With the rapid development of in-vehicle communication technology, the Internet of Vehicles (IoV) is gradually becoming a core component of next-generation transportation networks. However, ensuring the activity and reliability of IoV nodes remains a critical challenge. The emergence of blockchain technology offers new solutions to the problem of node selection in IoV. Nevertheless, traditional blockchain networks may suffer from malicious nodes, which pose security threats and disrupt normal blockchain operations. To address the issues of low participation and security risks among IoV nodes, this paper proposes a federated learning (FL) scheme based on blockchain and reputation value changes. This scheme encourages active involvement in blockchain consensus and facilitates the selection of trustworthy and reliable IoV nodes. First, we avoid conflicts between computing power for training and consensus by constructing state-channel transitions to move training tasks off-chain. Task rewards are then distributed to participating miner nodes based on their contributions to the FL model. Second, a reputation mechanism is designed to measure the reliability of participating nodes in FL, and a Proof of Contribution Consensus (PoCC) algorithm is proposed to allocate node incentives and package blockchain transactions. Finally, experimental results demonstrate that the proposed incentive mechanism enhances node participation in training and successfully identifies trustworthy nodes.

In a harsh environment, function aggregation of air–ground integrated network service function chaining (SFC) deployment can easily cause network load imbalance, which affects the network security and reliability. In this study, a task-similarity-based virtual network function (VNF) aggregation scheme was proposed. It considered air–ground network resource consumption and load balance before SFC mapping. A model for selecting VNFs to be aggregated based on task similarity was built. The tasks were classified based on their similarity. Furthermore, the VNFs to be aggregated were selected within the class under the constraints of the underlying physical resources. Load balancing was achieved by adjusting the similarity threshold. Moreover, an SFC mapping selection scheme based on network resource awareness was used to obtain the most suitable physical nodes for single-chain and multi-chain mapping according to various attributes of physical network nodes. The simulation results indicated that the proposed scheme with a better load balance design outperformed existing works on VNF aggregation. We also demonstrated that the task-similarity-based scheme was resource-consumption efficient and effective.

When Kafka is used in production environments, a large number of parameters are provided to facilitate user configuration for specific application environments in order to obtain better performance. However, configuring Kafka's parameters requires in-depth knowledge of the user, which is far beyond the ability of the average user and prevents Kafka from obtaining better performance. To address this problem, we propose an ACT-SAGAN method that adds a self-attention mechanism to the generative adversarial network model to capture the associations between hidden structures in good configuration combinations and configuration parameters, which uses these hidden structures and associations to generate better configuration combinations to improve Kafka's performance. Experimental results show that the algorithm improves Kafka's throughput and reduces latency after deployment for the configuration combinations generated by Kafka.

Blockchain provides a secure and trusted environment for task offloading in Vehicle Edge Computing (VEC). Nevertheless, single-layer blockchain architecture exhibits low transaction processing efficiency and insufficient scalability in VEC environments. Accordingly, we present a two-layer blockchain-based VEC (TBVEC) task offloading framework, which extends consensus nodes to parked vehicles (PVs) to improve the scalability of edge computing. Specifically, a Hotstuff–BLSPBFT two-layer blockchain is constructed by employing Hotstuff for the lower-layer blockchain and BLSPBFT for the upper-layer blockchain, thereby improving transaction efficiency of the blockchain system. Then, sharding is performed on consensus nodes of the lower-layer blockchain, and an improved K-Means algorithm is adopted for grouping optimization. Furthermore, utility functions of user vehicles and Road Side Units (RSUs) are formulated based on a two-layer blockchain. A game-based Deep Reinforcement Learning (DRL) model, equipped with two Soft Actor-Critic (SAC) agents, is developed to optimize user and RSU utility functions. The model determines the optimal task offloading strategies and transaction pricing policies, while jointly optimizing the number of consensus node groups and the quantity of nodes within each group to maximize the utility function. Experimental results validate that the proposed scheme not only ensures the security of task offloading but also significantly enhances overall system efficiency.

The integration of blockchain into parked vehicle edge computing (PVEC) has emerged as a promising approach to mitigate the inherent trust challenges in distributed and untrusted computing environments. However, during task offloading and consensus, vehicles are vulnerable to location information disclosure, leading to privacy leakage. To address this problem, we propose a location differential privacy-enabled blockchain PVEC (DBPVEC) framework to protect location information during offloading and consensus. Specifically, we design a location differential privacy mechanism based on the Laplace mechanism and theoretically prove that it satisfies ε-differential privacy. This mechanism perturbs vehicles’ locations, and a privacy-preserving offloading strategy is designed to enhance the Hotstuff consensus and protect location privacy in edge computing. Subsequently, we formulate a joint optimization problem, considering system energy consumption, latency, and privacy strength. To solve it, we design a two-layer deep reinforcement learning (DRL) algorithm, with a Deep Q-Network (DQN) as the upper layer and a Deep Deterministic Policy Gradient (DDPG) as the lower layer, to determine the optimal offloading strategy. The experimental results demonstrate that our scheme achieves significant reductions compared to the two baseline methods: the total cost decreases by 68.31% and 63.25%, energy consumption by 9.96% and 16.27%, and delay by 31.46% and 18.07%, respectively. Moreover, it effectively preserves vehicle location privacy during task offloading and consensus while maintaining favorable performance in energy consumption and latency.