With ever increasing people's awareness of low carbon and environmental protection, electric vehicles are gradually gaining wide popularity. However, the driving endurance of the electric vehicle is the biggest shortage that hinders the fully acceptance of this new vehicle technology. To deal with this shortage, this paper proposed a vehicle-to-vehicle (V2V) electricity trading scheme based on Bayesian game pricing in blockchain-enabled Internet of vehicles (BIoV). Specifically, the Bayesian game is adopted for pricing in the distributed BIoV with incomplete information sharing. The optimal pricing under the linear strategic equilibrium has been obtained which maximizes the utilities of both sides of electricity transaction. The transaction volume is determined from the formulated convex problem that maximizes the social welfare. Then, the pricing game is implemented by the dedicated smart contract. Blockchain guarantees its trustworthiness, security, and reliability. Finally, the experimental results show that referring to the benchmark of static game with complete information, the proposed Bayesian game with incomplete information can achieve approximate satisfaction of users. The degree of approximation can reach to 98% when the pricing ranges of buyers and sellers are close. Moreover, the proposed scheme has great advantages over the static game with complete information in terms of communication overhead and timeliness in the decentralized IoVs.

Federated learning is an emerging paradigm to conduct the machine learning collaboratively but avoid the leakage of original data. Then, how to motivate the data owners to participate federated learning and contribute high-quality data is the crucial issue. In this article, a blockchain-based federated learning (BFL) with a reputation mechanism for high-quality model aggregation is proposed. Specifically, the blockchain transforms the federated learning into a decentralized and trustworthy manner. Over the blockchain, federated learning tasks, undertaken by smart contracts, can be conducted transparently and fairly. Besides, a reputation-constrained data contribution and reward allocation mechanism is designed to encourage data owners to participate in BFL and contribute high-quality data. The noncooperative game is adopted to analyze the behavior strategies of data owners. The existence of the unique equilibrium is proved and the equilibrium point indicates that the data owners can acquire highest reward with the contribution of the highest quality data. Thus, the model quality of BFL is guaranteed. Finally, simulations on the public data sets (MNIST and CIFAR10) demonstrate that BFL with a reputation mechanism can well promote the high-quality model aggregation of federated learning as well as can prevent malicious nodes from corrupting the training task.

Blockchain technology, has the characteristics of decentralization, openness and transparency, so that everyone can participate in database recording. Therefore, blockchain technology has a good application prospect in various industries. As the most successful application of blockchain technology, the Bitcoin system applies the Proof of Work (PoW) consensus mechanism. Under the PoW consensus mechanism, each miner competes through his own power to solve a SHA256 mathematical problem together, so as to gain profits. Due to the difficulty of the cryptography puzzle, miners tend to join the mining pool to obtain stable income. And the block withholding attacks will be carried out between the mining pools, so as to maximize his own income by controlling the infiltration rate dispatched to other mining pools. In this paper, we build a game model between mining pools based on the PoW consensus algorithm, and analyze its Nash equilibrium from two perspectives. The influence of the mining pools’ power, the ratio of the power to be infiltrated, and the betrayed rate of dispatched miners on the mining pool’s infiltration rate selection and income were explored, and the results were obtained through numerical simulations.

Industrial wireless sensor networks (IWSNs) are committed to bring the industry automation into the era of Industry 4.0 by providing the ubiquitous perception to improve the production efficiency. However, the proliferation of wireless devices in industrial applications makes the spectrum sharing in limited industrial, scientific, and medical (ISM) band a challenging problem. In this paper, it is concerned with the intrinsic impact of the evenness of spectrum usage on the spectrum sharing performance in terms of channel accessing probability, spectrum utilization, and fairness of spectrum usage. In order to explore the explicit relationship between the evenness and spectrum sharing performance, a new concept of equilibrium is first defined to represent the achievable best evenness of spectrum usage. Then, a set of rules called local equilibrium-guided autonomous channel switching (LEQ-AutoCS) is devised, with which each accessed sensor autonomously equalizes the local channel occupations within its range of spectrum sensing without overhead on exchanging the sensors' spectrum sensing reports. It is further proved that the equilibrium can be achieved by this concessive manner. Theoretical analysis and experimental results demonstrate that the proposed LEQ-AutoCS rules provide higher utilization and fairness of spectrum usage comparing to the existing spectrum access approaches. Moreover, it is shown that LEQ-AutoCS rules assist the system to reduce the spectrum access delay to 1/2 of CSMA-based systems and 1/50 of TDMA-based systems, respectively.

Industrial wireless sensor networks (IWSNs) are the fundamental component in the next-generation factories. Due to massive heterogeneous data generated from large-scale IWSNs, it is still challenging to achieve predictable, deterministic, and real-time transmission scheduling. In this paper, a framework of priority-aware packet transmission scheduling (PPTS) in cluster-based IWSNs is proposed, where the PPTS strategy, the optimization theory, and the implementation design are systematically considered. In particular, the proposed PPTS strategy not only minimizes the transmission delay of high priority packets but also greatly improves the transmission delay of low priority packets. The optimization theory for end-to-end priority-aware scheduling in cluster-based IWSNs is formalized, which contributes to the optimal solution for multidimensional network resources allocation and achieves the minimum of average transmission delay. Finally, the advantages of the PPTS framework over some existing solutions are demonstrated by a case study.