of accuracy and processing time.

The Internet of Vehicles (IoV) enhances smart city functionalities by interconnecting diverse components, yet it introduces significant challenges in terms of user privacy, communication efficiency, and energy consumption. Traditional federated learning frameworks, while adept at addressing these concerns, fall short in personalization due to heterogeneous data distributions among clients. To overcome this, we introduce Hier-FedMeta, a novel framework that combines hierarchical federated learning with meta-learning to provide tailored and efficient solutions. Our comparative analyses with four estab-lished methods show Hier-FedMeta's superior generalization capabilities and adaptability, achieving enhanced performance with minimal computational overhead after just one update step. Furthermore, our in-depth analysis of aggregation parameters offers valuable insights for the optimization of hierarchical federated meta-learning architectures, representing a significant step forward in personalized learning for IoV in smart cities.

Remote driving, an emergent technology enabling remote operation of vehicles, presents a significant challenge due to the necessity of transmitting substantial volumes of image data from the vehicle to a central server. This requirement outpaces the capacity of traditional communication methods, emphasizing the need for efficient data communication. We propose a framework using semantic communication, specifically through a semantic segmentation-based method, which reduces the communication cost by transmitting meaningful semantic information rather than bit-wise data. Addressing the challenge of inconsistencies across knowledge bases in semantic communication, we present a blockchain-based, edge-assisted knowledge base management system. This system leverages edge nodes to manage multiple, geographically and contextually diverse knowledge bases while ensuring security through blockchain's tamper-resistant nature. Furthermore, blockchain sharding is employed to manage different knowledge bases for varying tasks, thereby enhancing the blockchain's throughput. Experimental results showed a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

Semantic communication, a promising candidate for 6G technology, has become a research hot spot. However, existing studies tend to focus more on image reconstruction rather than accurately transmitting semantic information at the pixel level. This paper introduces a novel approach using codec-based Masked AutoEncoders (MAE) for efficient image transmission. The proposed system compresses local information into low-dimensional latent vectors, improving system efficiency. We also design a selective module for enhanced image reconstruction and implement Noise Adversarial Training (NAT) to increase the system’s resilience to channel noise. Experimental results show that our method effectively improves downstream tasks while preserving image quality.

Internet of Things enables devices to communicate, collect and exchange data with the network. As the number of IoT devices keeps growing, the volume of data they produce is also increasing exponentially. Given the feature of limited computing and storage resources of IoT, it is inevitable to store data in the cloud for better services. However, for users to effectively and efficiently inspect those data over the cloud is a critical and open problem. Most public integrity auditing over the cloud schemes requires the user to do a sheer amount of preprocessing work on the local devices, which is unsuitable for IoT devices. With the development of edge computing extending cloud computing, it can provide computing capability for resource-constrained devices in close geographic proximity. In this paper, we design an auditing scheme based on secure computation outsourcing assisted by edge computing, in which the data preprocessing work can be offloaded to the edge server. The experiments show that it reduces the computing load on the devices and improves the efficiency of task processing.

The applications of Internet of Things have emerged in every aspect of people’s life. The volume of data gathered can be enormous. Enterprises and personal consumers are increasingly reliant on cloud storage services instead of local storage. While they enjoy the convenience of cloud storage services, they also worry about the integrity of the cloud-stored data since they do not physically own the data. To enable public integrity auditing, third-party auditors as trusted ones verify data integrity on behalf of the data owner. However, the vulnerability of auditors should also be considered. We propose a consortium blockchain-based public integrity verification system (CBPIV). In CBPIV, the auditor behaviors are recorded in the consortium blockchain so that authorized parties can audit the auditor to see if the verification results are correct. A smart contract is deployed to check the behavior of the auditor automatically, which can trigger alerts for unusual behaviors. The evaluation on both security and performance shows that our proposed scheme is secure and alleviates the burden on data owners of limited computation capability.

Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. However, TPAs may also be untrusted for forging the auditing results or colluding with cloud storage servers to deceive users. In this paper, we propose a novel Blockchain-based Public Auditing Outsourcing system without TPAs (BPAO), in which the computationally expensive operations in public auditing are outsourced through blockchain to the cloud servers without risking users' privacy. Our security analysis indicates that BPAO achieves soundness and robustness. The experimental results show that BPAO is computationally efficient for cloud storage user.

Summary Cloud computing has been an essential technology for providing on‐demand computing resources as a service on the Internet. Not only enterprises but also individuals can outsource their data to the cloud without worrying about purchase and maintenance cost. The cloud storage system, however, is not fully trustable. Cloud data integrity auditing is crucial for defending against the security threats of data in the untrusted multicloud environment. Storing multiple replicas is a commonly used strategy for the availability and reliability of critical data. In this paper, we summarize and analyze the state‐of‐the‐art multiple‐replica integrity auditing schemes in cloud data storage. We present the system model and security threats of outsourcing data to the cloud with classification of ongoing developments. We also summarize the existing data integrity auditing schemes for multicloud data storage. The important open issues and potential research directions are addressed.