Due to insufficient scalability, the existing consortium chain cannot meet the requirements of low latency, high throughput, and high security when applied to Internet of Vehicles (IoV) data sharing. Therefore, we propose a two-layer consensus algorithm based on the master-slave consortium chain - Weighted Raft and Byzantine Fault Tolerance (WRBFT). The intra-group consensus of the WRBFT algorithm adopts weighted Raft, and the best node is selected as the master node to lead the intra-group consensus by comprehensively evaluating the signal-to-noise ratio (SNR), data processing capacity and storage capacity of the nodes. The inter-group consensus adopts practical Byzantine fault tolerance (PBFT) based on BLS aggregate signature with nonlinear coefficients to ensure that the inter-group consensus can tolerate 1/3 of Byzantine nodes. At the same time, the verifiable random function (VRF) is used to select the master node of the inter-group consensus to ensure the randomness of the master node. A large number of experimental results show that the proposed WRBFT algorithm reduces delay, and improves throughput and system security.

Blockchain technology provides a reliable information access environment for the Internet of Vehicles, but the high latency and complex computing consensus mechanism in blockchain make it difficult to port to onboard devices. Recently, there are many methods to reduce the time cost of consensus by optimizing node grouping or reducing redundant calculations, but this would lower the security level of the blockchain. To address these issues and reduce the adverse effects of frequently changing channel quality on consensus results, a consensus mechanism based on vehicle comprehensive state factors for nodes selection (PoMS) is proposed. Firstly, the vehicle nodes utilize the machine learning model to predict local driving parameters and broadcast the predicted results to the other nodes. Secondly, each node uses interactive data to calculate the state values, and the leader comprehensively evaluates the nodes participating in the consensus and selects the nodes as relays. Finally, we also adopted a double-layer blockchain structure to accelerate the selection process of relay nodes. In order to verify the performance of the proposed consensus algorithm, we conducted tests on transmission time and communication quality. The experimental results show that compared to traditional consensus mechanisms, the algorithm proposed in this paper can reduce time overhead by an average of 12.7% and maintain a good transmission rates under a certain number of malicious nodes.

Parked-assisted vehicular edge computing (PVEC) fully leverages communication and computing resources of parking vehicles, thereby significantly alleviating the pressure on edge servers. However, resource sharing and trading for vehicular task offloading in the PVEC environment usually occur between untrustworthy entities, which compromises the security of data sharing and transactions by vehicles and edge devices. To address these concerns, blockchain is introduced to provide a secure and trustworthy environment for offloading and transactions in PVEC. Nevertheless, due to the mobility of the vehicles, the processes of computing offloading and blockchain transactions are interrupted, which greatly reduces the reliability of the blockchain in edge computing process. In this paper, we propose a blockchain-based PVEC (BPVEC) offloading framework to enhance the security and reliability of the task offloading and transaction. Specifically, a consensus node selection algorithm based on the connected dominating set (CDS) is designed to improve the Hotstuff consensus according to parking duration, computing capability and communication quality, which enhances blockchain reliability in computing offloading and transactions. Meanwhile, a Stackelberg game model, establishing the roadside units (RSUs) and parking vehicles (PVs) as leaders and the requesting vehicles (RVs) as follower, is utilized to optimize the offloading strategy and pricing. Subsequently, a BPVEC offloading strategy algorithm with gradient descent method is designed to maximize system revenue. Simulation results show that the proposed BPVEC offloading scheme is secure and reliable while ensuring maximum benefits.

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.

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.

Due to insufficient scalability, the existing consortium chain cannot meet the requirements of low latency, high throughput, and high security when applied to Internet of Vehicles (IoV) data sharing. Therefore, we propose a two-layer consensus algorithm based on the master-slave consortium chain - Weighted Raft and Byzantine Fault Tolerance (WRBFT). The intra-group consensus of the WRBFT algorithm adopts weighted Raft, and the best node is selected as the master node to lead the intra-group consensus by comprehensively evaluating the signal-to-noise ratio (SNR), data processing capacity and storage capacity of the nodes. The inter-group consensus adopts practical Byzantine fault tolerance (PBFT) based on BLS aggregate signature with nonlinear coefficients to ensure that the inter-group consensus can tolerate 1/3 of Byzantine nodes. At the same time, the verifiable random function (VRF) is used to select the master node of the inter-group consensus to ensure the randomness of the master node. A large number of experimental results show that the proposed WRBFT algorithm reduces delay, and improves throughput and system security.