In view of the node security risks and key management vulnerabilities in heterogeneous sensor networks, a key management protocol for heterogeneous sensor networks based on zero trust security and chaotic neural networks (KMPHSN-ZTSCNN) was proposed. Taking advantages of the decomposition difficulty of singular matrix and chaotic classification characteristics of Hopfield overload chaotic neural network, the node registration and authentication of sensor network were achieved by blockchain and zero-knowledge proof. The channel state information (CSI) and the adjustable mathematical function were relied on to generate a dynamically changing key to complete continuous verification and achieve zero trust security authentications, thus ensuring data security. The protocol can dynamically allocate different keyspace sizes according to the security level of the group, the storage capacity if the nodeand computing capacity and can adapt to the asymmetric structure of heterogeneous sensor networks. Theoretical proof and experimental performance analysis results show that the protocol is feasible and can meet the security requirements of heterogeneous sensor networks.

Deep learning is widely used in remote sensing field of feature recognition.Symmetric encoder-decoder network, such as UNet, is one of the most commonly used image segmentation networks, but the accuracy is often low due to its simple structure. We combine two neural network models of convolutional neural network (CNN) and Swin Transformer called modified Swin Transformer using UNet structure (MST-UNet) to achieve accurate segmentation of water bodies from remote sensing data, with Xiamen City as study area. MST-UNet is based on symmetric encoder-decoder network. We use CNN and Swin Transformer blocks to extract features from input images and capture the interdependence among different pixels, respectively. More attention is paid to global information of images. By four times upsampling to obtain predictions, the results show that the accuracy of MST-UNet is better than UNet and its improved models. The Intersection of Union (IoU), mean IoU, and Dice score on test set reach 87.80%, 92.93%, 93.08%, respectively, which verifies the feasibility of the MST-UNet. This experiment has a reference value for related studies.

Aiming at the node security risks and key management vulnerabilities in heterogeneous sensor networks, a key management protocol for heterogeneous sensor networks based on zero-trust security and chaotic neural networks (KMPHSN-ZTSCNN) was proposed. Based on the singular matrix decomposition of difficulty and Hopfield overload chaos neural network classification features, using blockchain and zero-knowledge proof to realize sensor network node registration and authentication, it relies on channel state information (CSI) and adjustable mathematical function to generate dynamically changing keys to complete continuous verification and achieve zero-trust security authentication to ensure data security. The protocol can dynamically allocate different keyspace sizes according to the security level of the group, node storage capacity and computing capacity, and can adapt to the asymmetric structure of heterogeneous sensor networks. Theoretical proof and experimental performance analysis show that the protocol is feasible and can meet the security requirements of heterogeneous sensor networks.