Data Lake is one of the arguable concepts appeared in the era of big data. Data Lake original idea is originated from business field instead of academic field. As Data Lake is a newly conceived idea with revolutionized concepts, it brings many challenges for its adoption. However, the potential to change the data landscape makes the research of Data Lake worthwhile.

When mobile network enters 5G era, 5G networks have a series of unparalleled advantages. Therefore, the application of 5G network technology in the Internet of Vehicles (IoV) can promote more intelligently vehicular networks and more efficiently vehicular information transmission. However, with the combination of 5G networks and vehicular networks technology, it requires safe and reliable authentication and low computation overhead. Therefore, it is a challenge to achieve such low latency, security, and high mobility. In this paper, we propose a secure and efficient lightweight authentication protocol for vehicle group. The scheme is based on the extended chaotic map to achieve authentication, and the Chinese remainder theorem distributes group keys. Scyther is used to verify the security of the scheme, and the verification results show that the security of the scheme can be guaranteed. In addition, through security analysis, the scheme can not only effectively resist various attacks but also guarantee security requirements such as anonymity and unlinkability. Finally, by performance analysis and comparison, our scheme has less computation and communication overhead.

As environmental issues and climate change worsen, Smart Grid has become a crucial technology for tackling energy challenges. Smart Grid substantially enhances the reliability, security and efficiency of power systems by integrating state-of-the-art communication, control and information technologies. As a crucial component of Smart Grid, Vehicle to Grid (V2G) facilitates bidirectional energy flow between electric vehicle (EV) and the grid. This not only optimizes energy utilization but also accelerates the large-scale adoption of EV, contributing to a more sustainable energy ecosystem. However, security and privacy concerns in V2G cannot be ignored, particularly regarding identity authentication and data protection. Therefore, enabling the secure transmission of users’ private information within V2G is crucial. This paper presents a secure and efficient authentication scheme for privacy preserving in V2G. The scheme validates vehicle user identities and leverages chebyshev chaotic maps along with hash function to enable V2G communication. It ensures both data integrity and user privacy during transmission, addressing key security concerns in V2G. Through formal security analysis, it is confirmed that the scheme can withstand common attacks. Additionally, detailed informal security discussions demonstrate that the scheme can resist known attacks and meet design objectives. Further performance evaluation shows that the proposed scheme balances efficiency and security.

The Deeplabv3+ network for semantic segmentation of remote sensing images has drawbacks like inaccurate edge segmentation and intra-class inconsistency in large-scale segmentation. The attention mechanism is a good solution to this problem. This paper integrates the dual attention mechanism module including spatial attention mechanism and channel attention mechanism (ISNet) into the network. Furthermore, the ISNet is added to both the encoder part and decoder part in the network. The newly proposed network is called DISNet. It can enhance target features and suppress background features, having the potential to improve semantic segmentation accuracy. The experimental results indicate that the Mean Intersection over Union (mIoU) of the Deepglobe dataset, GID dataset, and ISPRS test project (Vaihingen) dataset is respectively 64.22, 62.83, and 78.59%. Those results are 5.68, 4.80, and 6.20% higher than that of Deeplabv3+. The proposed network can effectively improve the accuracy of remote sensing land cover classification.