The Internet of Vehicles (IoV) is rapidly evolving, but it also faces significant security threats, including Denial of Service (DoS) attacks and deceptive behaviors. The construction of an intrusion detection framework for the IoV encounters the following challenges: 1) the pervasive Non-IID (Non-Independent and Identically Distributed) issue within datasets and 2) in existing applications combining Federated Learning (FL) with blockchain, node load issues are often overlooked. To address these challenges, we propose a novel scheme that integrates Federated Distillation (FD) with blockchain technology to safeguard the IoV from network attacks. First, the scheme combines Convolutional Neural Networks (CNN) and FD for intrusion detection, which both preserves the privacy of vehicle users’ data and effectively shares knowledge. Next, for each FD client, we construct a selector based on density ratio estimation to filter out accurate and reliable knowledge from local predictions, thereby effectively mitigating the performance degradation caused by the Non-IID data problem. Then, the DQN (Deep Q-Network) integrated PBFT algorithm (DQN-PBFT) is proposed to reduce the number of consensus nodes using the DQN algorithm, enhancing load balancing and fairness during the FD and block generation processes. Finally, experimental results show that, in a Non-IID environment, the proposed scheme outperforms the baseline scheme by approximately 30% in accuracy. Compared to other consensus algorithms, DQN-PBFT demonstrates greater advantages in FD environments, exhibiting higher consensus efficiency.

In the Internet of Vehicles (IoV), the large number of computation tasks generated by intelligent vehicles can usually be decomposed into multiple subtasks with timing and data dependencies. Due to the limited computation resources of the vehicle itself, effectively handling these subtasks becomes a significant challenge. In this paper, task processing delay is modelled by using a directed acyclic graph (DAG) to represent the dependencies between subtasks and the longest path as the total delay of the task. Secondly, this paper introduces the concept of latest start time (LST) to measure the urgency of subtasks. Based on this, we design a LST-based computation resource allocation (LST-CRA) algorithm, which allows vehicular edge computing (VEC) server to allocate computation resources efficiently according to the two key characteristics of subtasks, i.e., time constraint and data volume. Furthermore, in order to optimize the offloading process of subtasks, we model the problem as a multi-agent collaborative optimization decision-making problem. By employing the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we propose the LST-CRA driven MADDPG-based computation offloading (LDMCO) algorithm to meet the optimization requirements of subtask offloading. Experimental results show that the LDMCO algorithm and the LST-CRA algorithm demonstrate significant advantages in terms of task processing delay and task completion ratio. Even under high loads or strict time constraints, these algorithms demonstrate strong robustness and balanced system performance.

The application of Internet of Vehicles (IoV) technology has greatly improved users’ driving experience, but it also faces some challenges: 1) the central server is not powerful enough to support the rapid growth of IoV identity authentication requests and 2) there is a privacy leakage issue during vehicle authentication. To address these issues, we propose an anonymous authentication scheme based on trustworthy roadside unit group (TRUG)-PBFT main secondary chains and zero-knowledge proof (ZKP). First, to enhance authentication efficiency, we propose the TRUG-PBFT consensus algorithm. It improves the traditional PBFT by optimizing the PBFT consensus process, reducing the number of consensus nodes using fractional grouping, and selecting main node using verifiable random functions (VRFs). Second, we use a lattice-based ZKP scheme to achieve anonymous authentication of vehicles, and important data in the vehicle authentication process is stored by the main chain maintained by the base station group and the secondary chain maintained by the roadside unit group. Finally, experimental results demonstrate that compared to PBFT consensus, TRUG-PBFT in terms of consensus efficiency is improved by approximately 33%, and the authentication scheme’s computational cost is only 7.08 ms, superior to existing authentication schemes.

In wireless sensor networks (WSNs), enabling nodes to harvest energy from the environment and facilitating energy sharing among nodes through wireless power transfer (WPT) technology, known as energy cooperation, can alleviate energy scarcity issues and effectively prolong the lifespan of WSNs. Although previous research has investigated various forms of energy cooperation, recent developments have underscored the potential of Multicast Energy Cooperation (M-EC) in supporting efficient multinode energy sharing. This approach leverages the broadcast nature of wireless signals, potentially offering greater efficiency compared to traditional point-to-point style Unicast Energy Cooperation (U-EC). In this article, We focus on the M-EC Assisted Data Collection paradigm for energy harvesting-WSNs (EH-WSNs) and investigate the underlying M-EC assisted data collection scheduling (MECADCS) problem, aiming to minimize the data collection completion time by jointly optimizing the schedule decisions for energy cooperation and data collection. We formulate the MECADCS problem as a mixed integer nonlinear programming (MINLP) problem and establish its NP-hardness. We also simplified the MECADCS problem into a mixed integer linear programming (MILP) formulation via piecewise linear approximation, yet solving it using existing mature MILP solvers is still computationally expensive. To promptly return good solutions, we propose an efficient greedy-based data transmission scheduling algorithm (GDTS),heuristically determines energy cooperation and data transmission schedules and achieves a computational speedup of$10^{4}$times compared to exact solvers. Simulation results demonstrate that GDTS significantly reduces the data collection completion time compared to both algorithms without energy cooperation and those utilizing U-EC.

