Utilizing widely distributed communication nodes to achieve environmental reconstruction is one of the significant scenarios for integrated sensing and communication (ISAC) and a crucial technology for 6G. To achieve this crucial functionality, we propose a deep learning-based multinode ISAC 4D environment reconstruction method with the uplink-downlink (UL-DL) cooperation, which employs virtual aperture technology, constant false alarm rate (CFAR) detection, and mutiple signal classification (music) algorithm to maximize the sensing capabilities of single sensing nodes. Simultaneously, it introduces a cooperative environmental reconstruction scheme involving the multinode cooperation and UL-DL cooperation to overcome the limitations of single-node sensing caused by occlusion and limited viewpoints. Furthermore, the deep learning models attention gate gridding residual neural network (AGGRNN) and multiview sensing fusion network (MVSFNet) to enhance the density of the sparsely reconstructed point clouds are proposed, aiming to restore as many original environmental details as possible while preserving the spatial structure of the point cloud. Additionally, we propose a multilevel fusion strategy incorporating both the data-level and feature-level fusion to fully leverage the advantages of the multinode cooperation. Experimental results demonstrate that the environmental reconstruction performance of this method significantly outperforms the other comparative method, enabling high-precision environmental reconstruction using the ISAC system.

The Passive Keyless Entry and Start system (PKES) has become an essential element of vehicle systems since it allows owners to lock or unlock their properties without having to take out the keys. However, the system suffers from a potential and serious security problem because of a relay attack. This paper introduces dynamic biometrics to resolve such potential problems. Precisely, it opts for hand-gesture and proposes a Multi-Task Siamese Feature-pyramid network with Bidirectional Gated Recurrent Units (MTSFBet) against relay attack. MTSFBet is made up of a Dynamic Biometrics Extraction Module (DBEM) and Multi-Task Operation Module (MTOM). DBEM extracts trajectory and kinematic features from the gesture data of owners, whereas MTOM performs gesture classification and identity authentication using these features. Moreover, we designed a loss function for the identity authentication part. Eventually, based on the dataset collected by mobile devices, we constructed comparative experiments and ablation studies to demonstrate the effectiveness of the method. Our comprehensive model achieves an accuracy of 0.86 in gesture classification and an accuracy of 0.73 in identity authentication. The results showed that our MTSFBet significantly outperforms other comparison methods. The MTSFBet can also be used in any scenario associated with identity authentication based on hand gestures.