Traffic flow prediction has become an important component of intelligent transportation systems. However, high-precision traffic flow prediction (especially long-term prediction) is still very challenging due to the complex spatial-temporal dependence of urban traffic data. In this paper, a novel Multi-scale Temporal and Enhance Spatial Transformer (MTESformer) model is proposed to capture complex dynamic spatial-temporal dependencies. MTESformer provides a reasonable feature embedding of periodic characteristics of traffic; it can recognize different temporal feature patterns and capture long-term dependencies, and efficiently focuses on two different node-space dependencies (long-range and neighboring nodes dependencies). Specifically, we develop a special multi-scale convolution unit that unites temporal self-attention to capture a wider range of dynamic temporal dependencies from a multi-receptive field and identify different temporal feature patterns. Secondly, we design a novel Enhance Spatial Transformer module, which can better focus on the dynamic spatial dependencies among nodes by fusing their neighborhood information. Experimental results on the public transportation network datasets METR-LA, PEMS-BAY, PEMS04, and PEMS08 data show that our proposed method outperforms most of the baseline models and outperforms the state-of-the-art models in long-term prediction. (The MAE of 60min prediction of our model on METR-LA, PEMS-BAY dataset is 3.37, 1.87, and the MAPE is 9.62%, 4.35%, respectively, and all of them outperform the PDFormer on PEMS04 and PEMS08 dataset).

The information sharing among internet-connected vehicles is close, but there may be the issue of vehicles providing false information. This paper proposes a new incentive mechanism scheme for VANET to provide a decentralized and trusted service management. We introduce a trust-based message evaluation system, incorporating reputation assessment and negative feedback functions to evaluate the credibility of messages and assessments. A reward and punishment mechanism based on repeated games is designed to incentivize vehicles to proactively share information, thereby preventing false information and defamation attacks. Additionally, a blockchain-based distributed trust management scheme is designed to ensure the consistency of trust management storage through the consensus mechanism of the blockchain, reducing reliance on centralized institutions. Simulation results demonstrate that the establishment of reputation and incentive mechanisms among vehicles enhances the enthusiasm for information sharing and improves the accuracy of identifying false messages.

Enhancing data privacy security in medical data sharing is crucial for the informatization development in the healthcare sector. This paper proposes a healthcare data sharing scheme based on two-dimensional chaotic mapping and blockchain (2DCM-DS). Specifically, a new two-dimensional chaotic mapping is proposed, which demonstrates superior chaotic performance. Then, by incorporating biometric audio information as an identity credential and integrating it with the proposed two-dimensional chaotic mapping, we design a data encryption method that establishes a strongly coupled and bi-directionally verifiable data ownership relationship in healthcare data sharing. Finally, we employ blockchain as the underlying network and design corresponding smart contracts to support 2DCM-DS. This approach addresses potential issues of unauthorized access, malicious tampering, and single points of failure in centralized data sharing. Experimental results demonstrate that 2DCM-DS effectively protects data security under the specified attack models. The results validate the security and efficiency of the 2DCM-DS, proving its application potential in healthcare insurance data sharing scenarios.

With the rapid rise of Internet of Things (IoT) technology, cloud computing and attribute-based encryption (ABE) are often employed to safeguard the privacy and security of IoT data. However, most blockchain based access control methods are one-way, and user access policies are public, which cannot simultaneously meet the needs of dynamic attribute updates, two-way verification of users and data, and secure data transmission. To handle such challenges, we propose an attribute-based encryption scheme that satisfies real-time and secure sharing requirements through attribute updates and policy hiding. First, we designed a new dynamic update and policy hiding bidirectional attribute access control (DUPH-BAAC) scheme. In addition, a strategy hiding technique was adopted. The data owner sends encrypted addresses with hidden access policies to the blockchain network for verification through transactions. Then, the user locally matches attributes, the smart contract verifies user permissions, and generates access transactions for users who meet access policies. Moreover, the cloud server receives user identity keys and matches the user attribute set with the ciphertext attribute set. Besides, blockchain networks replace traditional IoT centralized servers for identity authentication, authorization, key management, and attribute updates, reducing information leakage risk. Finally, we demonstrate that the DUPH-BAAC scheme can resist indistinguishable choice access structures and selective plaintext attacks, achieving IND-sAS-CPA security.

With the rapid development of communication technologies, the demand for security and automation of driving has promoted the development of the Internet of Vehicles (IoV). The IoV aims to provide users with a safer, more comfortable, and more efficient driving experience. However, the current IoV also faces a series of potential security risks and privacy breaches, which has further propelled research on trust management for vehicular networks. The introduction of the blockchain has resolved the issue of data security in IoV trust management. However, the blockchain is limited by its own performance and scalability, making it unsuitable for large-scale networks. In order to enhance the transaction-processing efficiency of blockchain-based trust management solutions and address their scalability limitations, this paper presents a graph partition-based blockchain-sharding protocol. Simulation results on real-world datasets demonstrate that the proposed scheme exhibits better scalability compared to existing blockchain-based approaches and can accommodate larger-scale device access.