Recently, vehicular ad hoc networks (VANETs) have garnered significant attention in the industry, thanks to their distinctive characteristics of mobility and real-time capabilities. In order to protect the privacy of data sharing in VANETs, a privacy computing scheme based on homomorphic encryption and blockchain has been proposed. First, the Paillier algorithm of homomorphic encryption is used to encrypt the data, making it available but not invisible to ensure the security of the data. Second, the data structure of a directed acyclic graph (DAG) is applied to replace the chain structure so that it can be processed in parallel and reduce the delay time of transmission confirmation. Finally, a single point cluster is set up in the Bigchain database, and Docker virtual technology is used to simulate the high dynamic and high load environment of VANETs, so as to verify the computational efficiency and security of the proposed scheme. The experimental results show that the privacy computing scheme combining homomorphic encryption and DAG blockchain not only eliminates the need for repeated encryption and decryption in the communication process but also reduces the computational cost associated with private data. Compared with traditional chain blockchain, this scheme achieves a comprehensive delay reduction of 68.51% and shortens the average confirmation time by 85.73%, effectively meeting the high-performance requirements of VANETs.

The inefficiency of Consensus protocols is a significant impediment to blockchain and IoT convergence development. To solve the problems like inefficiency and poor dynamics of the Practical Byzantine Fault Tolerance (PBFT) in IoT scenarios, a hierarchical consensus protocol called DCBFT is proposed. Above all, we propose an improved k-sums algorithm to build a two-level consensus cluster, achieving an hierarchical management for IoT devices. Next, A scalable two-level consensus protocol is proposed, which uses a multi-primary node mechanism to solve the single-point-of-failure problem. In addition, a data synchronization process is introduced to ensure the consistency of block data after view changes. Finally, A dynamic reputation evaluation model is introduced to update the nodes’ reputation values and complete the rotation of consensus nodes at the end of each consensus round. The experimental results show that DCBFT has a more robust dynamic and higher consensus efficiency. Moreover, After running for some time, the performance of DCBFT shows some improvement.

The development of new energy vehicles is still in its nascent stages, and the scarcity of computing resources in new energy transactions has become a pressing concern. In order to address this challenge, we propose a Vehicle-to-Vehicle (V2V) energy trading scheme based on Collaborative Edge Computing (CEC) and Lightning Network (LN) technology. Firstly, collaborative vehicles are employed to perform edge computing in a more reasonable manner, effectively mitigating the task resource overload problem of fixed edge computing servers. Secondly, reliable and efficient transaction processing is achieved through the use of blockchain LN technology in transaction communication between vehicles. Thirdly, EdgeCloudSim is used to simulate the workload, and PCNsim is used to implement the LN energy trading scheme. The experimental results demonstrate that the edge computing scheme with collaborative mobile devices can reduce the task failure rate by 80%. Furthermore, it can also reduce network latency and significantly enhance the overall performance of the system in high contention and high workload environments.

The Internet of Things (IoT) has the characteristics of limited resources and wide range of points. Aiming at the problems of policy centralization and single point of failure in traditional access control schemes, a distributed access control method based on adaptive trust evaluation and smart contract is proposed to provide fine-grained, flexible and scalable authorization for IoT devices with limited resources. Firstly, a modular access control architecture with integrated blockchain is proposed to achieve hierarchical management of IoT devices. Secondly, an IoT trust evaluation model called AITTE based on adaptive fusion weights is designed to effectively improve the identification of illegal access requests from malicious nodes. Finally, an attribute-based access control model using smart contract called AACSC which is built, which consists of attribute set contract (ASC), registration contract (RC), state judgment contract (SJC), authority permission management contract (AMC), and access control contract (ACC). As experimental results show, the scheme can effectively solve the problem of access security in resource-constrained IoT environments. Moreover, it also ensures the reliability and efficiency of the access control implementation process.

Because wireless sensor networks (WSNs) are complex and difficult to deploy and manage, appropriate structures are required to make these networks more flexible. In this paper, a reconfigurable testbed is presented, which supports dynamic protocol switching by creating a novel architecture and experiments with several different protocols. The separation of the control and data planes in this testbed means that routing configuration and data transmission are independent. A programmable flow table provides the testbed with the ability to switch protocols dynamically. We experiment on various aspects of the testbed to analyze its functionality and performance. The results demonstrate that sensors in the testbed are easy to manage and can support multiple protocols. We then raise some important issues that should be investigated in future work concerning the testbed.